diff --git a/articles/CDISC_ADaM.html b/articles/CDISC_ADaM.html
index d8012134..6827063c 100644
--- a/articles/CDISC_ADaM.html
+++ b/articles/CDISC_ADaM.html
@@ -147,23 +147,23 @@ <h2 id="time-to-event-analysis">Time-to-event analysis<a class="anchor" aria-lab
 <span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> Please use `fct_na_value_to_level()` instead.</span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> The deprecated feature was likely used in the <span style="color: #0000BB;">visR</span> package.</span></span>
 <span><span class="co">#&gt;   Please report the issue at <span style="color: #0000BB; font-style: italic;">&lt;https://github.com/openpharma/visR/issues&gt;</span>.</span></span></code></pre></div>
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@@ -673,23 +673,23 @@ <h2 id="time-to-event-analysis">Time-to-event analysis<a class="anchor" aria-lab
 <code class="sourceCode R"><span><span class="co"># Display test statistics associated with the survival estimate</span></span>
 <span></span>
 <span><span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/render.html">render</a></span><span class="op">(</span><span class="va">survfit_object</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/get_pvalue.html">get_pvalue</a></span><span class="op">(</span><span class="op">)</span>, title <span class="op">=</span> <span class="st">"P-values"</span>, datasource <span class="op">=</span> <span class="va">DATASET</span><span class="op">)</span></span></code></pre></div>
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@@ -975,7 +975,7 @@ <h2 id="time-to-event-analysis">Time-to-event analysis<a class="anchor" aria-lab
   padding-right: 5px;
 }
 
-#sxvtrrpdxc .gt_first_grand_summary_row {
+#bzqjzatnih .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -985,7 +985,7 @@ <h2 id="time-to-event-analysis">Time-to-event analysis<a class="anchor" aria-lab
   border-top-color: #D3D3D3;
 }
 
-#sxvtrrpdxc .gt_last_grand_summary_row_top {
+#bzqjzatnih .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -995,11 +995,11 @@ <h2 id="time-to-event-analysis">Time-to-event analysis<a class="anchor" aria-lab
   border-bottom-color: #D3D3D3;
 }
 
-#sxvtrrpdxc .gt_striped {
+#bzqjzatnih .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#sxvtrrpdxc .gt_table_body {
+#bzqjzatnih .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1008,7 +1008,7 @@ <h2 id="time-to-event-analysis">Time-to-event analysis<a class="anchor" aria-lab
   border-bottom-color: #D3D3D3;
 }
 
-#sxvtrrpdxc .gt_footnotes {
+#bzqjzatnih .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1022,7 +1022,7 @@ <h2 id="time-to-event-analysis">Time-to-event analysis<a class="anchor" aria-lab
   border-right-color: #D3D3D3;
 }
 
-#sxvtrrpdxc .gt_footnote {
+#bzqjzatnih .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -1031,7 +1031,7 @@ <h2 id="time-to-event-analysis">Time-to-event analysis<a class="anchor" aria-lab
   padding-right: 5px;
 }
 
-#sxvtrrpdxc .gt_sourcenotes {
+#bzqjzatnih .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1045,7 +1045,7 @@ <h2 id="time-to-event-analysis">Time-to-event analysis<a class="anchor" aria-lab
   border-right-color: #D3D3D3;
 }
 
-#sxvtrrpdxc .gt_sourcenote {
+#bzqjzatnih .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -1053,63 +1053,63 @@ <h2 id="time-to-event-analysis">Time-to-event analysis<a class="anchor" aria-lab
   padding-right: 5px;
 }
 
-#sxvtrrpdxc .gt_left {
+#bzqjzatnih .gt_left {
   text-align: left;
 }
 
-#sxvtrrpdxc .gt_center {
+#bzqjzatnih .gt_center {
   text-align: center;
 }
 
-#sxvtrrpdxc .gt_right {
+#bzqjzatnih .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#sxvtrrpdxc .gt_font_normal {
+#bzqjzatnih .gt_font_normal {
   font-weight: normal;
 }
 
-#sxvtrrpdxc .gt_font_bold {
+#bzqjzatnih .gt_font_bold {
   font-weight: bold;
 }
 
-#sxvtrrpdxc .gt_font_italic {
+#bzqjzatnih .gt_font_italic {
   font-style: italic;
 }
 
-#sxvtrrpdxc .gt_super {
+#bzqjzatnih .gt_super {
   font-size: 65%;
 }
 
-#sxvtrrpdxc .gt_footnote_marks {
+#bzqjzatnih .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#sxvtrrpdxc .gt_asterisk {
+#bzqjzatnih .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#sxvtrrpdxc .gt_indent_1 {
+#bzqjzatnih .gt_indent_1 {
   text-indent: 5px;
 }
 
-#sxvtrrpdxc .gt_indent_2 {
+#bzqjzatnih .gt_indent_2 {
   text-indent: 10px;
 }
 
-#sxvtrrpdxc .gt_indent_3 {
+#bzqjzatnih .gt_indent_3 {
   text-indent: 15px;
 }
 
-#sxvtrrpdxc .gt_indent_4 {
+#bzqjzatnih .gt_indent_4 {
   text-indent: 20px;
 }
 
-#sxvtrrpdxc .gt_indent_5 {
+#bzqjzatnih .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
diff --git a/articles/Time_to_event_analysis.html b/articles/Time_to_event_analysis.html
index d19c5bf1..58f1fbe6 100644
--- a/articles/Time_to_event_analysis.html
+++ b/articles/Time_to_event_analysis.html
@@ -178,23 +178,23 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
 <span></span>
 <span><span class="co"># Render the tableone</span></span>
 <span><span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/render.html">render</a></span><span class="op">(</span><span class="va">tab1</span>, title <span class="op">=</span> <span class="st">"Overview over Lung Cancer patients"</span>, datasource <span class="op">=</span> <span class="va">DATASET</span><span class="op">)</span></span></code></pre></div>
-<div id="znkvyuwypl" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#znkvyuwypl table {
+<div id="xwgnuhpzwd" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#xwgnuhpzwd table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#znkvyuwypl thead, #znkvyuwypl tbody, #znkvyuwypl tfoot, #znkvyuwypl tr, #znkvyuwypl td, #znkvyuwypl th {
+#xwgnuhpzwd thead, #xwgnuhpzwd tbody, #xwgnuhpzwd tfoot, #xwgnuhpzwd tr, #xwgnuhpzwd td, #xwgnuhpzwd th {
   border-style: none;
 }
 
-#znkvyuwypl p {
+#xwgnuhpzwd p {
   margin: 0;
   padding: 0;
 }
 
-#znkvyuwypl .gt_table {
+#xwgnuhpzwd .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -220,12 +220,12 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-left-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_caption {
+#xwgnuhpzwd .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#znkvyuwypl .gt_title {
+#xwgnuhpzwd .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -237,7 +237,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-width: 0;
 }
 
-#znkvyuwypl .gt_subtitle {
+#xwgnuhpzwd .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -249,7 +249,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-top-width: 0;
 }
 
-#znkvyuwypl .gt_heading {
+#xwgnuhpzwd .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -261,13 +261,13 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_bottom_border {
+#xwgnuhpzwd .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_col_headings {
+#xwgnuhpzwd .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -282,7 +282,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_col_heading {
+#xwgnuhpzwd .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -302,7 +302,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   overflow-x: hidden;
 }
 
-#znkvyuwypl .gt_column_spanner_outer {
+#xwgnuhpzwd .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -314,15 +314,15 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 4px;
 }
 
-#znkvyuwypl .gt_column_spanner_outer:first-child {
+#xwgnuhpzwd .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#znkvyuwypl .gt_column_spanner_outer:last-child {
+#xwgnuhpzwd .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#znkvyuwypl .gt_column_spanner {
+#xwgnuhpzwd .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -334,11 +334,11 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   width: 100%;
 }
 
-#znkvyuwypl .gt_spanner_row {
+#xwgnuhpzwd .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#znkvyuwypl .gt_group_heading {
+#xwgnuhpzwd .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -364,7 +364,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   text-align: left;
 }
 
-#znkvyuwypl .gt_empty_group_heading {
+#xwgnuhpzwd .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -379,15 +379,15 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   vertical-align: middle;
 }
 
-#znkvyuwypl .gt_from_md > :first-child {
+#xwgnuhpzwd .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#znkvyuwypl .gt_from_md > :last-child {
+#xwgnuhpzwd .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#znkvyuwypl .gt_row {
+#xwgnuhpzwd .gt_row {
   padding-top: 4px;
   padding-bottom: 4px;
   padding-left: 5px;
@@ -406,7 +406,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   overflow-x: hidden;
 }
 
-#znkvyuwypl .gt_stub {
+#xwgnuhpzwd .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -419,7 +419,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#znkvyuwypl .gt_stub_row_group {
+#xwgnuhpzwd .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -433,15 +433,15 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   vertical-align: top;
 }
 
-#znkvyuwypl .gt_row_group_first td {
+#xwgnuhpzwd .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#znkvyuwypl .gt_row_group_first th {
+#xwgnuhpzwd .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#znkvyuwypl .gt_summary_row {
+#xwgnuhpzwd .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -451,16 +451,16 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#znkvyuwypl .gt_first_summary_row {
+#xwgnuhpzwd .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_first_summary_row.thick {
+#xwgnuhpzwd .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#znkvyuwypl .gt_last_summary_row {
+#xwgnuhpzwd .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -470,7 +470,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_grand_summary_row {
+#xwgnuhpzwd .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -480,7 +480,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#znkvyuwypl .gt_first_grand_summary_row {
+#xwgnuhpzwd .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -490,7 +490,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-top-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_last_grand_summary_row_top {
+#xwgnuhpzwd .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -500,11 +500,11 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_striped {
+#xwgnuhpzwd .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#znkvyuwypl .gt_table_body {
+#xwgnuhpzwd .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -513,7 +513,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_footnotes {
+#xwgnuhpzwd .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -527,7 +527,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_footnote {
+#xwgnuhpzwd .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -536,7 +536,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#znkvyuwypl .gt_sourcenotes {
+#xwgnuhpzwd .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -550,7 +550,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#znkvyuwypl .gt_sourcenote {
+#xwgnuhpzwd .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -558,63 +558,63 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#znkvyuwypl .gt_left {
+#xwgnuhpzwd .gt_left {
   text-align: left;
 }
 
-#znkvyuwypl .gt_center {
+#xwgnuhpzwd .gt_center {
   text-align: center;
 }
 
-#znkvyuwypl .gt_right {
+#xwgnuhpzwd .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#znkvyuwypl .gt_font_normal {
+#xwgnuhpzwd .gt_font_normal {
   font-weight: normal;
 }
 
-#znkvyuwypl .gt_font_bold {
+#xwgnuhpzwd .gt_font_bold {
   font-weight: bold;
 }
 
-#znkvyuwypl .gt_font_italic {
+#xwgnuhpzwd .gt_font_italic {
   font-style: italic;
 }
 
-#znkvyuwypl .gt_super {
+#xwgnuhpzwd .gt_super {
   font-size: 65%;
 }
 
-#znkvyuwypl .gt_footnote_marks {
+#xwgnuhpzwd .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#znkvyuwypl .gt_asterisk {
+#xwgnuhpzwd .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#znkvyuwypl .gt_indent_1 {
+#xwgnuhpzwd .gt_indent_1 {
   text-indent: 5px;
 }
 
-#znkvyuwypl .gt_indent_2 {
+#xwgnuhpzwd .gt_indent_2 {
   text-indent: 10px;
 }
 
-#znkvyuwypl .gt_indent_3 {
+#xwgnuhpzwd .gt_indent_3 {
   text-indent: 15px;
 }
 
-#znkvyuwypl .gt_indent_4 {
+#xwgnuhpzwd .gt_indent_4 {
   text-indent: 20px;
 }
 
-#znkvyuwypl .gt_indent_5 {
+#xwgnuhpzwd .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
@@ -676,23 +676,23 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Use wrapper functionality to create and display a tableone</span></span>
 <span><span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/tableone.html">tableone</a></span><span class="op">(</span><span class="va">lung_cohort_tab1</span>, title <span class="op">=</span> <span class="st">"Overview over Lung Cancer patients"</span>, datasource <span class="op">=</span> <span class="va">DATASET</span><span class="op">)</span></span></code></pre></div>
-<div id="wrdnoaezxp" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#wrdnoaezxp table {
+<div id="zvgflcsmnd" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#zvgflcsmnd table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#wrdnoaezxp thead, #wrdnoaezxp tbody, #wrdnoaezxp tfoot, #wrdnoaezxp tr, #wrdnoaezxp td, #wrdnoaezxp th {
+#zvgflcsmnd thead, #zvgflcsmnd tbody, #zvgflcsmnd tfoot, #zvgflcsmnd tr, #zvgflcsmnd td, #zvgflcsmnd th {
   border-style: none;
 }
 
-#wrdnoaezxp p {
+#zvgflcsmnd p {
   margin: 0;
   padding: 0;
 }
 
-#wrdnoaezxp .gt_table {
+#zvgflcsmnd .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -718,12 +718,12 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-left-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_caption {
+#zvgflcsmnd .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#wrdnoaezxp .gt_title {
+#zvgflcsmnd .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -735,7 +735,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-width: 0;
 }
 
-#wrdnoaezxp .gt_subtitle {
+#zvgflcsmnd .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -747,7 +747,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-top-width: 0;
 }
 
-#wrdnoaezxp .gt_heading {
+#zvgflcsmnd .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -759,13 +759,13 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_bottom_border {
+#zvgflcsmnd .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_col_headings {
+#zvgflcsmnd .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -780,7 +780,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_col_heading {
+#zvgflcsmnd .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -800,7 +800,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   overflow-x: hidden;
 }
 
-#wrdnoaezxp .gt_column_spanner_outer {
+#zvgflcsmnd .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -812,15 +812,15 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 4px;
 }
 
-#wrdnoaezxp .gt_column_spanner_outer:first-child {
+#zvgflcsmnd .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#wrdnoaezxp .gt_column_spanner_outer:last-child {
+#zvgflcsmnd .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#wrdnoaezxp .gt_column_spanner {
+#zvgflcsmnd .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -832,11 +832,11 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   width: 100%;
 }
 
-#wrdnoaezxp .gt_spanner_row {
+#zvgflcsmnd .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#wrdnoaezxp .gt_group_heading {
+#zvgflcsmnd .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -862,7 +862,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   text-align: left;
 }
 
-#wrdnoaezxp .gt_empty_group_heading {
+#zvgflcsmnd .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -877,15 +877,15 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   vertical-align: middle;
 }
 
-#wrdnoaezxp .gt_from_md > :first-child {
+#zvgflcsmnd .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#wrdnoaezxp .gt_from_md > :last-child {
+#zvgflcsmnd .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#wrdnoaezxp .gt_row {
+#zvgflcsmnd .gt_row {
   padding-top: 4px;
   padding-bottom: 4px;
   padding-left: 5px;
@@ -904,7 +904,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   overflow-x: hidden;
 }
 
-#wrdnoaezxp .gt_stub {
+#zvgflcsmnd .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -917,7 +917,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#wrdnoaezxp .gt_stub_row_group {
+#zvgflcsmnd .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -931,15 +931,15 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   vertical-align: top;
 }
 
-#wrdnoaezxp .gt_row_group_first td {
+#zvgflcsmnd .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#wrdnoaezxp .gt_row_group_first th {
+#zvgflcsmnd .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#wrdnoaezxp .gt_summary_row {
+#zvgflcsmnd .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -949,16 +949,16 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#wrdnoaezxp .gt_first_summary_row {
+#zvgflcsmnd .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_first_summary_row.thick {
+#zvgflcsmnd .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#wrdnoaezxp .gt_last_summary_row {
+#zvgflcsmnd .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -968,7 +968,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_grand_summary_row {
+#zvgflcsmnd .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -978,7 +978,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#wrdnoaezxp .gt_first_grand_summary_row {
+#zvgflcsmnd .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -988,7 +988,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-top-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_last_grand_summary_row_top {
+#zvgflcsmnd .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -998,11 +998,11 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_striped {
+#zvgflcsmnd .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#wrdnoaezxp .gt_table_body {
+#zvgflcsmnd .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1011,7 +1011,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_footnotes {
+#zvgflcsmnd .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1025,7 +1025,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_footnote {
+#zvgflcsmnd .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -1034,7 +1034,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#wrdnoaezxp .gt_sourcenotes {
+#zvgflcsmnd .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1048,7 +1048,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#wrdnoaezxp .gt_sourcenote {
+#zvgflcsmnd .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -1056,63 +1056,63 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#wrdnoaezxp .gt_left {
+#zvgflcsmnd .gt_left {
   text-align: left;
 }
 
-#wrdnoaezxp .gt_center {
+#zvgflcsmnd .gt_center {
   text-align: center;
 }
 
-#wrdnoaezxp .gt_right {
+#zvgflcsmnd .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#wrdnoaezxp .gt_font_normal {
+#zvgflcsmnd .gt_font_normal {
   font-weight: normal;
 }
 
-#wrdnoaezxp .gt_font_bold {
+#zvgflcsmnd .gt_font_bold {
   font-weight: bold;
 }
 
-#wrdnoaezxp .gt_font_italic {
+#zvgflcsmnd .gt_font_italic {
   font-style: italic;
 }
 
-#wrdnoaezxp .gt_super {
+#zvgflcsmnd .gt_super {
   font-size: 65%;
 }
 
-#wrdnoaezxp .gt_footnote_marks {
+#zvgflcsmnd .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#wrdnoaezxp .gt_asterisk {
+#zvgflcsmnd .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#wrdnoaezxp .gt_indent_1 {
+#zvgflcsmnd .gt_indent_1 {
   text-indent: 5px;
 }
 
-#wrdnoaezxp .gt_indent_2 {
+#zvgflcsmnd .gt_indent_2 {
   text-indent: 10px;
 }
 
-#wrdnoaezxp .gt_indent_3 {
+#zvgflcsmnd .gt_indent_3 {
   text-indent: 15px;
 }
 
-#wrdnoaezxp .gt_indent_4 {
+#zvgflcsmnd .gt_indent_4 {
   text-indent: 20px;
 }
 
-#wrdnoaezxp .gt_indent_5 {
+#zvgflcsmnd .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
@@ -1177,23 +1177,23 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
 <code class="sourceCode R"><span><span class="co"># Create and render a tableone with a stratifier and without displaying the total</span></span>
 <span><span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/tableone.html">tableone</a></span><span class="op">(</span><span class="va">lung_cohort_tab1</span>, strata <span class="op">=</span> <span class="st">"Sex"</span>, overall <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>               title <span class="op">=</span> <span class="st">"Overview over Lung Cancer patients"</span>, datasource <span class="op">=</span> <span class="va">DATASET</span><span class="op">)</span></span></code></pre></div>
-<div id="ivbdpjimxj" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#ivbdpjimxj table {
+<div id="ajcojtljmw" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#ajcojtljmw table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
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-#ivbdpjimxj thead, #ivbdpjimxj tbody, #ivbdpjimxj tfoot, #ivbdpjimxj tr, #ivbdpjimxj td, #ivbdpjimxj th {
+#ajcojtljmw thead, #ajcojtljmw tbody, #ajcojtljmw tfoot, #ajcojtljmw tr, #ajcojtljmw td, #ajcojtljmw th {
   border-style: none;
 }
 
-#ivbdpjimxj p {
+#ajcojtljmw p {
   margin: 0;
   padding: 0;
 }
 
-#ivbdpjimxj .gt_table {
+#ajcojtljmw .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -1219,12 +1219,12 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-left-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_caption {
+#ajcojtljmw .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#ivbdpjimxj .gt_title {
+#ajcojtljmw .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -1236,7 +1236,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-width: 0;
 }
 
-#ivbdpjimxj .gt_subtitle {
+#ajcojtljmw .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -1248,7 +1248,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-top-width: 0;
 }
 
-#ivbdpjimxj .gt_heading {
+#ajcojtljmw .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -1260,13 +1260,13 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_bottom_border {
+#ajcojtljmw .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_col_headings {
+#ajcojtljmw .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1281,7 +1281,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_col_heading {
+#ajcojtljmw .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1301,7 +1301,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   overflow-x: hidden;
 }
 
-#ivbdpjimxj .gt_column_spanner_outer {
+#ajcojtljmw .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1313,15 +1313,15 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 4px;
 }
 
-#ivbdpjimxj .gt_column_spanner_outer:first-child {
+#ajcojtljmw .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#ivbdpjimxj .gt_column_spanner_outer:last-child {
+#ajcojtljmw .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#ivbdpjimxj .gt_column_spanner {
+#ajcojtljmw .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -1333,11 +1333,11 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   width: 100%;
 }
 
-#ivbdpjimxj .gt_spanner_row {
+#ajcojtljmw .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#ivbdpjimxj .gt_group_heading {
+#ajcojtljmw .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1363,7 +1363,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   text-align: left;
 }
 
-#ivbdpjimxj .gt_empty_group_heading {
+#ajcojtljmw .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -1378,15 +1378,15 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   vertical-align: middle;
 }
 
-#ivbdpjimxj .gt_from_md > :first-child {
+#ajcojtljmw .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#ivbdpjimxj .gt_from_md > :last-child {
+#ajcojtljmw .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#ivbdpjimxj .gt_row {
+#ajcojtljmw .gt_row {
   padding-top: 4px;
   padding-bottom: 4px;
   padding-left: 5px;
@@ -1405,7 +1405,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   overflow-x: hidden;
 }
 
-#ivbdpjimxj .gt_stub {
+#ajcojtljmw .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1418,7 +1418,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#ivbdpjimxj .gt_stub_row_group {
+#ajcojtljmw .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1432,15 +1432,15 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   vertical-align: top;
 }
 
-#ivbdpjimxj .gt_row_group_first td {
+#ajcojtljmw .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#ivbdpjimxj .gt_row_group_first th {
+#ajcojtljmw .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#ivbdpjimxj .gt_summary_row {
+#ajcojtljmw .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1450,16 +1450,16 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#ivbdpjimxj .gt_first_summary_row {
+#ajcojtljmw .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_first_summary_row.thick {
+#ajcojtljmw .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#ivbdpjimxj .gt_last_summary_row {
+#ajcojtljmw .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1469,7 +1469,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_grand_summary_row {
+#ajcojtljmw .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1479,7 +1479,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#ivbdpjimxj .gt_first_grand_summary_row {
+#ajcojtljmw .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1489,7 +1489,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-top-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_last_grand_summary_row_top {
+#ajcojtljmw .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1499,11 +1499,11 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_striped {
+#ajcojtljmw .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#ivbdpjimxj .gt_table_body {
+#ajcojtljmw .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1512,7 +1512,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-bottom-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_footnotes {
+#ajcojtljmw .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1526,7 +1526,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_footnote {
+#ajcojtljmw .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -1535,7 +1535,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#ivbdpjimxj .gt_sourcenotes {
+#ajcojtljmw .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1549,7 +1549,7 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   border-right-color: #D3D3D3;
 }
 
-#ivbdpjimxj .gt_sourcenote {
+#ajcojtljmw .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -1557,63 +1557,63 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
   padding-right: 5px;
 }
 
-#ivbdpjimxj .gt_left {
+#ajcojtljmw .gt_left {
   text-align: left;
 }
 
-#ivbdpjimxj .gt_center {
+#ajcojtljmw .gt_center {
   text-align: center;
 }
 
-#ivbdpjimxj .gt_right {
+#ajcojtljmw .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#ivbdpjimxj .gt_font_normal {
+#ajcojtljmw .gt_font_normal {
   font-weight: normal;
 }
 
-#ivbdpjimxj .gt_font_bold {
+#ajcojtljmw .gt_font_bold {
   font-weight: bold;
 }
 
-#ivbdpjimxj .gt_font_italic {
+#ajcojtljmw .gt_font_italic {
   font-style: italic;
 }
 
-#ivbdpjimxj .gt_super {
+#ajcojtljmw .gt_super {
   font-size: 65%;
 }
 
-#ivbdpjimxj .gt_footnote_marks {
+#ajcojtljmw .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#ivbdpjimxj .gt_asterisk {
+#ajcojtljmw .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#ivbdpjimxj .gt_indent_1 {
+#ajcojtljmw .gt_indent_1 {
   text-indent: 5px;
 }
 
-#ivbdpjimxj .gt_indent_2 {
+#ajcojtljmw .gt_indent_2 {
   text-indent: 10px;
 }
 
-#ivbdpjimxj .gt_indent_3 {
+#ajcojtljmw .gt_indent_3 {
   text-indent: 15px;
 }
 
-#ivbdpjimxj .gt_indent_4 {
+#ajcojtljmw .gt_indent_4 {
   text-indent: 20px;
 }
 
-#ivbdpjimxj .gt_indent_5 {
+#ajcojtljmw .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
@@ -1672,8 +1672,8 @@ <h2 id="cohort-overview-table-one">Cohort Overview (Table one)<a class="anchor"
 <span><span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/tableone.html">tableone</a></span><span class="op">(</span><span class="va">lung_cohort_tab1</span>, strata <span class="op">=</span> <span class="st">"Sex"</span>, overall <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>               title <span class="op">=</span> <span class="st">"Overview over Lung Cancer patients"</span>, datasource <span class="op">=</span> <span class="va">DATASET</span>, </span>
 <span>               engine <span class="op">=</span> <span class="st">"dt"</span><span class="op">)</span></span></code></pre></div>
-<div class="datatables html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-0cfdb9bc4eba8a3481ff" style="width:100%;height:auto;"></div>
-<script type="application/json" data-for="htmlwidget-0cfdb9bc4eba8a3481ff">{"x":{"filter":"none","vertical":false,"caption":"<caption>Overview over Lung Cancer patients<\/caption>","data":[["1","2","3","4"],["Age","Age","Age","Age"],["Mean (SD)","Median (IQR)","Min-max","Missing"],["61.1 (8.85)","61 (55-68)","41-77","0 (0%)"],["63.3 (9.14)","64 (57-70)","39-82","0 (0%)"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>variable<\/th>\n      <th>statistic<\/th>\n      <th>Female (N=90)<\/th>\n      <th>Male (N=138)<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":10,"language":{"search":"Filter:"},"scrollX":true,"paging":false,"ordering":false,"info":false,"drawCallback":"function(settings){\n  var datatable = settings.oInstance.api();\n  var table = datatable.table().node();\n  var n_captions = $(table).find('caption').length + 1;\n  var caption = 'Data Source: NCCTG Lung Cancer Dataset (from survival package 3.4.0) ';\n  if (n_captions <= 1) { $(table).append('<caption style=\"caption-side: bottom\">' + caption + '<\/caption>')};\n}","columnDefs":[{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":["options.drawCallback"],"jsHooks":[]}</script><p>…and <code>kable</code> for flexible displaying in various output
+<div class="datatables html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-72a1b8348b0a1e673f58" style="width:100%;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-72a1b8348b0a1e673f58">{"x":{"filter":"none","vertical":false,"caption":"<caption>Overview over Lung Cancer patients<\/caption>","data":[["1","2","3","4"],["Age","Age","Age","Age"],["Mean (SD)","Median (IQR)","Min-max","Missing"],["61.1 (8.85)","61 (55-68)","41-77","0 (0%)"],["63.3 (9.14)","64 (57-70)","39-82","0 (0%)"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>variable<\/th>\n      <th>statistic<\/th>\n      <th>Female (N=90)<\/th>\n      <th>Male (N=138)<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":10,"language":{"search":"Filter:"},"scrollX":true,"paging":false,"ordering":false,"info":false,"drawCallback":"function(settings){\n  var datatable = settings.oInstance.api();\n  var table = datatable.table().node();\n  var n_captions = $(table).find('caption').length + 1;\n  var caption = 'Data Source: NCCTG Lung Cancer Dataset (from survival package 3.4.0) ';\n  if (n_captions <= 1) { $(table).append('<caption style=\"caption-side: bottom\">' + caption + '<\/caption>')};\n}","columnDefs":[{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":["options.drawCallback"],"jsHooks":[]}</script><p>…and <code>kable</code> for flexible displaying in various output
 formats (<code>html</code> by default, <code>latex</code>
 supported).</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
@@ -1841,23 +1841,23 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
 <span></span>
 <span><span class="co"># Display the risktable</span></span>
 <span><span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/render.html">render</a></span><span class="op">(</span><span class="va">rt</span>, title <span class="op">=</span> <span class="st">"Overview over survival rates of Lung Cancer patients"</span>, datasource <span class="op">=</span> <span class="va">DATASET</span><span class="op">)</span></span></code></pre></div>
-<div id="mzokubqeup" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#mzokubqeup table {
+<div id="sbyulkhyuq" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#sbyulkhyuq table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#mzokubqeup thead, #mzokubqeup tbody, #mzokubqeup tfoot, #mzokubqeup tr, #mzokubqeup td, #mzokubqeup th {
+#sbyulkhyuq thead, #sbyulkhyuq tbody, #sbyulkhyuq tfoot, #sbyulkhyuq tr, #sbyulkhyuq td, #sbyulkhyuq th {
   border-style: none;
 }
 
-#mzokubqeup p {
+#sbyulkhyuq p {
   margin: 0;
   padding: 0;
 }
 
-#mzokubqeup .gt_table {
+#sbyulkhyuq .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -1883,12 +1883,12 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-left-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_caption {
+#sbyulkhyuq .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#mzokubqeup .gt_title {
+#sbyulkhyuq .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -1900,7 +1900,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-width: 0;
 }
 
-#mzokubqeup .gt_subtitle {
+#sbyulkhyuq .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -1912,7 +1912,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-top-width: 0;
 }
 
-#mzokubqeup .gt_heading {
+#sbyulkhyuq .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -1924,13 +1924,13 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_bottom_border {
+#sbyulkhyuq .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_col_headings {
+#sbyulkhyuq .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1945,7 +1945,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_col_heading {
+#sbyulkhyuq .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1965,7 +1965,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   overflow-x: hidden;
 }
 
-#mzokubqeup .gt_column_spanner_outer {
+#sbyulkhyuq .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1977,15 +1977,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 4px;
 }
 
-#mzokubqeup .gt_column_spanner_outer:first-child {
+#sbyulkhyuq .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#mzokubqeup .gt_column_spanner_outer:last-child {
+#sbyulkhyuq .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#mzokubqeup .gt_column_spanner {
+#sbyulkhyuq .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -1997,11 +1997,11 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   width: 100%;
 }
 
-#mzokubqeup .gt_spanner_row {
+#sbyulkhyuq .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#mzokubqeup .gt_group_heading {
+#sbyulkhyuq .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2027,7 +2027,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   text-align: left;
 }
 
-#mzokubqeup .gt_empty_group_heading {
+#sbyulkhyuq .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -2042,15 +2042,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   vertical-align: middle;
 }
 
-#mzokubqeup .gt_from_md > :first-child {
+#sbyulkhyuq .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#mzokubqeup .gt_from_md > :last-child {
+#sbyulkhyuq .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#mzokubqeup .gt_row {
+#sbyulkhyuq .gt_row {
   padding-top: 4px;
   padding-bottom: 4px;
   padding-left: 5px;
@@ -2069,7 +2069,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   overflow-x: hidden;
 }
 
-#mzokubqeup .gt_stub {
+#sbyulkhyuq .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2082,7 +2082,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#mzokubqeup .gt_stub_row_group {
+#sbyulkhyuq .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2096,15 +2096,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   vertical-align: top;
 }
 
-#mzokubqeup .gt_row_group_first td {
+#sbyulkhyuq .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#mzokubqeup .gt_row_group_first th {
+#sbyulkhyuq .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#mzokubqeup .gt_summary_row {
+#sbyulkhyuq .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2114,16 +2114,16 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#mzokubqeup .gt_first_summary_row {
+#sbyulkhyuq .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_first_summary_row.thick {
+#sbyulkhyuq .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#mzokubqeup .gt_last_summary_row {
+#sbyulkhyuq .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2133,7 +2133,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_grand_summary_row {
+#sbyulkhyuq .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2143,7 +2143,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#mzokubqeup .gt_first_grand_summary_row {
+#sbyulkhyuq .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2153,7 +2153,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-top-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_last_grand_summary_row_top {
+#sbyulkhyuq .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2163,11 +2163,11 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_striped {
+#sbyulkhyuq .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#mzokubqeup .gt_table_body {
+#sbyulkhyuq .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2176,7 +2176,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_footnotes {
+#sbyulkhyuq .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2190,7 +2190,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_footnote {
+#sbyulkhyuq .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -2199,7 +2199,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#mzokubqeup .gt_sourcenotes {
+#sbyulkhyuq .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2213,7 +2213,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#mzokubqeup .gt_sourcenote {
+#sbyulkhyuq .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -2221,63 +2221,63 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#mzokubqeup .gt_left {
+#sbyulkhyuq .gt_left {
   text-align: left;
 }
 
-#mzokubqeup .gt_center {
+#sbyulkhyuq .gt_center {
   text-align: center;
 }
 
-#mzokubqeup .gt_right {
+#sbyulkhyuq .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#mzokubqeup .gt_font_normal {
+#sbyulkhyuq .gt_font_normal {
   font-weight: normal;
 }
 
-#mzokubqeup .gt_font_bold {
+#sbyulkhyuq .gt_font_bold {
   font-weight: bold;
 }
 
-#mzokubqeup .gt_font_italic {
+#sbyulkhyuq .gt_font_italic {
   font-style: italic;
 }
 
-#mzokubqeup .gt_super {
+#sbyulkhyuq .gt_super {
   font-size: 65%;
 }
 
-#mzokubqeup .gt_footnote_marks {
+#sbyulkhyuq .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#mzokubqeup .gt_asterisk {
+#sbyulkhyuq .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#mzokubqeup .gt_indent_1 {
+#sbyulkhyuq .gt_indent_1 {
   text-indent: 5px;
 }
 
-#mzokubqeup .gt_indent_2 {
+#sbyulkhyuq .gt_indent_2 {
   text-indent: 10px;
 }
 
-#mzokubqeup .gt_indent_3 {
+#sbyulkhyuq .gt_indent_3 {
   text-indent: 15px;
 }
 
-#mzokubqeup .gt_indent_4 {
+#sbyulkhyuq .gt_indent_4 {
   text-indent: 20px;
 }
 
-#mzokubqeup .gt_indent_5 {
+#sbyulkhyuq .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
@@ -2356,23 +2356,23 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Display a summary of the survival estimate</span></span>
 <span><span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/render.html">render</a></span><span class="op">(</span><span class="va">lung_suvival_object</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/get_summary.html">get_summary</a></span><span class="op">(</span><span class="op">)</span>, title <span class="op">=</span> <span class="st">"Summary"</span>, datasource <span class="op">=</span> <span class="va">DATASET</span><span class="op">)</span></span></code></pre></div>
-<div id="muowjzpvky" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#muowjzpvky table {
+<div id="flyupaxotd" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#flyupaxotd table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#muowjzpvky thead, #muowjzpvky tbody, #muowjzpvky tfoot, #muowjzpvky tr, #muowjzpvky td, #muowjzpvky th {
+#flyupaxotd thead, #flyupaxotd tbody, #flyupaxotd tfoot, #flyupaxotd tr, #flyupaxotd td, #flyupaxotd th {
   border-style: none;
 }
 
-#muowjzpvky p {
+#flyupaxotd p {
   margin: 0;
   padding: 0;
 }
 
-#muowjzpvky .gt_table {
+#flyupaxotd .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -2398,12 +2398,12 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-left-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_caption {
+#flyupaxotd .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#muowjzpvky .gt_title {
+#flyupaxotd .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -2415,7 +2415,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-width: 0;
 }
 
-#muowjzpvky .gt_subtitle {
+#flyupaxotd .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -2427,7 +2427,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-top-width: 0;
 }
 
-#muowjzpvky .gt_heading {
+#flyupaxotd .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -2439,13 +2439,13 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_bottom_border {
+#flyupaxotd .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_col_headings {
+#flyupaxotd .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2460,7 +2460,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_col_heading {
+#flyupaxotd .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2480,7 +2480,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   overflow-x: hidden;
 }
 
-#muowjzpvky .gt_column_spanner_outer {
+#flyupaxotd .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2492,15 +2492,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 4px;
 }
 
-#muowjzpvky .gt_column_spanner_outer:first-child {
+#flyupaxotd .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#muowjzpvky .gt_column_spanner_outer:last-child {
+#flyupaxotd .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#muowjzpvky .gt_column_spanner {
+#flyupaxotd .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -2512,11 +2512,11 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   width: 100%;
 }
 
-#muowjzpvky .gt_spanner_row {
+#flyupaxotd .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#muowjzpvky .gt_group_heading {
+#flyupaxotd .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2542,7 +2542,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   text-align: left;
 }
 
-#muowjzpvky .gt_empty_group_heading {
+#flyupaxotd .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -2557,15 +2557,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   vertical-align: middle;
 }
 
-#muowjzpvky .gt_from_md > :first-child {
+#flyupaxotd .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#muowjzpvky .gt_from_md > :last-child {
+#flyupaxotd .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#muowjzpvky .gt_row {
+#flyupaxotd .gt_row {
   padding-top: 4px;
   padding-bottom: 4px;
   padding-left: 5px;
@@ -2584,7 +2584,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   overflow-x: hidden;
 }
 
-#muowjzpvky .gt_stub {
+#flyupaxotd .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2597,7 +2597,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#muowjzpvky .gt_stub_row_group {
+#flyupaxotd .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2611,15 +2611,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   vertical-align: top;
 }
 
-#muowjzpvky .gt_row_group_first td {
+#flyupaxotd .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#muowjzpvky .gt_row_group_first th {
+#flyupaxotd .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#muowjzpvky .gt_summary_row {
+#flyupaxotd .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2629,16 +2629,16 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#muowjzpvky .gt_first_summary_row {
+#flyupaxotd .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_first_summary_row.thick {
+#flyupaxotd .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#muowjzpvky .gt_last_summary_row {
+#flyupaxotd .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2648,7 +2648,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_grand_summary_row {
+#flyupaxotd .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2658,7 +2658,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#muowjzpvky .gt_first_grand_summary_row {
+#flyupaxotd .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2668,7 +2668,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-top-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_last_grand_summary_row_top {
+#flyupaxotd .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2678,11 +2678,11 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_striped {
+#flyupaxotd .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#muowjzpvky .gt_table_body {
+#flyupaxotd .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2691,7 +2691,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_footnotes {
+#flyupaxotd .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2705,7 +2705,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_footnote {
+#flyupaxotd .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -2714,7 +2714,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#muowjzpvky .gt_sourcenotes {
+#flyupaxotd .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2728,7 +2728,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#muowjzpvky .gt_sourcenote {
+#flyupaxotd .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -2736,63 +2736,63 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
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+#flyupaxotd .gt_left {
   text-align: left;
 }
 
-#muowjzpvky .gt_center {
+#flyupaxotd .gt_center {
   text-align: center;
 }
 
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+#flyupaxotd .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#muowjzpvky .gt_font_normal {
+#flyupaxotd .gt_font_normal {
   font-weight: normal;
 }
 
-#muowjzpvky .gt_font_bold {
+#flyupaxotd .gt_font_bold {
   font-weight: bold;
 }
 
-#muowjzpvky .gt_font_italic {
+#flyupaxotd .gt_font_italic {
   font-style: italic;
 }
 
-#muowjzpvky .gt_super {
+#flyupaxotd .gt_super {
   font-size: 65%;
 }
 
-#muowjzpvky .gt_footnote_marks {
+#flyupaxotd .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#muowjzpvky .gt_asterisk {
+#flyupaxotd .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#muowjzpvky .gt_indent_1 {
+#flyupaxotd .gt_indent_1 {
   text-indent: 5px;
 }
 
-#muowjzpvky .gt_indent_2 {
+#flyupaxotd .gt_indent_2 {
   text-indent: 10px;
 }
 
-#muowjzpvky .gt_indent_3 {
+#flyupaxotd .gt_indent_3 {
   text-indent: 15px;
 }
 
-#muowjzpvky .gt_indent_4 {
+#flyupaxotd .gt_indent_4 {
   text-indent: 20px;
 }
 
-#muowjzpvky .gt_indent_5 {
+#flyupaxotd .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
@@ -2844,23 +2844,23 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Display test statistics associated with the survival estimate</span></span>
 <span><span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/render.html">render</a></span><span class="op">(</span><span class="va">lung_suvival_object</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/get_pvalue.html">get_pvalue</a></span><span class="op">(</span><span class="op">)</span>, title <span class="op">=</span> <span class="st">"P-values"</span>, datasource <span class="op">=</span> <span class="va">DATASET</span><span class="op">)</span></span></code></pre></div>
-<div id="qifvoaecnz" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#qifvoaecnz table {
+<div id="abkccooefw" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#abkccooefw table {
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 }
 
-#qifvoaecnz thead, #qifvoaecnz tbody, #qifvoaecnz tfoot, #qifvoaecnz tr, #qifvoaecnz td, #qifvoaecnz th {
+#abkccooefw thead, #abkccooefw tbody, #abkccooefw tfoot, #abkccooefw tr, #abkccooefw td, #abkccooefw th {
   border-style: none;
 }
 
-#qifvoaecnz p {
+#abkccooefw p {
   margin: 0;
   padding: 0;
 }
 
-#qifvoaecnz .gt_table {
+#abkccooefw .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -2886,12 +2886,12 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-left-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_caption {
+#abkccooefw .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#qifvoaecnz .gt_title {
+#abkccooefw .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -2903,7 +2903,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-width: 0;
 }
 
-#qifvoaecnz .gt_subtitle {
+#abkccooefw .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -2915,7 +2915,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-top-width: 0;
 }
 
-#qifvoaecnz .gt_heading {
+#abkccooefw .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -2927,13 +2927,13 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_bottom_border {
+#abkccooefw .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_col_headings {
+#abkccooefw .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2948,7 +2948,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_col_heading {
+#abkccooefw .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2968,7 +2968,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   overflow-x: hidden;
 }
 
-#qifvoaecnz .gt_column_spanner_outer {
+#abkccooefw .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2980,15 +2980,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 4px;
 }
 
-#qifvoaecnz .gt_column_spanner_outer:first-child {
+#abkccooefw .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#qifvoaecnz .gt_column_spanner_outer:last-child {
+#abkccooefw .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#qifvoaecnz .gt_column_spanner {
+#abkccooefw .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -3000,11 +3000,11 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   width: 100%;
 }
 
-#qifvoaecnz .gt_spanner_row {
+#abkccooefw .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#qifvoaecnz .gt_group_heading {
+#abkccooefw .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3030,7 +3030,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   text-align: left;
 }
 
-#qifvoaecnz .gt_empty_group_heading {
+#abkccooefw .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -3045,15 +3045,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   vertical-align: middle;
 }
 
-#qifvoaecnz .gt_from_md > :first-child {
+#abkccooefw .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#qifvoaecnz .gt_from_md > :last-child {
+#abkccooefw .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#qifvoaecnz .gt_row {
+#abkccooefw .gt_row {
   padding-top: 4px;
   padding-bottom: 4px;
   padding-left: 5px;
@@ -3072,7 +3072,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   overflow-x: hidden;
 }
 
-#qifvoaecnz .gt_stub {
+#abkccooefw .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3085,7 +3085,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#qifvoaecnz .gt_stub_row_group {
+#abkccooefw .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3099,15 +3099,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   vertical-align: top;
 }
 
-#qifvoaecnz .gt_row_group_first td {
+#abkccooefw .gt_row_group_first td {
   border-top-width: 2px;
 }
 
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+#abkccooefw .gt_row_group_first th {
   border-top-width: 2px;
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-#qifvoaecnz .gt_summary_row {
+#abkccooefw .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -3117,16 +3117,16 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#qifvoaecnz .gt_first_summary_row {
+#abkccooefw .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_first_summary_row.thick {
+#abkccooefw .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#qifvoaecnz .gt_last_summary_row {
+#abkccooefw .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3136,7 +3136,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_grand_summary_row {
+#abkccooefw .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -3146,7 +3146,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#qifvoaecnz .gt_first_grand_summary_row {
+#abkccooefw .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3156,7 +3156,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-top-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_last_grand_summary_row_top {
+#abkccooefw .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3166,11 +3166,11 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_striped {
+#abkccooefw .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#qifvoaecnz .gt_table_body {
+#abkccooefw .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -3179,7 +3179,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_footnotes {
+#abkccooefw .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -3193,7 +3193,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_footnote {
+#abkccooefw .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -3202,7 +3202,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#qifvoaecnz .gt_sourcenotes {
+#abkccooefw .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -3216,7 +3216,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#qifvoaecnz .gt_sourcenote {
+#abkccooefw .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -3224,63 +3224,63 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#qifvoaecnz .gt_left {
+#abkccooefw .gt_left {
   text-align: left;
 }
 
-#qifvoaecnz .gt_center {
+#abkccooefw .gt_center {
   text-align: center;
 }
 
-#qifvoaecnz .gt_right {
+#abkccooefw .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#qifvoaecnz .gt_font_normal {
+#abkccooefw .gt_font_normal {
   font-weight: normal;
 }
 
-#qifvoaecnz .gt_font_bold {
+#abkccooefw .gt_font_bold {
   font-weight: bold;
 }
 
-#qifvoaecnz .gt_font_italic {
+#abkccooefw .gt_font_italic {
   font-style: italic;
 }
 
-#qifvoaecnz .gt_super {
+#abkccooefw .gt_super {
   font-size: 65%;
 }
 
-#qifvoaecnz .gt_footnote_marks {
+#abkccooefw .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#qifvoaecnz .gt_asterisk {
+#abkccooefw .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#qifvoaecnz .gt_indent_1 {
+#abkccooefw .gt_indent_1 {
   text-indent: 5px;
 }
 
-#qifvoaecnz .gt_indent_2 {
+#abkccooefw .gt_indent_2 {
   text-indent: 10px;
 }
 
-#qifvoaecnz .gt_indent_3 {
+#abkccooefw .gt_indent_3 {
   text-indent: 15px;
 }
 
-#qifvoaecnz .gt_indent_4 {
+#abkccooefw .gt_indent_4 {
   text-indent: 20px;
 }
 
-#qifvoaecnz .gt_indent_5 {
+#abkccooefw .gt_indent_5 {
   text-indent: 25px;
 }
 </style>
@@ -3329,23 +3329,23 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Display qunatile information of the survival estimate</span></span>
 <span><span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/render.html">render</a></span><span class="op">(</span><span class="va">lung_suvival_object</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/get_quantile.html">get_quantile</a></span><span class="op">(</span><span class="op">)</span>, title <span class="op">=</span> <span class="st">"Quantile Information"</span>, datasource <span class="op">=</span> <span class="va">DATASET</span><span class="op">)</span></span></code></pre></div>
-<div id="labjvbijow" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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@@ -3371,12 +3371,12 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
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 </style>
@@ -3840,23 +3840,23 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
 <code class="sourceCode R"><span><span class="co"># Display a cox model estimate associated with the survival estimate</span></span>
 <span><span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/render.html">render</a></span><span class="op">(</span><span class="va">lung_suvival_object</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu">visR</span><span class="fu">::</span><span class="fu"><a href="../reference/get_COX_HR.html">get_COX_HR</a></span><span class="op">(</span><span class="op">)</span>, title <span class="op">=</span> <span class="st">"COX estimate"</span>, datasource <span class="op">=</span> <span class="va">DATASET</span><span class="op">)</span></span>
 <span><span class="co">#&gt; tidyme S3 default method (broom::tidy) used.</span></span></code></pre></div>
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-<style>#lclbsnlpkk table {
+<div id="ickjtzercz" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#ickjtzercz table {
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-#lclbsnlpkk thead, #lclbsnlpkk tbody, #lclbsnlpkk tfoot, #lclbsnlpkk tr, #lclbsnlpkk td, #lclbsnlpkk th {
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+#ickjtzercz .gt_table {
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@@ -3882,12 +3882,12 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-left-color: #D3D3D3;
 }
 
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+#ickjtzercz .gt_caption {
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+#ickjtzercz .gt_subtitle {
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@@ -3911,7 +3911,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-top-width: 0;
 }
 
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+#ickjtzercz .gt_heading {
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@@ -3923,13 +3923,13 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
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+#ickjtzercz .gt_col_headings {
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@@ -3944,7 +3944,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#lclbsnlpkk .gt_col_heading {
+#ickjtzercz .gt_col_heading {
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@@ -3964,7 +3964,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
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+#ickjtzercz .gt_column_spanner_outer {
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@@ -3976,15 +3976,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 4px;
 }
 
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+#ickjtzercz .gt_column_spanner_outer:first-child {
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+#ickjtzercz .gt_column_spanner {
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@@ -3996,11 +3996,11 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   width: 100%;
 }
 
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@@ -4026,7 +4026,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
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+#ickjtzercz .gt_empty_group_heading {
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@@ -4041,15 +4041,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
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+#ickjtzercz .gt_stub {
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@@ -4081,7 +4081,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
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 }
 
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@@ -4095,15 +4095,15 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
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-#lclbsnlpkk .gt_grand_summary_row {
+#ickjtzercz .gt_grand_summary_row {
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@@ -4142,7 +4142,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
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@@ -4162,11 +4162,11 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
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+#ickjtzercz .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
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+#ickjtzercz .gt_table_body {
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@@ -4175,7 +4175,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-bottom-color: #D3D3D3;
 }
 
-#lclbsnlpkk .gt_footnotes {
+#ickjtzercz .gt_footnotes {
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@@ -4189,7 +4189,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
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+#ickjtzercz .gt_footnote {
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@@ -4198,7 +4198,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#lclbsnlpkk .gt_sourcenotes {
+#ickjtzercz .gt_sourcenotes {
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@@ -4212,7 +4212,7 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   border-right-color: #D3D3D3;
 }
 
-#lclbsnlpkk .gt_sourcenote {
+#ickjtzercz .gt_sourcenote {
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   padding-top: 4px;
   padding-bottom: 4px;
@@ -4220,63 +4220,63 @@ <h4 id="displaying-the-risktable">Displaying the risktable<a class="anchor" aria
   padding-right: 5px;
 }
 
-#lclbsnlpkk .gt_left {
+#ickjtzercz .gt_left {
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diff --git a/articles/interactive_tte_tutorial/tte_tutorial.html b/articles/interactive_tte_tutorial/tte_tutorial.html
index c506812f..18c19687 100644
--- a/articles/interactive_tte_tutorial/tte_tutorial.html
+++ b/articles/interactive_tte_tutorial/tte_tutorial.html
@@ -20645,7 +20645,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -20699,7 +20699,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -20753,7 +20753,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 9, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -20804,7 +20804,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -20857,7 +20857,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -20908,7 +20908,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -20961,7 +20961,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21013,7 +21013,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21064,7 +21064,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21115,7 +21115,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21166,7 +21166,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21218,7 +21218,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21271,7 +21271,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21322,7 +21322,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21373,7 +21373,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21425,7 +21425,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21477,7 +21477,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21528,7 +21528,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21579,7 +21579,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21631,7 +21631,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 7, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21682,7 +21682,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21733,7 +21733,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21788,7 +21788,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         strip.white = TRUE, cache = 0, cache.path = "tte_tutorial_cache/html/", 
         cache.vars = NULL, cache.lazy = TRUE, dependson = NULL, 
         autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high", 
-        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.show = "asis", fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21853,7 +21853,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
@@ -21906,7 +21906,7 @@ <h3 id="section-possible-solution-to-the-exercise-for-recreating-figure-5">Possi
         cache.path = "tte_tutorial_cache/html/", cache.vars = NULL, 
         cache.lazy = TRUE, dependson = NULL, autodep = FALSE, 
         cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", 
-        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
+        fig.align = "default", fig.path = "/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/", 
         dev = "ragg_png", dev.args = list(bg = NA), dpi = 192L, 
         fig.ext = "png", fig.width = 7.29166666666667, fig.height = 4.50659250103008, 
         fig.env = "figure", fig.cap = NULL, fig.scap = NULL, 
diff --git a/pkgdown.yml b/pkgdown.yml
index 20ce7ea3..990445c5 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -7,7 +7,7 @@ articles:
   Styling_KM_plots: Styling_KM_plots.html
   Time_to_event_analysis: Time_to_event_analysis.html
   tte_tutorial: interactive_tte_tutorial/tte_tutorial.html
-last_built: 2023-11-20T19:39Z
+last_built: 2023-11-20T19:45Z
 urls:
   reference: https://openpharma.github.io/visR/reference
   article: https://openpharma.github.io/visR/articles
diff --git a/search.json b/search.json
index cdc9167b..1dd6cee4 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://openpharma.github.io/visR/CODE_OF_CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behavior participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behavior may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://www.contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/.","code":""},{"path":"https://openpharma.github.io/visR/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022 visR authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://openpharma.github.io/visR/articles/CDISC_ADaM.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Survival Analysis with visR using CDISC ADaM Time-To-Event Analysis Dataset (ADTTE)","text":"tutorial illustrates standard time--event analysis can done efficiently data set adheres CDISC ADaM standard. detailed time--event analysis broad overview visR’s functionality presented another vignette.","code":"library(ggplot2) library(visR)"},{"path":"https://openpharma.github.io/visR/articles/CDISC_ADaM.html","id":"global-document-setup","dir":"Articles","previous_headings":"","what":"Global Document Setup","title":"Survival Analysis with visR using CDISC ADaM Time-To-Event Analysis Dataset (ADTTE)","text":"","code":"# Metadata Title DATASET <- paste0(\"Analyis Data Time-To-Event (ADTTE)\")  # Save original options() old <- options()    # Global formatting options options(digits = 3)  # Global ggplot settings theme_set(theme_bw())  # Global table settings  options(DT.options = list(pageLength = 10,                            language = list(search = 'Filter:'),                            scrollX = TRUE))  # load ADTTE from CDISC pilot  data(adtte)  # Restore original options() options(old)"},{"path":"https://openpharma.github.io/visR/articles/CDISC_ADaM.html","id":"time-to-event-analysis","dir":"Articles","previous_headings":"","what":"Time-to-event analysis","title":"Survival Analysis with visR using CDISC ADaM Time-To-Event Analysis Dataset (ADTTE)","text":"visR includes wrapper function easily display summary tables (e.g. tableone) wrapper function estimate Kaplan-Meier curve compatible %>% purrr::map functions without losing traceability dataset name inside call object. data set adheres CDISC ADaM standards, stratifier needs specified. Given survival object, visR includes several functions quickly extract additional information survival object (e.g. test statistics p-values) general function display table (render). survival object can plotted using visR function visr. Additional information like confidence intervals risktable can added plot.","code":"# Display a summary table (e.g. tableone) visR::tableone(adtte[,c(\"TRTP\", \"AGE\")],          title = \"Demographic summary\" , datasource = DATASET) #> Warning: There was 1 warning in `summarise()`. #> ℹ In argument: `TRTP = (function (x) ...`. #> ℹ In group 1: `all = \"Total\"`. #> Caused by warning: #> ! `fct_explicit_na()` was deprecated in forcats 1.0.0. #> ℹ Please use `fct_na_value_to_level()` instead. #> ℹ The deprecated feature was likely used in the visR package. #>   Please report the issue at <https://github.com/openpharma/visR/issues>. # Estimate a survival object survfit_object <-  adtte %>%   visR::estimate_KM(data = ., strata = \"TRTP\") #> Warning: `estimate_KM()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated. survfit_object #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     TRTP, data = data) #>  #>                            n events median 0.95LCL 0.95UCL #> TRTP=Placebo              86     29     NA      NA      NA #> TRTP=Xanomeline High Dose 84     61     36      25      47 #> TRTP=Xanomeline Low Dose  84     62     33      28      51 # Display test statistics associated with the survival estimate  visR::render(survfit_object %>% get_pvalue(), title = \"P-values\", datasource = DATASET) # Create and display a Kaplan-Meier from the survival object and add a risktable visr(survfit_object) %>%    visR::add_CI() %>%   visR::add_risktable() #> Warning: `visr.survfit()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"data-preparation","dir":"Articles","previous_headings":"Attrition example","what":"Data preparation","title":"Creating consort flow diagram with visR","text":"Prepare data using attrition function.","code":"attrition <- visR::get_attrition(adtte,    criteria_descriptions = c(\"1. Not in Placebo Group\",                              \"2. Be 75 years of age or older.\",                              \"3. White\",                              \"4. Female\"),    criteria_conditions   = c(\"TRTP != 'Placebo'\",                              \"AGE >= 75\",                              \"RACE=='WHITE'\",                              \"SEX=='F'\"),    subject_column_name   = \"USUBJID\")"},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"render-chart","dir":"Articles","previous_headings":"Attrition example","what":"Render chart","title":"Creating consort flow diagram with visR","text":"Draw CONSORT attrition chart without specifying extra text complement","code":"attrition %>%   visR::visr(\"Criteria\", \"Remaining N\")"},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"adding-more-detail","dir":"Articles","previous_headings":"Attrition example","what":"Adding more detail","title":"Creating consort flow diagram with visR","text":"Adding detailed complement descriptions “exclusion” part CONSORT diagram","code":""},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"add-the-control-group","dir":"Articles","previous_headings":"Attrition example > Adding more detail","what":"Add the control group","title":"Creating consort flow diagram with visR","text":"Step 1. Add new column attrition dataframe","code":"attrition$Complement <- c(\"NA\", \"Placebo Group\", \"Younger than 75 years\", \"Non-White\", \"Male\")"},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"define-metadata","dir":"Articles","previous_headings":"Attrition example > Adding more detail","what":"Define metadata","title":"Creating consort flow diagram with visR","text":"Step 2. Define name column call plotting function","code":"attrition %>%   visR::visr(\"Criteria\", \"Remaining N\", \"Complement\")"},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"styling-the-consort-flowchart-","dir":"Articles","previous_headings":"Additional features","what":"Styling the CONSORT flowchart.","title":"Creating consort flow diagram with visR","text":"Change fill outline boxes flowchart","code":"attrition %>%   visR::visr(\"Criteria\", \"Remaining N\", \"Complement\", fill = \"lightblue\", border=\"grey\")"},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"adjusting-size","dir":"Articles","previous_headings":"Additional features","what":"Adjusting size","title":"Creating consort flow diagram with visR","text":"Adjust font size boxes","code":"attrition %>%  visR::visr(\"Criteria\", \"Remaining N\", font_size = 10)"},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Styling survival plots","text":"tutorial illustrates usage styling function visR provides. default, visR::visr() apply form visual changes generated survival plots. Therefore, default output looks like expect standard ggplot2::ggplot() plot. examples visualize results estimate_KM(), principles apply competing risks cumulative incidence objects created estimate_cuminc() well.","code":"library(visR)"},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"preparation-of-the-data","dir":"Articles","previous_headings":"Introduction","what":"Preparation of the data","title":"Styling survival plots","text":"example, work patient data NCCTG Lung Cancer dataset part survival package. data also used demonstrate functions visR another vignette. However, particular one, used demonstrate adjustments aesthetics.","code":""},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"generation-of-a-survfit-object","dir":"Articles","previous_headings":"Introduction","what":"Generation of a survfit object","title":"Styling survival plots","text":"","code":"lung_cohort <- survival::lung  lung_cohort <- lung_cohort %>%     dplyr::mutate(sex = as.factor(ifelse(sex == 1, \"Male\", \"Female\")))  %>%     dplyr::mutate(status = status - 1) %>%   dplyr::rename(Age = \"age\", Sex = \"sex\", Status = \"status\", Days = \"time\")   lung_suvival_object <- lung_cohort %>%   visR::estimate_KM(strata = \"Sex\", CNSR = \"Status\", AVAL = \"Days\") #> Warning: `estimate_KM()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"plotting-the-generated-survfit-object-without-adjustments","dir":"Articles","previous_headings":"Styling","what":"Plotting the generated survfit object without adjustments","title":"Styling survival plots","text":"can, plot shows default ggplot2::theme_grey() style grey background, visible grid default ggplot2 colours.","code":"p <- lung_suvival_object %>%   visR::visr() #> Warning: `visr.survfit()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated. p"},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"using-ggplot2-to-style-the-plot","dir":"Articles","previous_headings":"Styling","what":"Using ggplot2 to style the plot","title":"Styling survival plots","text":"Since visR::visr() also generates valid ggplot object output, can use conventional styling logic options ggplot2 provides, shown .  However, visR also provides functions adjust common aesthetics easily less code.","code":"p +   ggplot2::theme_bw() +   ggplot2::theme(legend.position = \"top\") +   ggplot2::scale_color_manual(values = c(\"red\", \"blue\"))"},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"using-visr-to-style-the-plot","dir":"Articles","previous_headings":"Styling","what":"Using visR to style the plot","title":"Styling survival plots","text":"direct option style plots generated visR::visr() using parameters function provides. Internally, parameters like y-axis label automatically deducted used function. following example demonstrates options exposed.  However, rather minimal adjustments usually don’t cover things user wants modify. Therefore, provide two additional functions adjust aesthetics: visR::define_theme() visR::apply_theme(). first one provides easy wrapper create nested list list styling options applied plot second function.","code":"lung_suvival_object %>%   visR::visr(x_label = \"Time\",              y_label = NULL, # NULL (default) causes the label to be deducted from the used function              x_ticks = seq(0, 1200, 200),              y_ticks = seq(0, 100, 20),              fun = \"pct\",              legend_position = \"top\")"},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"defining-a-visr_theme-using-visrdefine_theme","dir":"Articles","previous_headings":"New themes","what":"Defining a visR_theme using visR::define_theme()","title":"Styling survival plots","text":"options provided visR::define_theme(), nonetheless returns minimal list reasonable defaults. However, function also takes several styling options. currently usable ones displayed . One particular use mind writing function , wanted option define different colours strata worry present.","code":"visR::define_theme()  #> $fontfamily #> [1] \"Helvetica\" #>  #> $grid #> [1] FALSE #>  #> $bg #> [1] \"transparent\" #>  #> attr(,\"class\") #> [1] \"visR_theme\" \"list\" theme <-   visR::define_theme(     strata = list(       \"Sex\" = list(\"Female\" = \"red\",                    \"Male\" = \"blue\"),       \"ph.ecog\" = list(\"0\" = \"cyan\",                        \"1\" = \"purple\",                        \"2\" = \"brown\")     ),     fontsizes = list(       \"axis\" = 12,       \"ticks\" = 10,       \"legend_title\" = 10,       \"legend_text\" = 8     ),     fontfamily = \"Helvetica\",     grid = list(\"major\" = FALSE,                 \"minor\" = FALSE),     #grid = TRUE/FALSE # <- can also be used instead of the named list above     bg = \"transparent\",     legend_position = \"top\"   )"},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"apply-styling-using-visrapply_theme","dir":"Articles","previous_headings":"New themes","what":"Apply styling using visR::apply_theme()","title":"Styling survival plots","text":"visR::apply_theme() function exposes user two ways style plot. direct one just apply function plot without specifying options. applies several reasonable defaults plot.  second one apply nested list lists , ideally generated visR::define_theme() plot. serves purpose generate detailed visR_theme object apply one several plots single line. lists also easily saved shared. usage theme generated shown .","code":"lung_suvival_object %>%   visR::visr() %>%   visR::apply_theme() lung_suvival_object %>%   visR::visr() %>%   visR::apply_theme(theme)"},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Survival Analysis with visR","text":"tutorial illustrates typical use case clinical development - analysis time certain event (e.g., death) different groups. Typically, data obtained randomized clinical trials (RCT) can used estimate overall survival patients one group (e.g., treated drug X) vs another group (e.g., treated drug Y) thus determine treatment difference. thorough introduction Survival Analysis, recommend following tutorial. example, work patient data NCCTG Lung Cancer dataset part survival package. Another vignette presents example using data set following CDISC ADaM standard.","code":"library(ggplot2) library(visR)"},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"global-document-setup","dir":"Articles","previous_headings":"","what":"Global Document Setup","title":"Survival Analysis with visR","text":"","code":"# Metadata Title DATASET <- paste0(\"NCCTG Lung Cancer Dataset (from survival package \",                    packageVersion(\"survival\"), \")\")  # Save original options() old <- options()    # Global formatting options options(digits = 3)  # Global ggplot settings theme_set(theme_bw())  # Global table settings  options(DT.options = list(pageLength = 10,                            language = list(search = 'Filter:'),                            scrollX = TRUE))  lung_cohort <- survival::lung  # Change gender to be a factor and rename some variables to make output look nicer lung_cohort <- lung_cohort %>%     dplyr::mutate(sex = as.factor(ifelse(sex == 1, \"Male\", \"Female\")))  %>%     dplyr::rename(Age = \"age\", Sex = \"sex\", Status = \"status\", Days = \"time\")  # Restore original options() options(old)"},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"cohort-overview-table-one","dir":"Articles","previous_headings":"","what":"Cohort Overview (Table one)","title":"Survival Analysis with visR","text":"Visualizing tables, like table one risk tables, two-step process visR . First, data.frame (tibble) created get_XXX() function (e.g. get_tableone()). Secondly, data.frame can displayed calling function render(). advantage process data summaries can created, used adjusted throughout analysis, every step data summaries can displayed even downloaded. Populations usually displayed -called table one. Function get_tableone creates tibble includes populations summaries. Function render nicely displays tableone. Additionally, visR includes wrapper function create display tableone one function call. Creating visualizing tableone default settings simple can done one line code. However, customization options. get wrapper functions, stratifier can defined column displaying total information can removed. visR’s render supports three different rendering engines flexible possible. default, render uses gt. Additional engines datatable (dt) include easy downloading options… …kable flexible displaying various output formats (html default, latex supported). Overview Lung Cancer patients Called html output format, html view displayed; called latex string containing latex code printed.","code":"# Select variables of interest and change names to look nicer lung_cohort_tab1 <- lung_cohort %>%     dplyr::select(Age, Sex)   # Create a table one tab1 <- visR::get_tableone(lung_cohort_tab1) #> Warning: There was 1 warning in `summarise()`. #> ℹ In argument: `Sex = (function (x) ...`. #> ℹ In group 1: `all = \"Total\"`. #> Caused by warning: #> ! `fct_explicit_na()` was deprecated in forcats 1.0.0. #> ℹ Please use `fct_na_value_to_level()` instead. #> ℹ The deprecated feature was likely used in the visR package. #>   Please report the issue at <https://github.com/openpharma/visR/issues>.  # Render the tableone visR::render(tab1, title = \"Overview over Lung Cancer patients\", datasource = DATASET) # Use wrapper functionality to create and display a tableone visR::tableone(lung_cohort_tab1, title = \"Overview over Lung Cancer patients\", datasource = DATASET) # Create and render a tableone with a stratifier and without displaying the total visR::tableone(lung_cohort_tab1, strata = \"Sex\", overall = FALSE,                title = \"Overview over Lung Cancer patients\", datasource = DATASET) # Create and render a tableone with with dt as an engine visR::tableone(lung_cohort_tab1, strata = \"Sex\", overall = FALSE,                title = \"Overview over Lung Cancer patients\", datasource = DATASET,                 engine = \"dt\") # Create and render a tableone with with kable as an engine and html as output format visR::tableone(lung_cohort_tab1, strata = \"Sex\", overall = FALSE,                 title = \"Overview over Lung Cancer patients\", datasource = DATASET,                 engine = \"kable\", output_format=\"html\")"},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"survival-estimation","dir":"Articles","previous_headings":"Time-to-event analysis","what":"Survival estimation","title":"Survival Analysis with visR","text":"visR provides wrapper function estimate Kaplan-Meier curve several functions visualize results. wrapper function compatible %>% purrr::map functions without losing traceability dataset name.","code":"# Select variables of interest and change names to look nicer lung_cohort_survival <- lung_cohort %>%     dplyr::select(Age, Sex, Status, Days)    # For the survival estimate, the censor must be 0 or 1 lung_cohort_survival$Status <- lung_cohort_survival$Status - 1  # Estimate the survival curve lung_suvival_object <- lung_cohort_survival %>%    visR::estimate_KM(strata = \"Sex\", CNSR = \"Status\", AVAL = \"Days\") #> Warning: `estimate_KM()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated. lung_suvival_object #> Call: ~survival::survfit(formula = survival::Surv(Days, 1 - Status) ~  #>     Sex, data = data) #>  #>              n events median 0.95LCL 0.95UCL #> Sex=Female  90     37    529     376      NA #> Sex=Male   138     26    840     806      NA"},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"survival-visualization","dir":"Articles","previous_headings":"Time-to-event analysis","what":"Survival visualization","title":"Survival Analysis with visR","text":"two frequently used ways estimate time--event data: risk table Kaplan-Meier curve. principle, visR allows either visualize risk table Kaplan-Meier curve separately, together one plot.","code":""},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"displaying-the-risktable","dir":"Articles","previous_headings":"Time-to-event analysis > Survival visualization","what":"Displaying the risktable","title":"Survival Analysis with visR","text":"Creating visualizing risk table separately works exact way tableone (): First, get_risktable() creates tibble risk information can still changed. Secondly, risk table can rendered displayed. risktable one piece information can extracted survival object get_XXX rendered.","code":"# Create a risktable rt <- visR::get_risktable(lung_suvival_object)  # Display the risktable visR::render(rt, title = \"Overview over survival rates of Lung Cancer patients\", datasource = DATASET) # Display a summary of the survival estimate visR::render(lung_suvival_object %>% visR::get_summary(), title = \"Summary\", datasource = DATASET) # Display test statistics associated with the survival estimate visR::render(lung_suvival_object %>% visR::get_pvalue(), title = \"P-values\", datasource = DATASET) # Display qunatile information of the survival estimate visR::render(lung_suvival_object %>% visR::get_quantile(), title = \"Quantile Information\", datasource = DATASET) # Display a cox model estimate associated with the survival estimate visR::render(lung_suvival_object %>% visR::get_COX_HR(), title = \"COX estimate\", datasource = DATASET) #> tidyme S3 default method (broom::tidy) used."},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"plotting-the-kaplan-meier","dir":"Articles","previous_headings":"Time-to-event analysis > Survival visualization","what":"Plotting the Kaplan-Meier","title":"Survival Analysis with visR","text":"Alternatively, survival data can plotted Kaplan-Meier curve. visR, plot cases ggplot object adapting plot follows general principle creating plot adding visual contents step--step.    visR includes wrapper function create risktable add directly Kaplan-Meier plot.","code":"# Create and display a Kaplan-Meier from the survival object gg <- visR::visr(lung_suvival_object) #> Warning: `visr.survfit()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated. gg # Add a confidence interval to the Kaplan-Meier and display the plot gg %>% visR::add_CI() # Add a confidence interval and the censor ticks to the Kaplan-Meier and display the plot gg %>% visR::add_CI() %>% visR::add_CNSR(shape = 3, size = 2) # Add a confidence interval and the censor ticks and a risktable to the Kaplan-Meier and display the plot gg %>% visR::add_CI() %>% visR::add_CNSR(shape = 3, size = 2) %>% visR::add_risktable()"},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"competing-risks","dir":"Articles","previous_headings":"","what":"Competing Risks","title":"Survival Analysis with visR","text":"addition classic right-censored data, {visR} package supports estimation time--event outcomes presence competing events. package wraps {tidycmprsk} package, exports functions cumulative incidence estimation visualization. function estimate_cuminc() estimates cumulative incidence competing event outcome interest. syntax nearly identical estimate_KM(); however, outcome status variable (passed CNSR= argument) must factor first level indicates censoring, second level competing event interest, subsequent levels competing events. Visualization functions, visr(), add_CI(), add_CNSR(), add_risktable() share syntax Kaplan-Meier variants.","code":"visR::estimate_cuminc(   tidycmprsk::trial,   strata = \"trt\",   CNSR = \"death_cr\",   AVAL = \"ttdeath\" ) %>%   visR::visr(     legend_position = \"bottom\",      x_label = \"Months from Treatment\",     y_label = \"Risk of Death\"   ) %>%   visR::add_CI() %>%   visR::add_risktable(statlist = c(\"n.risk\", \"cum.event\")) #> Warning: `visr.tidycuminc()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggcuminc()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-visr---a-package-for-effective-visualizations-in-pharma","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Welcome","what":"visR - A package for Effective Visualizations in Pharma","title":"visR","text":"tutorial introduce basic usage visR, R package effective visual communication. package presents easy--use interface visualizations relevant clinical development process, implementing several best practices. developed part openpharma initiative, effort open-source cross-industry collaboration.","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-further-reading","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Welcome","what":"Further reading","title":"visR","text":"graphical principles visR aims implement [graphicsprinciples.github.io] visR Documentation [openpharma.github.io/visR] visR @ GitHub [github.com/openpharma/visR] visR @ CRAN [cran.r-project.org/web/packages/visR] openpharma overview [openpharma.github.io]","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-package-architecture","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Package architecture","title":"visR","text":"visR package implements simple--use interface adhering consistent naming conventions.","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-overview","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Package architecture","what":"Overview","title":"visR","text":"principle, extimate_XXX functions allow estimate statistical models data (e.g. estimate_KM estimates survival model), get_XXX functions get kinds summary statistics (e.g. get_tableone get_pvalue), render visr display plots tables, add_XXX functions add specific information plot table (e.g. add_CI adds confidence intervals plot.)","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-outline","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Package architecture","what":"Outline","title":"visR","text":"tutorial walk basic survival analysis show-casing principles features visR. start introducing data set provided COVID-19 India Org Data Operations Group use throughout tutorial. introduce -called table 1 gives overview patient population. estimate_ survival model introduce functions get_ summary statistics. plot survival model show can add_ additional information . integrate summary statistics plots.","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-the-study-and-the-data","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"The study and the data","title":"visR","text":"Risk Survival Analysis COVID Outbreak Data :Lessons India goal paper assess mortality India due COVID-19. understand survival COVID-19 patients, time--event analysis performed based Kaplan-Meier estimates using gender age group (separately combined) strata. According authors (Bankar et al., 2021), probably biggest retrospective-cohort survival analysis conducted India, according authors. paper used publicly available data COVID-19 India Org Data Operations Group. figure roughly described trends distributions patient population.","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-the-data","dir":"Articles > Interactive_tte_tutorial","previous_headings":"The study and the data","what":"The data","title":"visR","text":"Patients admitted Karnakata, South India, recruited. final dataset consists 26,741 patients. Age gender patient, number days hospital per stay, status patient (1=censored/alive, 2=dead) age category (<18 yr, 18 yr – 60 yr, >60 yr) collected. [Publication] [GitHub] [Raw data]","code":"# First, we set the default ggplot2 theme theme_set(theme_bw())  # Then, we directly load the data from their GitHub repository data = read.csv(\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\")  data Age Sex Stay Status Age_Cat 1      46.00   M    2      2       2 2      90.00   M    7      2       3 3      72.00   F    1      2       3 4      54.00   M    0      2       2 5      51.00   F    4      2       2 6      65.00   M    0      2       3 7      56.00   M    0      2       2 8      39.00   M    1      2       2 9      66.00   F    0      2       3 10     53.00   F   10      2       2 11     62.00   M    0      2       3 12     55.00   M    2      2       2 13     70.00   M    0      2       3 14     46.00   M    1      2       2 15     60.00   F    5      2       3 16     90.00   M    0      2       3 17     45.00   M    3      2       2 18     38.00   F    1      2       2 19     70.00   M    0      2       3 20     70.00   M   13      2       3 21     47.00   F    6      2       2 22     85.00   F    5      2       3 23     81.00   M    3      2       3 24     32.00   M    0      2       2 25     67.00   M    1      2       3 26     40.00   M    1      2       2 27     85.00   F    5      2       3 28     59.00   F   14      2       2 29     54.00   F    6      2       2 30     59.00   M    3      2       2 31     73.00   M    2      2       3 32     78.00   M    1      2       3 33     55.00   M    1      2       2 34     43.00   M    0      2       2 35     28.00   M    1      2       2 36     56.00   M    1      2       2 37     53.00   M    0      2       2 38     60.00   M    3      2       3 39     70.00   F    0      2       3 40     50.00   F    0      2       2 41     68.00   M    0      2       3 42     61.00   M    1      2       3 43     68.00   F    2      2       3 44     87.00   M    0      2       3 45     80.00   F    3      2       3 46     60.00   F    0      2       3 47     38.00   M    3      2       2 48     43.00   F    7      2       2 49     42.00   M    4      2       2 50     75.00   F    2      2       3 51     57.00   M    0      2       2 52     71.00   M    0      2       3 53     55.00   M    1      2       2 54     65.00   F    0      2       3 55     45.00   M    0      2       2 56     63.00   F    3      2       3 57     66.00   F    9      2       3 58     83.00   M   16      2       3 59     65.00   M   10      2       3 60     70.00   M    8      2       3 61     72.00   M    3      2       3 62     74.00   F    5      2       3 63     95.00   F    2      2       3 64     50.00   M    1      2       2 65     75.00   M    0      2       3 66     73.00   M    2      2       3 67     65.00   M    0      2       3 68     73.00   F    0      2       3 69     71.00   F   18      2       3 70     60.00   F   12      2       3 71     66.00   M    4      2       3 72     62.00   M    7      2       3 73     55.00   M    9      2       2 74     55.00   M    6      2       2 75     48.00   F    2      2       2 76     70.00   M    1      2       3 77     76.00   F    1      2       3 78     40.00   M    0      2       2 79     65.00   M    5      2       3 80     50.00   M    1      2       2 81     57.00   M    0      2       2 82     65.00   M    1      2       3 83     31.00   M    1      2       2 84     51.00   F    2      2       2 85     67.00   M   15      2       3 86     80.00   F    1      2       3 87     60.00   M    5      2       3 88     60.00   F    4      2       3 89     59.00   M    4      2       2 90     48.00   M    1      2       2 91     60.00   M    1      2       3 92     50.00   M    1      2       2 93     70.00   M    3      2       3 94     63.00   F    0      2       3 95     40.00   M    0      2       2 96     49.00   F    1      2       2 97     60.00   F    0      2       3 98     56.00   M    2      2       2 99     31.00   M    0      2       2 100    41.00   M    1      2       2 101    56.00   M    0      2       2 102    43.00   M    0      2       2 103    42.00   F    1      2       2 104    31.00   F    1      2       2 105    75.00   F    6      2       3 106    85.00   F    7      2       3 107    83.00   M    6      2       3 108    44.00   M    6      2       2 109    63.00   M    3      2       3 110    55.00   F    2      2       2 111    45.00   M    2      2       2 112    60.00   F    6      2       3 113    63.00   F    1      2       3 114    64.00   F    0      2       3 115    50.00   M    0      2       2 116    50.00   F    1      2       2 117    52.00   M    4      2       2 118    69.00   M    0      2       3 119    32.00   M    0      2       2 120    36.00   M    0      2       2 121    56.00   M    1      2       2 122    58.00   F    2      2       2 123    58.00   F    0      2       2 124    50.00   F    1      2       2 125    50.00   M    4      2       2 126    72.00   M    0      2       3 127    72.00   M    0      2       3 128    80.00   M    3      2       3 129    63.00   M   11      2       3 130    30.00   M    1      2       2 131    66.00   F   12      2       3 132    75.00   M    0      2       3 133    43.00   M    7      2       2 134    79.00   M    8      2       3 135    76.00   M    6      2       3 136    48.00   M    4      2       2 137    75.00   F    3      2       3 138    48.00   M   13      2       2 139    74.00   M    7      2       3 140    61.00   M    1      2       3 141    50.00   M    2      2       2 142    70.00   F    2      2       3 143    65.00   M    1      2       3 144    60.00   M    0      2       3 145    36.00   M    1      2       2 146    55.00   M    0      2       2 147    45.00   M    1      2       2 148    68.00   F    5      2       3 149    31.00   M    0      2       2 150    50.00   F    8      2       2 151    58.00   M    6      2       2 152    73.00   M   14      2       3 153    56.00   M    0      2       2 154    55.00   M    0      2       2 155    63.00   M    0      2       3 156    52.00   M    6      2       2 157    54.00   M    4      2       2 158    87.00   M    3      2       3 159    60.00   M    0      2       3 160    58.00   M    0      2       2 161    65.00   F    2      2       3 162    75.00   F    5      2       3 163    73.00   F    1      2       3 164    80.00   M    8      2       3 165    25.00   F    5      2       2 166    62.00   M    1      2       3 167    70.00   F    1      2       3 168    75.00   M    1      2       3 169    46.00   F    1      2       2 170    48.00   M    0      2       2 171    80.00   F    3      2       3 172    72.00   F    1      2       3 173    66.00   M    2      2       3 174    51.00   M    0      2       2 175    84.00   M    2      2       3 176    82.00   M    6      2       3 177    29.00   M   10      2       2 178    45.00   M    0      2       2 179    57.00   M    4      2       2 180    63.00   M    1      2       3 181    68.00   M    5      2       3 182    65.00   M    4      2       3 183    66.00   F    0      2       3 184    50.00   F    1      2       2 185    52.00   F    7      2       2 186    42.00   F    3      2       2 187    60.00   F    0      2       3 188    39.00   M    0      2       2 189    56.00   M    2      2       2 190    66.00   F    3      2       3 191    53.00   M    4      2       2 192    78.00   F    3      2       3 193    62.00   M    0      2       3 194    60.00   M    1      2       3 195    68.00   F    1      2       3 196    73.00   M    1      2       3 197    47.00   M    3      2       2 198    84.00   M    1      2       3 199    32.00   M    2      2       2 200    80.00   M    0      2       3 201    60.00   F    0      2       3 202    67.00   M    0      2       3 203    65.00   F    0      2       3 204    45.00   F    2      2       2 205    75.00   M    1      2       3 206    73.00   M    1      2       3 207    55.00   M    0      2       2 208    24.00   F    0      2       2 209    70.00   F    2      2       3 210    50.00   M    0      2       2 211    70.00   F    1      2       3 212    31.00   M    1      2       2 213    57.00   M   14      2       2 214    70.00   M    4      2       3 215    55.00   F   17      2       2 216    50.00   F    9      2       2 217    64.00   M    5      2       3 218    58.00   M    0      2       2 219    73.00   M    0      2       3 220    95.00   M    2      2       3 221    48.00   M    1      2       2 222    63.00   M    3      2       3 223    64.00   F   27      2       3 224    54.00   M    8      2       2 225    48.00   M    1      2       2 226    71.00   M    8      2       3 227    62.00   M    0      2       3 228    68.00   M    3      2       3 229    58.00   F    2      2       2 230    77.00   M    1      2       3 231    69.00   M    1      2       3 232    64.00   M    0      2       3 233    50.00   F    5      2       2 234    53.00   M    1      2       2 235    48.00   M    0      2       2 236    78.00   M    0      2       3 237    67.00   F    4      2       3 238    66.00   M    0      2       3 239    70.00   M    0      2       3 240    25.00   F    2      2       2 241    45.00   M    0      2       2 242    54.00   M    2      2       2 243    63.00   M    1      2       3 244    45.00   M    1      2       2 245    60.00   F    0      2       3 246    65.00   M    6      2       3 247    32.00   F    1      2       2 248    59.00   M    0      2       2 249    60.00   M    0      2       3 250    65.00   M    0      2       3 251    44.00   M   11      2       2 252    58.00   M   12      2       2 253    54.00   M    7      2       2 254    79.00   M    4      2       3 255    62.00   M    6      2       3 256    59.00   F    7      2       2 257    59.00   M   12      2       2 258    74.00   M    7      2       3 259    68.00   M    7      2       3 260    73.00   M    3      2       3 261    52.00   M    6      2       2 262    49.00   M    4      2       2 263    60.00   F    3      2       3 264    50.00   M    3      2       2 265    64.00   M    2      2       3 266    78.00   M    1      2       3 267    60.00   M    0      2       3 268    80.00   M    0      2       3 269    90.00   F    0      2       3 270    75.00   M    0      2       3 271    70.00   M    0      2       3 272    22.00   M    1      2       2 273    65.00   F    0      2       3 274    65.00   F    1      2       3 275    40.00   M    0      2       2 276    46.00   M    2      2       2 277    75.00   M    2      2       3 278    38.00   F    0      2       2 279    58.00   M    0      2       2 280    30.00   M    0      2       2 281    80.00   M    2      2       3 282    48.00   M    2      2       2 283    68.00   M    1      2       3 284    75.00   F    2      2       3 285    47.00   M    1      2       2 286    52.00   M    0      2       2 287    55.00   F    0      2       2 288    55.00   M    1      2       2 289    71.00   M    0      2       3 290    40.00   M    2      2       2 291    68.00   F    1      2       3 292    70.00   M    0      2       3 293    70.00   F    0      2       3 294    65.00   M    1      2       3 295    90.00   F   16      2       3 296    42.00   M    4      2       2 297    73.00   M   11      2       3 298    56.00   F    6      2       2 299    37.00   M   10      2       2 300    44.00   M    6      2       2 301    34.00   M   10      2       2 302    60.00   M    7      2       3 303    70.00   M    0      2       3 304    67.00   F   12      2       3 305    60.00   M    5      2       3 306    68.00   M    2      2       3 307    65.00   M    1      2       3 308    55.00   M    8      2       2 309    58.00   F    8      2       2 310    58.00   F   11      2       2 311    70.00   M    5      2       3 312    36.00   M    6      2       2 313    14.00   M    2      2       1 314    50.00   F   10      2       2 315    67.00   M    7      2       3 316    85.00   M    8      2       3 317    65.00   M    1      2       3 318    58.00   M    0      2       2 319    61.00   F    1      2       3 320    29.00   M    4      2       2 321    57.00   M    7      2       2 322    60.00   F    6      2       3 323    35.00   M   13      2       2 324    69.00   M    3      2       3 325    47.00   M    2      2       2 326    34.00   M    5      2       2 327    65.00   M    6      2       3 328    65.00   M    5      2       3 329    65.00   M   13      2       3 330    56.00   M    0      2       2 331    60.00   F    3      2       3 332    70.00   F    8      2       3 333    40.00   M    6      2       2 334    50.00   M    1      2       2 335    80.00   M    3      2       3 336    54.00   M    1      2       2 337    52.00   F    0      2       2 338    48.00   F    1      2       2 339    58.00   F    0      2       2 340    55.00   F    0      2       2 341    48.00   F    1      2       2 342    85.00   F    0      2       3 343    62.00   M    0      2       3 344    65.00   M    1      2       3 345    54.00   F    1      2       2 346    70.00   M    1      2       3 347    65.00   M    0      2       3 348    45.00   F   12      2       2 349    55.00   M    6      2       2 350    88.00   M    2      2       3 351    42.00   M    0      2       2 352    65.00   M    5      2       3 353    77.00   M    3      2       3 354    67.00   M    4      2       3 355    50.00   F    3      2       2 356    78.00   F    0      2       3 357    76.00   F    3      2       3 358    64.00   M    0      2       3 359    52.00   M    0      2       2 360    53.00   M    1      2       2 361    73.00   M    1      2       3 362    54.00   M    3      2       2 363    85.00   M    2      2       3 364    53.00   M    4      2       2 365    59.00   F   15      2       2 366    75.00   M    9      2       3 367    32.00   F    2      2       2 368    70.00   F    9      2       3 369    51.00   M    0      2       2 370    37.00   M    6      2       2 371    71.00   M    4      2       3 372    51.00   M    0      2       2 373    65.00   M    5      2       3 374    65.00   M    2      2       3 375    52.00   M    0      2       2 376    45.00   M    0      2       2 377    90.00   M    1      2       3 378    72.00   M    8      2       3 379    48.00   F    1      2       2 380    65.00   M    0      2       3 381    56.00   M    0      2       2 382    69.00   F    0      2       3 383    31.00   F    1      2       2 384    51.00   M    4      2       2 385    63.00   M    4      2       3 386    55.00   F    0      2       2 387    55.00   M    0      2       2 388    48.00   M   10      2       2 389    45.00   M    3      2       2 390    47.00   M    9      2       2 391    70.00   M    1      2       3 392    42.00   M    7      2       2 393    72.00   M    1      2       3 394    62.00   M    0      2       3 395    68.00   F    0      2       3 396    55.00   F    3      2       2 397    44.00   F    4      2       2 398    66.00   M    6      2       3 399    83.00   M    6      2       3 400    75.00   M    7      2       3 401    62.00   M    8      2       3 402    58.00   F    2      2       2 403    52.00   M    3      2       2 404    40.00   M    3      2       2 405    57.00   M    4      2       2 406    61.00   M    5      2       3 407    61.00   M    4      2       3 408    72.00   M    2      2       3 409    71.00   F    1      2       3 410    55.00   M    1      2       2 411    42.00   M    1      2       2 412    60.00   M   33      2       3 413    35.00   M    1      2       2 414    52.00   M    0      2       2 415    50.00   F    1      2       2 416    57.00   M    1      2       2 417    65.00   F    0      2       3 418    72.00   M    1      2       3 419    49.00   M    1      2       2 420    48.00   M    0      2       2 421    82.00   M    0      2       3 422    65.00   M   14      2       3 423    30.00   M   14      2       2 424    66.00   M    0      2       3 425    58.00   M    2      2       2 426    58.00   F    0      2       2 427    68.00   M    2      2       3 428    63.00   F    3      2       3 429    68.00   M    0      2       3 430    76.00   M    8      2       3 431    62.00   M    5      2       3 432    65.00   F    2      2       3 433    60.00   F    0      2       3 434    83.00   M    1      2       3 435    59.00   M    1      2       2 436    59.00   F    1      2       2 437    58.00   M    0      2       2 438    57.00   F    0      2       2 439    67.00   M    0      2       3 440    54.00   M    4      2       2 441    70.00   F    1      2       3 442    56.00   F    1      2       2 443    49.00   F    0      2       2 444    73.00   M    0      2       3 445    88.00   M    0      2       3 446    54.00   M    1      2       2 447    61.00   M    0      2       3 448    65.00   M    0      2       3 449    79.00   M    1      2       3 450    51.00   F    0      2       2 451    73.00   F    5      2       3 452    40.00   M    8      2       2 453    55.00   F    1      2       2 454    80.00   M    6      2       3 455    51.00   M    4      2       2 456    75.00   M    6      2       3 457    78.00   F    5      2       3 458    84.00   M    6      2       3 459    59.00   M    2      2       2 460    68.00   M    5      2       3 461    73.00   M    1      2       3 462    65.00   M    2      2       3 463    67.00   M    0      2       3 464    55.00   M    2      2       2 465    51.00   F    1      2       2 466    65.00   M    3      2       3 467    58.00   F    0      2       2 468    31.00   M   14      2       2 469    72.00   M    4      2       3 470    65.00   F    0      2       3 471    55.00   M    0      2       2 472    45.00   F    6      2       2 473    58.00   F    1      2       2 474    47.00   M    1      2       2 475    65.00   M    0      2       3 476    54.00   M    1      2       2 477    58.00   M    1      2       2 478    66.00   M    2      2       3 479    55.00   M   11      2       2 480    62.00   M    0      2       3 481    12.00   F    1      2       1 482    46.00   F    0      2       2 483    26.00   F    0      2       2 484    57.00   F    1      2       2 485    62.00   F    5      2       3 486    70.00   M    4      2       3 487    60.00   F    0      2       3 488    68.00   M    1      2       3 489    50.00   M    0      2       2 490    71.00   M    0      2       3 491    58.00   M    1      2       2 492    46.00   M    6      2       2 493     0.00   F    0      2       1 494    70.00   M   11      2       3 495    65.00   M    3      2       3 496    17.00   F    8      2       1 497    45.00   F   13      2       2 498    64.00   M   11      2       3 499    61.00   M    7      2       3 500    59.00   F    2      2       2 501    63.00   M    0      2       3 502    36.00   M   14      2       2 503    60.00   M    3      2       3 504    49.00   F    2      2       2 505    60.00   M   11      2       3 506    45.00   M    3      2       2 507    71.00   F    2      2       3 508    42.00   F    6      2       2 509    84.00   M    0      2       3 510    57.00   M    1      2       2 511    60.00   M    9      2       3 512    33.00   M   23      2       2 513    75.00   F    1      2       3 514    50.00   F    1      2       2 515    60.00   F    6      2       3 516    45.00   F    7      2       2 517    70.00   F    0      2       3 518    60.00   M    9      2       3 519    69.00   M   13      2       3 520    82.00   M   10      2       3 521    68.00   M    0      2       3 522    49.00   M    1      2       2 523    56.00   M    4      2       2 524    33.00   F    2      2       2 525    80.00   F    3      2       3 526    74.00   M    1      2       3 527    65.00   M    2      2       3 528    48.00   M    0      2       2 529    52.00   F   11      2       2 530    75.00   F    0      2       3 531    26.00   M    3      2       2 532    57.00   M    5      2       2 533    62.00   M    7      2       3 534    65.00   M    3      2       3 535    71.00   M    1      2       3 536    56.00   M    1      2       2 537    76.00   F    1      2       3 538    49.00   F    0      2       2 539    57.00   M    1      2       2 540    79.00   M    3      2       3 541    59.00   F    0      2       2 542    54.00   F    0      2       2 543    65.00   F    0      2       3 544    48.00   M    1      2       2 545    70.00   F    8      2       3 546    72.00   M    6      2       3 547    78.00   M    4      2       3 548    53.00   M    2      2       2 549    45.00   F    1      2       2 550    52.00   M    3      2       2 551    50.00   M    2      2       2 552    42.00   M    1      2       2 553    68.00   M    0      2       3 554    58.00   M    3      2       2 555    47.00   M    0      2       2 556    55.00   F    1      2       2 557    60.00   F    1      2       3 558    58.00   F    0      2       2 559    42.00   F    0      2       2 560    60.00   M    1      2       3 561    80.00   F    0      2       3 562    57.00   M    2      2       2 563    45.00   F   10      2       2 564    55.00   F    3      2       2 565    61.00   F    1      2       3 566    52.00   M    4      2       2 567    57.00   M   11      2       2 568    35.00   M    0      2       2 569    36.00   F    1      2       2 570    50.00   F    3      2       2 571    55.00   F   17      2       2 572    48.00   M    0      2       2 573    52.00   M    2      2       2 574    36.00   M    0      2       2 575    48.00   M    0      2       2 576    59.00   M    1      2       2 577    46.00   F    8      2       2 578    45.00   M    0      2       2 579    72.00   F    1      2       3 580    84.00   M    2      2       3 581    29.00   M   10      2       2 582    45.00   M    0      2       2 583    57.00   M    4      2       2 584    65.00   M    3      2       3 585    50.00   F    1      2       2 586    52.00   F    7      2       2 587    42.00   F    3      2       2 588    39.00   M    0      2       2 589    40.00   M    0      2       2 590    66.00   F    3      2       3 591    78.00   F    5      2       3 592    62.00   M    0      2       3 593    55.00   F    1      2       2 594    36.00   F    1      2       2 595    80.00   F    3      2       3 596    63.00   M    2      2       3 597    75.00   F    2      2       3 598    65.00   F    9      2       3 599    48.00   F    1      2       2 600    60.00   F    0      2       3 601    90.00   F    6      2       3 602    80.00   M    6      2       3 603    77.00   M    4      2       3 604    70.00   M    4      2       3 605    62.00   M    2      2       3 606    59.00   M    0      2       2 607    65.00   M    0      2       3 608    63.00   M    1      2       3 609    79.00   M    3      2       3 610    50.00   M    0      2       2 611    65.00   M    4      2       3 612    50.00   M    0      2       2 613    63.00   M    0      2       3 614    52.00   M    0      2       2 615    70.00   M    0      2       3 616    45.00   M    4      2       2 617    20.00   M    3      2       2 618    86.00   F    0      2       3 619    85.00   F    6      2       3 620    39.00   M    6      2       2 621    74.00   M    2      2       3 622    70.00   M    0      2       3 623    51.00   M    1      2       2 624    68.00   M    0      2       3 625    76.00   M    1      2       3 626    52.00   M    2      2       2 627    72.00   M    0      2       3 628    45.00   M    2      2       2 629    45.00   M    1      2       2 630    82.00   F    0      2       3 631    58.00   F    0      2       2 632    69.00   M    2      2       3 633    68.00   M    2      2       3 634    63.00   M    0      2       3 635    38.00   M    2      2       2 636    45.00   F   16      2       2 637    59.00   M    1      2       2 638    47.00   M    0      2       2 639    40.00   M    1      2       2 640    29.00   F    0      2       2 641    50.00   F    0      2       2 642    59.00   M    2      2       2 643    74.00   M    1      2       3 644    58.00   F    1      2       2 645    47.00   M    1      2       2 646    67.00   M    1      2       3 647    73.00   M    2      2       3 648    42.00   F    2      2       2 649    23.00   F    2      2       2 650    55.00   M    0      2       2 651    35.00   M    1      2       2 652    55.00   M    6      2       2 653    64.00   M    7      2       3 654    76.00   F    0      2       3 655    84.00   M    1      2       3 656    54.00   M    3      2       2 657    48.00   M    0      2       2 658    60.00   M    5      2       3 659    60.00   F    0      2       3 660    75.00   F    0      2       3 661    68.00   M    1      2       3 662    53.00   M    2      2       2 663    85.00   M    0      2       3 664    60.00   M    3      2       3 665    55.00   F    0      2       2 666    67.00   F    0      2       3 667    49.00   F    0      2       2 668    68.00   F    2      2       3 669    80.00   F    1      2       3 670    70.00   M    1      2       3 671    35.00   F    1      2       2 672    40.00   F    2      2       2 673    70.00   F    0      2       3 674    49.00   M    0      2       2 675    48.00   M    0      2       2 676    48.00   F    1      2       2 677    42.00   F    3      2       2 678    75.00   F    0      2       3 679    72.00   M    2      2       3 680    52.00   M    4      2       2 681    60.00   M    6      2       3 682    61.00   M    5      2       3 683    50.00   M    0      2       2 684    70.00   F    2      2       3 685    49.00   M    0      2       2 686    38.00   M    0      2       2 687    34.00   F    1      2       2 688    50.00   M    1      2       2 689    41.00   M    1      2       2 690    76.00   M    1      2       3 691    40.00   M    0      2       2 692    55.00   M    1      2       2 693    57.00   M    2      2       2 694    53.00   M    7      2       2 695    61.00   M    0      2       3 696    60.00   M    0      2       3 697    90.00   M    3      2       3 698    41.00   M    1      2       2 699    68.00   M    6      2       3 700    40.00   F    0      2       2 701    50.00   F    2      2       2 702    70.00   M    2      2       3 703    75.00   F    3      2       3 704    70.00   M   22      2       3 705    49.00   F    4      2       2 706    70.00   M    0      2       3 707    56.00   F    2      2       2 708    56.00   M    1      2       2 709    52.00   M    2      2       2 710    76.00   F    0      2       3 711    51.00   F    2      2       2 712    65.00   M    0      2       3 713    85.00   F    3      2       3 714    48.00   M    3      2       2 715    68.00   F    0      2       3 716    60.00   M    0      2       3 717    63.00   F    1      2       3 718    60.00   M    0      2       3 719    71.00   M    0      2       3 720    69.00   M    0      2       3 721    70.00   F    1      2       3 722    58.00   M    0      2       2 723    46.00   M    9      2       2 724    78.00   M    4      2       3 725    56.00   M    4      2       2 726    60.00   F   15      2       3 727    41.00   M    1      2       2 728    66.00   M    6      2       3 729    60.00   F    0      2       3 730    70.00   M   11      2       3 731    52.00   F   13      2       2 732    48.00   M    6      2       2 733    85.00   F    5      2       3 734    55.00   F    9      2       2 735    77.00   M    6      2       3 736    78.00   M    9      2       3 737    51.00   M    6      2       2 738    65.00   M    0      2       3 739    48.00   F    0      2       2 740    62.00   F    1      2       3 741    40.00   M    2      2       2 742    78.00   M    2      2       3 743    61.00   M    1      2       3 744    62.00   M    0      2       3 745    51.00   F    0      2       2 746    26.00   M    0      2       2 747    51.00   M    0      2       2 748    60.00   M    1      2       3 749    81.00   F    0      2       3 750    60.00   M    0      2       3 751    55.00   F    0      2       2 752    63.00   M    3      2       3 753    28.00   M    0      2       2 754    78.00   M    0      2       3 755    60.00   F    1      2       3 756    65.00   M    2      2       3 757    14.00   M    4      2       1 758    70.00   M    6      2       3 759    70.00   F    1      2       3 760    46.00   M    2      2       2 761    47.00   M    1      2       2 762    41.00   M    0      2       2 763    72.00   M    2      2       3 764    65.00   M    0      2       3 765    76.00   M    0      2       3 766    50.00   M    4      2       2 767    80.00   F    2      2       3 768    84.00   M    0      2       3 769    68.00   F    1      2       3 770    54.00   M    0      2       2 771    39.00   M    0      2       2 772    75.00   M    3      2       3 773    48.00   F    1      2       2 774    41.00   M    3      2       2 775    61.00   M    6      2       3 776    27.00   F    0      2       2 777    55.00   F    4      2       2 778    75.00   F    0      2       3 779    56.00   F    0      2       2 780    59.00   M    0      2       2 781    63.00   M    0      2       3 782    45.00   F    1      2       2 783    54.00   F    9      2       2 784    47.00   M    1      2       2 785    38.00   M    0      2       2 786    60.00   M    0      2       3 787    43.00   M    1      2       2 788    62.00   F    0      2       3 789    72.00   F    0      2       3 790    50.00   F    0      2       2 791    38.00   M    0      2       2 792    45.00   M    0      2       2 793    65.00   M    4      2       3 794    49.00   M    0      2       2 795    52.00   F    0      2       2 796    52.00   M    0      2       2 797    75.00   M    1      2       3 798    74.00   F    1      2       3 799    53.00   M    1      2       2 800    38.00   F    0      2       2 801    75.00   F    0      2       3 802    55.00   F    0      2       2 803    46.00   F    1      2       2 804    60.00   M    3      2       3 805    58.00   F    1      2       2 806    68.00   M    1      2       3 807    51.00   M    4      2       2 808    61.00   F    0      2       3 809    45.00   M    0      2       2 810    59.00   M    0      2       2 811    41.00   M    0      2       2 812    56.00   M    0      2       2 813    83.00   F    1      2       3 814    55.00   F    0      2       2 815    66.00   M   11      2       3 816    56.00   M    2      2       2 817    55.00   M    4      2       2 818    55.00   M    1      2       2 819    45.00   M    0      2       2 820    67.00   M    1      2       3 821    27.00   M    0      2       2 822    57.00   M    0      2       2 823    20.00   M    1      2       2 824    61.00   M    0      2       3 825    70.00   M    0      2       3 826    55.00   M    1      2       2 827    56.00   F    1      2       2 828    65.00   M    0      2       3 829    69.00   M    0      2       3 830    50.00   F    1      2       2 831    55.00   M    1      2       2 832    68.00   M    5      2       3 833    58.00   M    2      2       2 834    40.00   F    3      2       2 835    75.00   F    1      2       3 836    58.00   M    2      2       2 837    41.00   M    0      2       2 838    68.00   M    1      2       3 839    70.00   M    0      2       3 840    45.00   M    1      2       2 841    51.00   M    5      2       2 842    72.00   F    1      2       3 843    38.00   M    2      2       2 844    56.00   M    0      2       2 845    34.00   M    0      2       2 846    81.00   M    0      2       3 847    70.00   M    3      2       3 848    45.00   M    0      2       2 849    54.00   M    0      2       2 850    66.00   F    2      2       3 851    57.00   M    0      2       2 852    65.00   M    7      2       3 853    35.00   M    1      2       2 854    50.00   F    0      2       2 855    54.00   M    0      2       2 856    59.00   M    1      2       2 857    82.00   M    3      2       3 858    52.00   M    0      2       2 859    53.00   M    0      2       2 860    60.00   M    0      2       3 861    61.00   M    0      2       3 862    23.00   F    0      2       2 863    37.00   M    0      2       2 864    73.00   F    0      2       3 865    71.00   M    0      2       3 866    68.00   M    0      2       3 867    45.00   M    0      2       2 868    58.00   F    0      2       2 869    32.00   F    1      2       2 870    61.00   M    5      2       3 871    60.00   M    0      2       3 872    38.00   M    3      2       2 873    37.00   M    0      2       2 874    53.00   M    1      2       2 875    65.00   F    1      2       3 876    68.00   F   11      2       3 877    48.00   M    0      2       2 878    63.00   M    0      2       3 879    73.00   M    9      2       3 880    65.00   M    3      2       3 881    66.00   M    5      2       3 882    35.00   M    0      2       2 883    70.00   M    5      2       3 884    65.00   M    4      2       3 885    37.00   F    1      2       2 886    75.00   M    3      2       3 887    65.00   F    3      2       3 888    55.00   M    0      2       2 889    65.00   F    2      2       3 890    48.00   F    3      2       2 891    70.00   M    2      2       3 892    28.00   M    2      2       2 893    56.00   M    1      2       2 894    31.00   M   34      2       2 895    72.00   F    2      2       3 896    43.00   F    5      2       2 897    30.00   M    0      2       2 898    92.00   F    2      2       3 899    66.00   M    5      2       3 900    55.00   F    1      2       2 901    69.00   F    3      2       3 902    58.00   F    3      2       2 903    60.00   F    6      2       3 904    66.00   M    0      2       3 905    55.00   M    2      2       2 906    60.00   M    1      2       3 907    60.00   F    0      2       3 908    65.00   F    0      2       3 909    53.00   M    0      2       2 910    49.00   M    3      2       2 911    45.00   M    0      2       2 912    73.00   M    0      2       3 913    45.00   M    2      2       2 914    50.00   M   13      2       2 915    58.00   M    0      2       2 916    58.00   M    0      2       2 917    40.00   M    0      2       2 918    65.00   M    1      2       3 919    63.00   M   12      2       3 920    61.00   M    0      2       3 921    42.00   M    0      2       2 922    55.00   M    1      2       2 923    55.00   M    0      2       2 924    67.00   M    1      2       3 925    64.00   M    1      2       3 926    50.00   M    0      2       2 927    45.00   M    0      2       2 928    53.00   F    2      2       2 929    50.00   F    2      2       2 930    75.00   M    0      2       3 931    59.00   M    0      2       2 932    51.00   F    4      2       2 933    64.00   M    4      2       3 934    48.00   M    0      2       2 935    37.00   M    0      2       2 936    60.00   M    2      2       3 937    70.00   F   11      2       3 938    62.00   F    3      2       3 939    51.00   M    1      2       2 940    50.00   F    2      2       2 941    73.00   M    3      2       3 942    35.00   F    0      2       2 943    76.00   M    2      2       3 944    40.00   M    1      2       2 945    53.00   M    4      2       2 946    46.00   M   11      2       2 947    61.00   M    4      2       3 948    65.00   M    0      2       3 949    60.00   M    0      2       3 950    55.00   M    0      2       2 951    53.00   M    6      2       2 952    38.00   F    2      2       2 953    84.00   M   14      2       3 954    46.00   M    7      2       2 955    60.00   M    9      2       3 956    85.00   M    5      2       3 957    80.00   M    8      2       3 958    48.00   M    5      2       2 959    38.00   M    0      2       2 960    68.00   F    6      2       3 961    62.00   M    5      2       3 962    75.00   M    6      2       3 963    80.00   F    2      2       3 964    78.00   M    1      2       3 965    61.00   M    0      2       3 966    49.00   M    3      2       2 967    60.00   M    1      2       3 968    65.00   M    0      2       3 969    65.00   M    1      2       3 970    60.00   M    6      2       3 971    52.00   M    7      2       2 972    48.00   M    0      2       2 973    24.00   M    1      2       2 974    50.00   F    0      2       2 975    22.00   M    0      2       2 976    58.00   M    0      2       2 977    60.00   F    2      2       3 978    58.00   M    0      2       2 979    54.00   M    1      2       2 980    78.00   M    0      2       3 981    38.00   F    0      2       2 982    54.00   M    0      2       2 983    65.00   F    0      2       3 984    87.00   F    0      2       3 985    75.00   F    0      2       3 986    60.00   F    0      2       3 987    23.00   F    1      2       2 988    85.00   M    0      2       3 989    69.00   M    4      2       3 990    72.00   M   13      2       3 991    85.00   M    2      2       3 992    57.00   F    1      2       2 993    78.00   M    0      2       3 994    68.00   M    0      2       3 995    63.00   M    2      2       3 996    67.00   M    7      2       3 997    64.00   M    0      2       3 998    77.00   M    0      2       3 999    53.00   M    2      2       2 1000   66.00   M    2      2       3 1001   82.00   M    3      2       3 1002   63.00   M    0      2       3 1003   40.00   M    0      2       2 1004   55.00   F    0      2       2 1005   67.00   M    0      2       3 1006   49.00   M    0      2       2 1007   59.00   F    0      2       2 1008   75.00   F    1      2       3 1009   68.00   M    1      2       3 1010   72.00   M    0      2       3 1011   77.00   M    0      2       3 1012   47.00   M    1      2       2 1013   60.00   M    0      2       3 1014   72.00   M    2      2       3 1015   55.00   M    6      2       2 1016   43.00   F    1      2       2 1017   62.00   M    0      2       3 1018   55.00   M    4      2       2 1019   60.00   M    3      2       3 1020   45.00   F    3      2       2 1021   70.00   M    3      2       3 1022   90.00   F    0      2       3 1023   49.00   M    1      2       2 1024   55.00   F    2      2       2 1025   52.00   F    1      2       2 1026   63.00   M    1      2       3 1027   47.00   M    1      2       2 1028   69.00   M    1      2       3 1029   60.00   F    1      2       3 1030   60.00   M    0      2       3 1031   82.00   M    0      2       3 1032   61.00   M    2      2       3 1033   65.00   M    2      2       3 1034   65.00   M    0      2       3 1035   59.00   M   11      2       2 1036   26.00   M   10      2       2 1037   65.00   M    7      2       3 1038   69.00   M   13      2       3 1039   55.00   F    0      2       2 1040   79.00   M    3      2       3 1041   75.00   F    5      2       3 1042   51.00   M    6      2       2 1043   22.00   M    0      2       2 1044   50.00   M    0      2       2 1045   60.00   M    3      2       3 1046   67.00   F    1      2       3 1047   69.00   M    0      2       3 1048   55.00   F    0      2       2 1049   74.00   M    3      2       3 1050   65.00   M    0      2       3 1051   60.00   M    1      2       3 1052   65.00   M    0      2       3 1053   60.00   M    4      2       3 1054   55.00   M    3      2       2 1055   65.00   M    0      2       3 1056   48.00   M    0      2       2 1057   60.00   F    0      2       3 1058   38.00   M    0      2       2 1059   76.00   F    2      2       3 1060   62.00   F    5      2       3 1061   62.00   F    2      2       3 1062   63.00   M    0      2       3 1063   59.00   M    2      2       2 1064   52.00   M    2      2       2 1065   72.00   M    0      2       3 1066   58.00   M    1      2       2 1067   77.00   M    1      2       3 1068   59.00   M    1      2       2 1069   61.00   M    1      2       3 1070   61.00   F    1      2       3 1071   51.00   M   12      2       2 1072   47.00   F    0      2       2 1073   20.00   F    0      2       2 1074   53.00   M    3      2       2 1075   50.00   M    0      2       2 1076   70.00   M    3      2       3 1077   51.00   M    0      2       2 1078   73.00   M    0      2       3 1079   75.00   M    1      2       3 1080   86.00   F    2      2       3 1081   60.00   M    0      2       3 1082   73.00   F    1      2       3 1083   62.00   M    8      2       3 1084   48.00   F    3      2       2 1085   74.00   M    0      2       3 1086   45.00   M    0      2       2 1087   55.00   F    2      2       2 1088   35.00   F    2      2       2 1089   50.00   F    0      2       2 1090   44.00   M    0      2       2 1091   72.00   M    2      2       3 1092   47.00   M    7      2       2 1093   55.00   M    0      2       2 1094   47.00   F    1      2       2 1095   65.00   M    2      2       3 1096   72.00   M    1      2       3 1097   52.00   M    4      2       2 1098   45.00   F    1      2       2 1099   43.00   M    7      2       2 1100   62.00   M    3      2       3 1101   30.00   F    2      2       2 1102   66.00   F    2      2       3 1103   40.00   F    6      2       2 1104   20.00   F   14      2       2 1105   62.00   M    1      2       3 1106   70.00   M    2      2       3 1107   70.00   F    1      2       3 1108   39.00   M    4      2       2 1109   82.00   M    4      2       3 1110   48.00   F    1      2       2 1111   43.00   M    1      2       2 1112   60.00   F    1      2       3 1113   45.00   F    4      2       2 1114   58.00   F    0      2       2 1115   72.00   M    0      2       3 1116   70.00   F    1      2       3 1117   70.00   F    0      2       3 1118   45.00   F    1      2       2 1119   65.00   M    0      2       3 1120   23.00   F    4      2       2 1121   58.00   M    1      2       2 1122   67.00   M    9      2       3 1123   80.00   F    9      2       3 1124   77.00   M    4      2       3 1125   54.00   M    5      2       2 1126   82.00   M    3      2       3 1127   59.00   M    0      2       2 1128   82.00   M    1      2       3 1129   57.00   M    0      2       2 1130   55.00   F    0      2       2 1131   69.00   F    3      2       3 1132   65.00   M    0      2       3 1133   61.00   M    0      2       3 1134   69.00   M    0      2       3 1135   73.00   F    0      2       3 1136   72.00   M   10      2       3 1137   68.00   F    6      2       3 1138   57.00   M   16      2       2 1139   55.00   M   13      2       2 1140   71.00   M    4      2       3 1141   55.00   M   16      2       2 1142   48.00   M    4      2       2 1143   55.00   M    0      2       2 1144   39.00   M    0      2       2 1145   44.00   M    3      2       2 1146   68.00   F    0      2       3 1147   59.00   M    2      2       2 1148   57.00   F    7      2       2 1149   40.00   M    0      2       2 1150   57.00   M    5      2       2 1151   55.00   M    5      2       2 1152   59.00   M    4      2       2 1153   52.00   M    1      2       2 1154   75.00   F    5      2       3 1155   53.00   M    5      2       2 1156   56.00   F    4      2       2 1157   61.00   M   12      2       3 1158   84.00   M    1      2       3 1159   87.00   F    2      2       3 1160   48.00   F    4      2       2 1161   39.00   M    1      2       2 1162   48.00   M    1      2       2 1163   41.00   M    4      2       2 1164   62.00   M    5      2       3 1165   67.00   M    0      2       3 1166   53.00   F    4      2       2 1167   62.00   M    1      2       3 1168   60.00   M    0      2       3 1169   35.00   M    0      2       2 1170   67.00   M   13      2       3 1171   74.00   M    3      2       3 1172   60.00   M    2      2       3 1173   54.00   M    1      2       2 1174   51.00   F    1      2       2 1175   60.00   F    0      2       3 1176   53.00   M    0      2       2 1177   57.00   F    0      2       2 1178   86.00   F   16      2       3 1179   85.00   M    3      2       3 1180   54.00   F    1      2       2 1181   43.00   M    0      2       2 1182   54.00   M    2      2       2 1183   52.00   F    4      2       2 1184   50.00   F    0      2       2 1185   60.00   F    2      2       3 1186   67.00   M    3      2       3 1187   73.00   M    3      2       3 1188   70.00   F    1      2       3 1189   90.00   M    1      2       3 1190   52.00   F    0      2       2 1191   72.00   M    2      2       3 1192   62.00   M    1      2       3 1193   25.00   M    1      2       2 1194   52.00   M    0      2       2 1195   55.00   F    3      2       2 1196   60.00   M    4      2       3 1197   60.00   F    1      2       3 1198   45.00   M    4      2       2 1199   48.00   M    0      2       2 1200   78.00   M    0      2       3 1201   47.00   F    0      2       2 1202   40.00   M    2      2       2 1203   50.00   F    0      2       2 1204   60.00   M    0      2       3 1205   65.00   F    6      2       3 1206   52.00   M    6      2       2 1207   60.00   M    0      2       3 1208   70.00   F    9      2       3 1209   73.00   M    1      2       3 1210   45.00   M    0      2       2 1211   61.00   M    0      2       3 1212   65.00   F    1      2       3 1213   80.00   F    6      2       3 1214   79.00   M    8      2       3 1215   81.00   F    0      2       3 1216   80.00   F    0      2       3 1217   57.00   F    4      2       2 1218   65.00   M   11      2       3 1219   77.00   F    9      2       3 1220   40.00   M    3      2       2 1221   79.00   M    4      2       3 1222   61.00   F   14      2       3 1223   43.00   M    1      2       2 1224   50.00   F    3      2       2 1225   78.00   M    1      2       3 1226   78.00   M    6      2       3 1227   79.00   F    5      2       3 1228   65.00   M    1      2       3 1229   65.00   M    1      2       3 1230   54.00   M    0      2       2 1231   48.00   M    3      2       2 1232   53.00   M    4      2       2 1233   38.00   F    3      2       2 1234   70.00   M    0      2       3 1235   35.00   F    2      2       2 1236   49.00   F    2      2       2 1237   62.00   M    2      2       3 1238   62.00   M    1      2       3 1239   51.00   M    2      2       2 1240   21.00   F    0      2       2 1241   40.00   M    0      2       2 1242   64.00   M    0      2       3 1243   54.00   M    0      2       2 1244   22.00   F    0      2       2 1245   57.00   F    0      2       2 1246   52.00   M    0      2       2 1247   34.00   F    0      2       2 1248   70.00   F    4      2       3 1249   56.00   F    0      2       2 1250   72.00   M    1      2       3 1251   45.00   M    1      2       2 1252   60.00   F    0      2       3 1253   58.00   M    1      2       2 1254   67.00   F    0      2       3 1255   55.00   F    0      2       2 1256   72.00   M    1      2       3 1257   64.00   F    9      2       3 1258   58.00   M    3      2       2 1259   47.00   M    4      2       2 1260   56.00   M    0      2       2 1261   45.00   F    0      2       2 1262   52.00   M    0      2       2 1263   60.00   M    0      2       3 1264   52.00   F    0      2       2 1265   52.00   M    0      2       2 1266   58.00   F    1      2       2 1267   70.00   M    1      2       3 1268   70.00   M    0      2       3 1269   59.00   F    0      2       2 1270   64.00   M    1      2       3 1271   62.00   M   10      2       3 1272   58.00   M    1      2       2 1273   78.00   F    1      2       3 1274   75.00   F    0      2       3 1275   42.00   M    2      2       2 1276   68.00   M    0      2       3 1277   56.00   F    0      2       2 1278   42.00   M    0      2       2 1279   44.00   F    1      2       2 1280   52.00   M    0      2       2 1281   63.00   M    0      2       3 1282   70.00   M    2      2       3 1283   70.00   F    0      2       3 1284   65.00   M    2      2       3 1285   37.00   M    0      2       2 1286   51.00   M    0      2       2 1287   63.00   M    1      2       3 1288   39.00   F    0      2       2 1289   50.00   F    1      2       2 1290   48.00   M    4      2       2 1291   45.00   M   12      2       2 1292   58.00   M   12      2       2 1293   48.00   M    1      2       2 1294   65.00   M   12      2       3 1295   86.00   M    8      2       3 1296   70.00   F    2      2       3 1297   38.00   F    3      2       2 1298   50.00   M    5      2       2 1299   48.00   F    2      2       2 1300   70.00   F    2      2       3 1301   55.00   M    4      2       2 1302   76.00   M    1      2       3 1303   66.00   M    0      2       3 1304   51.00   F    0      2       2 1305   50.00   M    0      2       2 1306   60.00   M    1      2       3 1307   59.00   F    4      2       2 1308   55.00   M    2      2       2 1309   30.00   F    2      2       2 1310    0.00   M    1      2       1 1311   68.00   F    3      2       3 1312   65.00   M    2      2       3 1313   34.00   M    4      2       2 1314   48.00   M    1      2       2 1315   81.00   M    3      2       3 1316   30.00   F    1      2       2 1317   42.00   F    0      2       2 1318   70.00   F    0      2       3 1319   52.00   F    0      2       2 1320   88.00   F    1      2       3 1321   70.00   F    1      2       3 1322   40.00   M    0      2       2 1323   70.00   M    0      2       3 1324   75.00   M    3      2       3 1325   68.00   M    0      2       3 1326   91.00   F    2      2       3 1327   51.00   M    0      2       2 1328   68.00   M    3      2       3 1329   71.00   M    1      2       3 1330   49.00   M    0      2       2 1331   65.00   M    5      2       3 1332   53.00   F    4      2       2 1333   69.00   M    0      2       3 1334   68.00   M    0      2       3 1335   58.00   M    0      2       2 1336   62.00   M    0      2       3 1337   53.00   M    0      2       2 1338   50.00   M    0      2       2 1339   40.00   M    1      2       2 1340   70.00   F    0      2       3 1341   29.00   M    0      2       2 1342   62.00   M    1      2       3 1343   65.00   F    0      2       3 1344   60.00   F    0      2       3 1345   35.00   M    0      2       2 1346   70.00   M   11      2       3 1347   78.00   F   10      2       3 1348   68.00   M    6      2       3 1349   79.00   F   13      2       3 1350   68.00   M    9      2       3 1351   59.00   M   10      2       2 1352   74.00   M    9      2       3 1353   58.00   F   12      2       2 1354   67.00   M    8      2       3 1355   68.00   M    1      2       3 1356   65.00   M    1      2       3 1357   70.00   M    1      2       3 1358   69.00   M    4      2       3 1359   57.00   F    5      2       2 1360   75.00   M    5      2       3 1361   82.00   M    2      2       3 1362   48.00   F    0      2       2 1363   58.00   M    2      2       2 1364   56.00   F    2      2       2 1365   54.00   M    1      2       2 1366   46.00   M    0      2       2 1367   36.00   M    1      2       2 1368   76.00   M    2      2       3 1369   76.00   F    2      2       3 1370   47.00   M    1      2       2 1371   80.00   M    0      2       3 1372   55.00   M    0      2       2 1373   65.00   F    2      2       3 1374   48.00   F    0      2       2 1375   87.00   M    4      2       3 1376   85.00   M    0      2       3 1377   58.00   F    0      2       2 1378   57.00   M    0      2       2 1379   30.00   F    0      2       2 1380   58.00   F    3      2       2 1381   28.00   M    0      2       2 1382   50.00   M    1      2       2 1383   62.00   M    0      2       3 1384   69.00   M    0      2       3 1385   57.00   M    0      2       2 1386   53.00   M    0      2       2 1387   74.00   M    0      2       3 1388   65.00   M   24      2       3 1389   55.00   F    1      2       2 1390   74.00   F    0      2       3 1391   28.00   F    1      2       2 1392   55.00   M    0      2       2 1393   54.00   M    0      2       2 1394   52.00   M    0      2       2 1395   72.00   M    0      2       3 1396   20.00   F    0      2       2 1397   80.00   M    0      2       3 1398   42.00   F    0      2       2 1399   60.00   M    0      2       3 1400   65.00   M    0      2       3 1401   70.00   F    0      2       3 1402   45.00   F    2      2       2 1403   56.00   M    1      2       2 1404   47.00   M    7      2       2 1405   45.00   M    0      2       2 1406   52.00   M    0      2       2 1407   30.00   M    0      2       2 1408   49.00   F    0      2       2 1409   54.00   F    2      2       2 1410   53.00   M    5      2       2 1411   53.00   M   13      2       2 1412   49.00   M    0      2       2 1413   45.00   F    0      2       2 1414   70.00   M    0      2       3 1415   54.00   M    6      2       2 1416   47.00   M    1      2       2 1417   68.00   M    0      2       3 1418   71.00   M    0      2       3 1419   57.00   M    1      2       2 1420   68.00   M    2      2       3 1421   65.00   M    1      2       3 1422   59.00   F    0      2       2 1423   62.00   M    7      2       3 1424   50.00   M    3      2       2 1425   40.00   M    2      2       2 1426   45.00   M    2      2       2 1427   65.00   F    6      2       3 1428   65.00   M    3      2       3 1429   71.00   M    1      2       3 1430   68.00   F    3      2       3 1431   74.00   M    3      2       3 1432   68.00   M    6      2       3 1433   69.00   M    5      2       3 1434   58.00   F    5      2       2 1435   54.00   M    7      2       2 1436   55.00   M    7      2       2 1437   39.00   F    3      2       2 1438   60.00   M    1      2       3 1439   47.00   M    0      2       2 1440   70.00   M    2      2       3 1441   75.00   M    2      2       3 1442   55.00   M    2      2       2 1443   63.00   M    0      2       3 1444   61.00   M    0      2       3 1445   84.00   F    4      2       3 1446   61.00   M    0      2       3 1447   48.00   M    5      2       2 1448   55.00   F    8      2       2 1449   52.00   F    0      2       2 1450   49.00   M    3      2       2 1451   68.00   F    5      2       3 1452   55.00   M    6      2       2 1453   50.00   F    5      2       2 1454   62.00   M    8      2       3 1455   71.00   F    4      2       3 1456   60.00   M    0      2       3 1457   60.00   M    1      2       3 1458   52.00   M    3      2       2 1459   55.00   F    7      2       2 1460   71.00   M    6      2       3 1461   61.00   M    0      2       3 1462   78.00   M    5      2       3 1463   62.00   M    1      2       3 1464   55.00   M    4      2       2 1465   51.00   F    3      2       2 1466   94.00   F    1      2       3 1467   75.00   F    2      2       3 1468   52.00   F    1      2       2 1469   61.00   M    1      2       3 1470   70.00   M   13      2       3 1471   60.00   F    0      2       3 1472   71.00   M    0      2       3 1473   54.00   M    0      2       2 1474   35.00   F    3      2       2 1475   50.00   F    0      2       2 1476   68.00   M    0      2       3 1477   36.00   F    0      2       2 1478   28.00   M    1      2       2 1479   42.00   M    0      2       2 1480   65.00   F    0      2       3 1481   55.00   M    5      2       2 1482   70.00   M    0      2       3 1483   64.00   M    1      2       3 1484   46.00   F    0      2       2 1485   70.00   M    0      2       3 1486   48.00   M    1      2       2 1487   90.00   M    0      2       3 1488   54.00   M    1      2       2 1489   72.00   F    0      2       3 1490   60.00   M    0      2       3 1491   70.00   F    1      2       3 1492   42.00   M    2      2       2 1493   58.00   M    2      2       2 1494   72.00   M    0      2       3 1495   65.00   M   16      2       3 1496   70.00   F    2      2       3 1497   70.00   M    2      2       3 1498   61.00   M   11      2       3 1499   85.00   F   13      2       3 1500   65.00   M   10      2       3 1501   51.00   F    8      2       2 1502   65.00   M    0      2       3 1503   59.00   M    0      2       2 1504   58.00   F   11      2       2 1505   64.00   M    3      2       3 1506   65.00   F    9      2       3 1507   61.00   M    0      2       3 1508   66.00   F    0      2       3 1509   59.00   M    2      2       2 1510   65.00   M    8      2       3 1511   58.00   M    7      2       2 1512   46.00   F   10      2       2 1513   61.00   M    2      2       3 1514   67.00   M   10      2       3 1515   47.00   M    6      2       2 1516   63.00   F    6      2       3 1517   47.00   M    0      2       2 1518   69.00   M    1      2       3 1519   67.00   M    3      2       3 1520   57.00   M    4      2       2 1521   69.00   F    0      2       3 1522   61.00   M   12      2       3 1523   73.00   M    4      2       3 1524   37.00   F    1      2       2 1525   59.00   M    6      2       2 1526   63.00   M    7      2       3 1527   60.00   F    6      2       3 1528   40.00   F    7      2       2 1529   52.00   M    4      2       2 1530   40.00   M    0      2       2 1531   70.00   M    1      2       3 1532   55.00   M    0      2       2 1533   56.00   F    0      2       2 1534   80.00   M    0      2       3 1535   67.00   M    2      2       3 1536   83.00   F    4      2       3 1537   64.00   M    1      2       3 1538   59.00   F    1      2       2 1539   68.00   M    0      2       3 1540   51.00   M    0      2       2 1541   40.00   F    2      2       2 1542   64.00   F    0      2       3 1543   58.00   M    9      2       2 1544   70.00   M    1      2       3 1545   52.00   M    3      2       2 1546   44.00   M    3      2       2 1547   56.00   M    5      2       2 1548   83.00   M    8      2       3 1549   54.00   F    0      2       2 1550   55.00   F    1      2       2 1551   26.00   F    4      2       2 1552   58.00   M    4      2       2 1553   40.00   M    0      2       2 1554   50.00   M    0      2       2 1555   49.00   M    1      2       2 1556   56.00   M    0      2       2 1557   54.00   M    1      2       2 1558   57.00   M    0      2       2 1559   43.00   F    0      2       2 1560   61.00   M    1      2       3 1561   70.00   M    0      2       3 1562   21.00   M    2      2       2 1563   55.00   M    2      2       2 1564   79.00   F    2      2       3 1565   40.00   M    0      2       2 1566   40.00   M    3      2       2 1567   66.00   M    2      2       3 1568   85.00   M    0      2       3 1569   55.00   M    0      2       2 1570   59.00   F    0      2       2 1571   60.00   F    5      2       3 1572   42.00   M    3      2       2 1573   23.00   M    1      2       2 1574   83.00   M    0      2       3 1575   67.00   M    0      2       3 1576   64.00   M    2      2       3 1577   58.00   F    0      2       2 1578   60.00   M    1      2       3 1579   63.00   M    0      2       3 1580   55.00   M    3      2       2 1581   65.00   M    0      2       3 1582   58.00   M    0      2       2 1583   59.00   M    0      2       2 1584   71.00   F    1      2       3 1585   60.00   M    0      2       3 1586   74.00   M    0      2       3 1587   67.00   M    0      2       3 1588   75.00   M    1      2       3 1589   52.00   F    1      2       2 1590   45.00   M    1      2       2 1591   63.00   M    0      2       3 1592   65.00   M    0      2       3 1593   48.00   M    0      2       2 1594   59.00   M    0      2       2 1595   26.00   F    1      2       2 1596   54.00   M    2      2       2 1597   21.00   M    0      2       2 1598   59.00   M    1      2       2 1599   54.00   M    9      2       2 1600   65.00   F    0      2       3 1601   80.00   F    0      2       3 1602   68.00   M    3      2       3 1603   65.00   M    0      2       3 1604   65.00   F    0      2       3 1605   67.00   M    1      2       3 1606   52.00   F    4      2       2 1607   32.00   M    3      2       2 1608   65.00   F    0      2       3 1609   71.00   M    0      2       3 1610   59.00   F    0      2       2 1611   58.00   M    8      2       2 1612   83.00   M    9      2       3 1613   47.00   F    0      2       2 1614   80.00   F    4      2       3 1615   80.00   M    9      2       3 1616   76.00   F    5      2       3 1617   71.00   F    0      2       3 1618   66.00   M    4      2       3 1619   60.00   M    3      2       3 1620   65.00   M    7      2       3 1621   47.00   M    1      2       2 1622   60.00   M    7      2       3 1623   65.00   M    0      2       3 1624   72.00   M    6      2       3 1625   40.00   M    7      2       2 1626   75.00   M    0      2       3 1627   55.00   F    6      2       2 1628   64.00   F    5      2       3 1629   66.00   F    2      2       3 1630   40.00   M    1      2       2 1631   48.00   M    2      2       2 1632   59.00   M    8      2       2 1633   54.00   F    5      2       2 1634   41.00   M   10      2       2 1635   52.00   M    1      2       2 1636   50.00   F    0      2       2 1637   25.00   F    1      2       2 1638   42.00   M    0      2       2 1639   60.00   M    0      2       3 1640   67.00   M   30      2       3 1641   68.00   M    3      2       3 1642   72.00   M    6      2       3 1643   74.00   M    1      2       3 1644   76.00   M    2      2       3 1645   56.00   M    4      2       2 1646   37.00   M    0      2       2 1647   73.00   M    1      2       3 1648   63.00   M    1      2       3 1649   28.00   F    1      2       2 1650   49.00   F    1      2       2 1651   65.00   F    0      2       3 1652   70.00   F    0      2       3 1653   75.00   M    1      2       3 1654   81.00   M    2      2       3 1655   46.00   F    0      2       2 1656   65.00   M    0      2       3 1657   50.00   F    0      2       2 1658   68.00   M    1      2       3 1659   30.00   M    0      2       2 1660   58.00   M    0      2       2 1661   70.00   M    0      2       3 1662   65.00   F    1      2       3 1663   48.00   F    0      2       2 1664   66.00   M    0      2       3 1665   62.00   M    1      2       3 1666   67.00   M    0      2       3 1667   47.00   F    0      2       2 1668   53.00   M    0      2       2 1669   50.00   M    0      2       2 1670   44.00   M    0      2       2 1671   69.00   M    1      2       3 1672   60.00   F    0      2       3 1673   70.00   F    1      2       3 1674   49.00   M    1      2       2 1675   40.00   M    1      2       2 1676   46.00   M    0      2       2 1677   75.00   F    1      2       3 1678   55.00   F    1      2       2 1679   78.00   M    0      2       3 1680   70.00   F   17      2       3 1681   65.00   F    6      2       3 1682   34.00   M    1      2       2 1683   59.00   M    1      2       2 1684   61.00   M    2      2       3 1685   48.00   M   11      2       2 1686   65.00   M   11      2       3 1687   60.00   M    7      2       3 1688   60.00   M    0      2       3 1689   60.00   F    8      2       3 1690   76.00   M    9      2       3 1691   55.00   M    1      2       2 1692   57.00   M    7      2       2 1693   49.00   F   10      2       2 1694   34.00   M    5      2       2 1695   58.00   M    0      2       2 1696   58.00   F    2      2       2 1697   45.00   M    9      2       2 1698   31.00   M    6      2       2 1699   78.00   M    0      2       3 1700   64.00   M    6      2       3 1701   44.00   M    1      2       2 1702   60.00   M    4      2       3 1703   78.00   F    1      2       3 1704   47.00   F    2      2       2 1705   66.00   F    7      2       3 1706   55.00   M    3      2       2 1707   50.00   F    1      2       2 1708   65.00   M    7      2       3 1709   72.00   M    4      2       3 1710   63.00   M    4      2       3 1711   25.00   M    3      2       2 1712   48.00   F    1      2       2 1713   70.00   M    3      2       3 1714   70.00   M    2      2       3 1715   55.00   F    0      2       2 1716   57.00   M    1      2       2 1717   45.00   F    4      2       2 1718   64.00   M    5      2       3 1719   54.00   M    0      2       2 1720   47.00   M    5      2       2 1721   21.00   M    1      2       2 1722   79.00   M    1      2       3 1723   65.00   F    2      2       3 1724   76.00   M    4      2       3 1725   58.00   M    0      2       2 1726   45.00   F    0      2       2 1727   68.00   M    0      2       3 1728   58.00   M    4      2       2 1729   64.00   M    3      2       3 1730   78.00   M    0      2       3 1731   67.00   M    1      2       3 1732   81.00   F    1      2       3 1733   55.00   M    3      2       2 1734   55.00   M    3      2       2 1735   55.00   F    1      2       2 1736   52.00   M    0      2       2 1737   63.00   M    1      2       3 1738   55.00   M    0      2       2 1739   65.00   M    0      2       3 1740   66.00   M    0      2       3 1741   65.00   M    0      2       3 1742   70.00   F    1      2       3 1743   49.00   M    1      2       2 1744   59.00   F    1      2       2 1745   64.00   F    5      2       3 1746   57.00   M    1      2       2 1747   66.00   M    0      2       3 1748   48.00   M    3      2       2 1749   70.00   M    2      2       3 1750   82.00   M    0      2       3 1751   76.00   M    0      2       3 1752   73.00   M    4      2       3 1753   40.00   F    1      2       2 1754   45.00   M    4      2       2 1755   73.00   M    2      2       3 1756   53.00   M    0      2       2 1757   80.00   F    1      2       3 1758   69.00   M    1      2       3 1759   41.00   M    1      2       2 1760   75.00   F    0      2       3 1761   70.00   M    0      2       3 1762   72.00   M   16      2       3 1763   41.00   F    4      2       2 1764   38.00   F    2      2       2 1765   72.00   M   14      2       3 1766   45.00   M    0      2       2 1767   65.00   F    7      2       3 1768   58.00   M   13      2       2 1769   56.00   M    9      2       2 1770   59.00   F    7      2       2 1771   75.00   M   10      2       3 1772   80.00   M    0      2       3 1773   65.00   F   12      2       3 1774   73.00   M    7      2       3 1775   36.00   M   12      2       2 1776   47.00   M    3      2       2 1777   88.00   M   13      2       3 1778   53.00   F    9      2       2 1779   75.00   F    1      2       3 1780   50.00   M    5      2       2 1781   60.00   M    4      2       3 1782   65.00   M    8      2       3 1783   70.00   M    4      2       3 1784   93.00   M    8      2       3 1785   70.00   M    0      2       3 1786   70.00   F    6      2       3 1787   72.00   M    3      2       3 1788   75.00   M    6      2       3 1789   68.00   M    6      2       3 1790   54.00   M    6      2       2 1791   55.00   M    3      2       2 1792   71.00   M    4      2       3 1793   75.00   F    0      2       3 1794   55.00   M    1      2       2 1795   48.00   M    3      2       2 1796   76.00   F    3      2       3 1797   60.00   M    3      2       3 1798   71.00   F    3      2       3 1799   75.00   M    0      2       3 1800   63.00   F    1      2       3 1801   83.00   M    0      2       3 1802   76.00   M    0      2       3 1803   82.00   M    1      2       3 1804   65.00   F    5      2       3 1805   70.00   M    2      2       3 1806   60.00   M    1      2       3 1807   51.00   M    0      2       2 1808   65.00   F    1      2       3 1809   61.00   F    2      2       3 1810   64.00   M    3      2       3 1811   60.00   F    1      2       3 1812   65.00   M    0      2       3 1813   41.00   M    0      2       2 1814   35.00   M    0      2       2 1815   43.00   M    1      2       2 1816   63.00   M    1      2       3 1817   74.00   F    0      2       3 1818   60.00   M    3      2       3 1819   61.00   M    0      2       3 1820   75.00   M    7      2       3 1821   75.00   M    0      2       3 1822   35.00   M    0      2       2 1823   50.00   F    0      2       2 1824   73.00   M    0      2       3 1825   48.00   M    4      2       2 1826   75.00   F    1      2       3 1827   52.00   M    2      2       2 1828   63.00   M    0      2       3 1829   65.00   M    1      2       3 1830   65.00   M    0      2       3 1831   67.00   M    0      2       3 1832   65.00   M    3      2       3 1833   50.00   M    0      2       2 1834   55.00   M    0      2       2 1835   51.00   M    1      2       2 1836   39.00   M    0      2       2 1837   89.00   M   12      2       3 1838   46.00   M   16      2       2 1839   56.00   M   19      2       2 1840   52.00   F    1      2       2 1841   62.00   M    2      2       3 1842   58.00   M   10      2       2 1843   65.00   F   10      2       3 1844   63.00   M   10      2       3 1845   63.00   F    1      2       3 1846   67.00   M    0      2       3 1847   55.00   M    1      2       2 1848   38.00   M    2      2       2 1849   65.00   M    0      2       3 1850   60.00   M   11      2       3 1851   60.00   F    6      2       3 1852   75.00   F    6      2       3 1853   46.00   M    0      2       2 1854   74.00   M    0      2       3 1855   72.00   M    8      2       3 1856   68.00   M    2      2       3 1857   62.00   M    9      2       3 1858   51.00   M    0      2       2 1859   55.00   F    0      2       2 1860   60.00   F    6      2       3 1861   65.00   F    2      2       3 1862   66.00   F    2      2       3 1863   43.00   M    6      2       2 1864   58.00   F    5      2       2 1865   82.00   M    2      2       3 1866   63.00   M    7      2       3 1867   84.00   M    6      2       3 1868   65.00   F    7      2       3 1869   65.00   M    8      2       3 1870   68.00   M    0      2       3 1871   64.00   M    7      2       3 1872   46.00   M    6      2       2 1873   60.00   F    8      2       3 1874   48.00   M    5      2       2 1875   44.00   M    3      2       2 1876   61.00   F    0      2       3 1877   78.00   F   10      2       3 1878   55.00   M    5      2       2 1879   72.00   M    1      2       3 1880   58.00   M   11      2       2 1881   70.00   M    9      2       3 1882   56.00   M   12      2       2 1883   47.00   M    1      2       2 1884   52.00   M    0      2       2 1885   62.00   F    1      2       3 1886   74.00   F    1      2       3 1887   59.00   F    3      2       2 1888   38.00   F    0      2       2 1889   60.00   M    5      2       3 1890   68.00   F    3      2       3 1891   75.00   M    1      2       3 1892   70.00   M    2      2       3 1893   55.00   M    1      2       2 1894   50.00   M    2      2       2 1895   72.00   F    1      2       3 1896   64.00   F    2      2       3 1897   62.00   F    2      2       3 1898   58.00   F    1      2       2 1899   58.00   F    0      2       2 1900   69.00   M   20      2       3 1901   60.00   F    1      2       3 1902   76.00   F    6      2       3 1903   31.00   F    5      2       2 1904   40.00   F    0      2       2 1905   56.00   M    3      2       2 1906   59.00   F    5      2       2 1907   55.00   M    1      2       2 1908   71.00   F    2      2       3 1909   45.00   M    4      2       2 1910   53.00   M    0      2       2 1911   60.00   M    0      2       3 1912   50.00   M    2      2       2 1913   31.00   M    1      2       2 1914   50.00   F    2      2       2 1915   46.00   M    0      2       2 1916   60.00   M    1      2       3 1917   40.00   M    0      2       2 1918   62.00   F    2      2       3 1919   57.00   M    4      2       2 1920   60.00   F    1      2       3 1921   66.00   M    0      2       3 1922   55.00   F    1      2       2 1923   65.00   M    0      2       3 1924   63.00   M   20      2       3 1925   56.00   F    0      2       2 1926   60.00   M    0      2       3 1927   40.00   M    0      2       2 1928   50.00   F    0      2       2 1929   38.00   F    0      2       2 1930   65.00   M    0      2       3 1931   80.00   M    0      2       3 1932   55.00   M    0      2       2 1933   72.00   F    2      2       3 1934   50.00   M    7      2       2 1935   47.00   M    0      2       2 1936   47.00   M    2      2       2 1937   54.00   M    2      2       2 1938   65.00   F    0      2       3 1939   67.00   F   19      2       3 1940   60.00   M    0      2       3 1941   60.00   F    0      2       3 1942   50.00   F    0      2       2 1943   49.00   M    0      2       2 1944   50.00   M    9      2       2 1945   65.00   M    3      2       3 1946   54.00   M    2      2       2 1947   62.00   M    8      2       3 1948   45.00   M    2      2       2 1949   66.00   M   11      2       3 1950   59.00   M    5      2       2 1951   58.00   M    2      2       2 1952   60.00   M   25      2       3 1953   58.00   M    5      2       2 1954   60.00   F    3      2       3 1955   75.00   M    3      2       3 1956   50.00   F    9      2       2 1957   60.00   M    9      2       3 1958   55.00   F    1      2       2 1959   54.00   F    1      2       2 1960   26.00   F    3      2       2 1961   60.00   M    9      2       3 1962   53.00   F    7      2       2 1963   35.00   F    0      2       2 1964   59.00   M    8      2       2 1965   62.00   M    3      2       3 1966   55.00   M    8      2       2 1967   60.00   M    8      2       3 1968   63.00   F    2      2       3 1969   98.00   M    4      2       3 1970   68.00   F    1      2       3 1971   68.00   F    5      2       3 1972   72.00   M    4      2       3 1973   80.00   M    1      2       3 1974   72.00   M    2      2       3 1975   68.00   F    1      2       3 1976   60.00   F    5      2       3 1977   48.00   M    4      2       2 1978   50.00   M    6      2       2 1979   56.00   M    1      2       2 1980   56.00   M    4      2       2 1981   52.00   M    2      2       2 1982   60.00   M    1      2       3 1983   78.00   M    0      2       3 1984   48.00   M    2      2       2 1985   62.00   M    2      2       3 1986   62.00   M    4      2       3 1987   70.00   F    1      2       3 1988   75.00   M    2      2       3 1989   44.00   F    3      2       2 1990   62.00   M    2      2       3 1991   78.00   M    3      2       3 1992   52.00   M    0      2       2 1993   55.00   M    0      2       2 1994   75.00   F   17      2       3 1995   80.00   M    0      2       3 1996   43.00   M    1      2       2 1997   36.00   M    0      2       2 1998   65.00   M    5      2       3 1999   51.00   M    1      2       2 2000   34.00   M    1      2       2 2001   60.00   F    0      2       3 2002   47.00   M    1      2       2 2003   64.00   M   10      2       3 2004   51.00   M    3      2       2 2005   62.00   M    2      2       3 2006   82.00   M    1      2       3 2007   82.00   F    0      2       3 2008   72.00   M    0      2       3 2009   59.00   F    5      2       2 2010   65.00   F    0      2       3 2011   74.00   M    0      2       3 2012   86.00   F    0      2       3 2013   44.00   M    1      2       2 2014   70.00   F    1      2       3 2015   62.00   M    0      2       3 2016   46.00   F    2      2       2 2017   44.00   M    0      2       2 2018   50.00   F    1      2       2 2019   71.00   M    0      2       3 2020   30.00   F    1      2       2 2021   69.00   M    1      2       3 2022   65.00   F    2      2       3 2023   65.00   F    1      2       3 2024   75.00   M    0      2       3 2025   51.00   M    0      2       2 2026   58.00   M    0      2       2 2027   34.00   M    0      2       2 2028   56.00   M    0      2       2 2029   72.00   F    0      2       3 2030   65.00   M    1      2       3 2031   70.00   F    1      2       3 2032   66.00   M   16      2       3 2033   73.00   M    3      2       3 2034   68.00   F    0      2       3 2035   46.00   M    1      2       2 2036   60.00   F    0      2       3 2037   55.00   M    0      2       2 2038   59.00   M   10      2       2 2039   46.00   M   10      2       2 2040   72.00   F    5      2       3 2041   84.00   F    4      2       3 2042   74.00   F    7      2       3 2043   58.00   F    0      2       2 2044   70.00   F   14      2       3 2045   46.00   F   10      2       2 2046   71.00   M    8      2       3 2047   88.00   M    7      2       3 2048   68.00   M    0      2       3 2049   75.00   M    6      2       3 2050   52.00   M    1      2       2 2051   68.00   M    6      2       3 2052   60.00   M    7      2       3 2053   64.00   F    2      2       3 2054   67.00   M    1      2       3 2055   64.00   M    7      2       3 2056   58.00   M    4      2       2 2057   67.00   M    5      2       3 2058   52.00   M    0      2       2 2059   68.00   M    2      2       3 2060   50.00   M    5      2       2 2061   56.00   M    0      2       2 2062   85.00   F    4      2       3 2063   60.00   F   12      2       3 2064   66.00   M    5      2       3 2065   80.00   M    2      2       3 2066   65.00   F    0      2       3 2067   62.00   M    2      2       3 2068   87.00   M    2      2       3 2069   82.00   M    1      2       3 2070   66.00   M    2      2       3 2071   80.00   F    3      2       3 2072   85.00   F    3      2       3 2073   50.00   F    1      2       2 2074   54.00   M    4      2       2 2075   50.00   M    2      2       2 2076   56.00   F    1      2       2 2077   80.00   M    0      2       3 2078   80.00   F    0      2       3 2079   68.00   M    3      2       3 2080   65.00   F    0      2       3 2081   58.00   M    4      2       2 2082   55.00   M    6      2       2 2083   35.00   M    0      2       2 2084   55.00   M    1      2       2 2085   65.00   F    3      2       3 2086   58.00   M    0      2       2 2087   40.00   F    0      2       2 2088   67.00   M    3      2       3 2089   24.00   M    0      2       2 2090   36.00   F    4      2       2 2091   40.00   M    1      2       2 2092   39.00   M    3      2       2 2093   52.00   M    0      2       2 2094   66.00   F    2      2       3 2095   36.00   M    0      2       2 2096   55.00   M    3      2       2 2097   65.00   F    0      2       3 2098   42.00   M    0      2       2 2099   47.00   F    9      2       2 2100   55.00   M    1      2       2 2101   55.00   F    8      2       2 2102   73.00   F    2      2       3 2103   80.00   F   13      2       3 2104   39.00   M    0      2       2 2105   56.00   M    1      2       2 2106   52.00   M    0      2       2 2107   26.00   F    1      2       2 2108   63.00   F    5      2       3 2109   76.00   M    1      2       3 2110   50.00   M    0      2       2 2111   55.00   F    0      2       2 2112   58.00   F    1      2       2 2113   72.00   M    0      2       3 2114   62.00   F   23      2       3 2115   30.00   F    0      2       2 2116   72.00   M   12      2       3 2117   91.00   M   11      2       3 2118   59.00   M   12      2       2 2119   22.00   M    3      2       2 2120   81.00   F   10      2       3 2121   73.00   F    2      2       3 2122   53.00   M    0      2       2 2123   65.00   M    8      2       3 2124   67.00   M   15      2       3 2125   70.00   M   15      2       3 2126   87.00   F   11      2       3 2127   50.00   F    7      2       2 2128   60.00   M   12      2       3 2129   65.00   M    3      2       3 2130   30.00   M    1      2       2 2131   54.00   F   13      2       2 2132   72.00   F   22      2       3 2133   71.00   F   14      2       3 2134   52.00   F    7      2       2 2135    8.00   F    0      2       1 2136   64.00   F    3      2       3 2137   86.00   F    5      2       3 2138   86.00   M    4      2       3 2139   65.00   F    0      2       3 2140   77.00   M    1      2       3 2141   69.00   M    4      2       3 2142   54.00   F    0      2       2 2143   70.00   M    7      2       3 2144   60.00   M    1      2       3 2145   52.00   M    0      2       2 2146   70.00   M    5      2       3 2147   50.00   M    0      2       2 2148   80.00   F    2      2       3 2149   88.00   M   21      2       3 2150   76.00   M    1      2       3 2151   60.00   F    1      2       3 2152   41.00   M    3      2       2 2153   58.00   M    5      2       2 2154   54.00   M    5      2       2 2155   45.00   F    3      2       2 2156   60.00   M    1      2       3 2157   48.00   F    3      2       2 2158   63.00   M    3      2       3 2159   60.00   M    2      2       3 2160   92.00   M    3      2       3 2161   46.00   M    1      2       2 2162   65.00   F    2      2       3 2163   85.00   M    1      2       3 2164   55.00   F    2      2       2 2165   85.00   M    3      2       3 2166   64.00   F    0      2       3 2167   65.00   F    2      2       3 2168   38.00   M    1      2       2 2169   64.00   M    0      2       3 2170   69.00   M    1      2       3 2171   65.00   F    1      2       3 2172   51.00   F    3      2       2 2173   84.00   F    0      2       3 2174   57.00   F    0      2       2 2175   57.00   M    2      2       2 2176   49.00   M    4      2       2 2177   60.00   F    0      2       3 2178   63.00   F    0      2       3 2179   58.00   F    0      2       2 2180   56.00   M    0      2       2 2181   62.00   F    0      2       3 2182   83.00   F    1      2       3 2183   63.00   F    1      2       3 2184   55.00   M    0      2       2 2185   74.00   M    3      2       3 2186   56.00   F   18      2       2 2187   52.00   M    1      2       2 2188   78.00   M    1      2       3 2189   54.00   F    1      2       2 2190   53.00   M    2      2       2 2191   45.00   M    1      2       2 2192   19.00   M    1      2       2 2193   82.00   M    0      2       3 2194   58.00   M    0      2       2 2195   47.00   M    0      2       2 2196   65.00   M    1      2       3 2197   40.00   F    1      2       2 2198   60.00   F    1      2       3 2199   78.00   F   11      2       3 2200   66.00   M   10      2       3 2201   73.00   M   10      2       3 2202   60.00   F   12      2       3 2203   52.00   M   11      2       2 2204   74.00   M    3      2       3 2205   62.00   M    8      2       3 2206   65.00   M    4      2       3 2207   63.00   M    2      2       3 2208   60.00   M    2      2       3 2209   55.00   M    3      2       2 2210   52.00   M    3      2       2 2211   76.00   M   15      2       3 2212   73.00   M    5      2       3 2213   68.00   M    6      2       3 2214   62.00   M    6      2       3 2215   65.00   F    0      2       3 2216   72.00   M    4      2       3 2217   65.00   M    5      2       3 2218   52.00   M    6      2       2 2219   72.00   M    5      2       3 2220   62.00   M    5      2       3 2221   34.00   M    2      2       2 2222   72.00   M    2      2       3 2223   61.00   M    4      2       3 2224   36.00   M    4      2       2 2225   53.00   M    1      2       2 2226   39.00   M    6      2       2 2227   52.00   M    1      2       2 2228   96.00   M    1      2       3 2229   65.00   F    6      2       3 2230   45.00   M    6      2       2 2231   55.00   M    7      2       2 2232   56.00   M    0      2       2 2233   70.00   M    0      2       3 2234   81.00   F    2      2       3 2235   75.00   M    0      2       3 2236   61.00   M    0      2       3 2237   45.00   M    0      2       2 2238   57.00   M    3      2       2 2239   55.00   F    1      2       2 2240   56.00   M    0      2       2 2241   40.00   M    3      2       2 2242   66.00   M    1      2       3 2243   65.00   F    0      2       3 2244   70.00   M    0      2       3 2245   42.00   F    1      2       2 2246   50.00   M    1      2       2 2247   60.00   M    1      2       3 2248   63.00   M    0      2       3 2249   56.00   M    1      2       2 2250   56.00   M    4      2       2 2251   63.00   M    0      2       3 2252   60.00   F    0      2       3 2253   36.00   F    1      2       2 2254   59.00   M    1      2       2 2255   70.00   M    1      2       3 2256   58.00   F    0      2       2 2257   75.00   F    2      2       3 2258   90.00   M    3      2       3 2259   70.00   F    1      2       3 2260   49.00   F    0      2       2 2261   84.00   M    5      2       3 2262   60.00   M    4      2       3 2263   80.00   F    0      2       3 2264   72.00   M    0      2       3 2265   53.00   M    0      2       2 2266   69.00   M    0      2       3 2267   55.00   F    2      2       2 2268   87.00   M    2      2       3 2269   63.00   M    1      2       3 2270   56.00   M    3      2       2 2271   67.00   F    3      2       3 2272   65.00   F    0      2       3 2273   62.00   F    2      2       3 2274   51.00   M    4      2       2 2275   31.00   M    1      2       2 2276   65.00   M    2      2       3 2277   64.00   M    0      2       3 2278   94.00   M    1      2       3 2279   62.00   M    1      2       3 2280   35.00   M    0      2       2 2281   75.00   M    1      2       3 2282   70.00   F    0      2       3 2283   65.00   M    1      2       3 2284   60.00   F    0      2       3 2285   60.00   F    0      2       3 2286   59.00   M    0      2       2 2287   61.00   M    1      2       3 2288   65.00   M    0      2       3 2289   72.00   M    0      2       3 2290   60.00   F    1      2       3 2291   32.00   M    0      2       2 2292   78.00   F    0      2       3 2293   40.00   F    0      2       2 2294   55.00   F    0      2       2 2295   59.00   M   10      2       2 2296   80.00   M   19      2       3 2297   80.00   F   16      2       3 2298   67.00   M   21      2       3 2299   72.00   M    0      2       3 2300   87.00   M    3      2       3 2301   69.00   F    0      2       3 2302   65.00   M    2      2       3 2303   52.00   M    5      2       2 2304   65.00   M    8      2       3 2305   28.00   M    9      2       2 2306   61.00   M    2      2       3 2307   80.00   M    6      2       3 2308   68.00   M    9      2       3 2309   55.00   F    9      2       2 2310   65.00   M   10      2       3 2311   57.00   M    6      2       2 2312   65.00   F   11      2       3 2313   80.00   M    0      2       3 2314   50.00   M    0      2       2 2315   73.00   F    2      2       3 2316   78.00   M    3      2       3 2317   74.00   M    5      2       3 2318   82.00   M    1      2       3 2319   83.00   M    1      2       3 2320   72.00   M   22      2       3 2321   46.00   M    1      2       2 2322   36.00   M    0      2       2 2323   70.00   M    1      2       3 2324   75.00   M    3      2       3 2325   85.00   F    4      2       3 2326   85.00   M    2      2       3 2327   69.00   M    2      2       3 2328   70.00   M    6      2       3 2329   47.00   M    7      2       2 2330   66.00   M    0      2       3 2331   69.00   M   11      2       3 2332   70.00   F    3      2       3 2333   78.00   M    0      2       3 2334   56.00   M    1      2       2 2335   45.00   F    2      2       2 2336   61.00   M    1      2       3 2337   70.00   M    4      2       3 2338   51.00   M    3      2       2 2339   52.00   M    1      2       2 2340   42.00   F    0      2       2 2341   67.00   M    2      2       3 2342   77.00   F    2      2       3 2343   68.00   M    5      2       3 2344   45.00   F    1      2       2 2345   40.00   M    8      2       2 2346   68.00   M    0      2       3 2347   54.00   M    1      2       2 2348   67.00   M    3      2       3 2349   66.00   M    2      2       3 2350   66.00   M    0      2       3 2351   72.00   M    0      2       3 2352   75.00   M    0      2       3 2353   62.00   F    0      2       3 2354   57.00   M    1      2       2 2355   62.00   M    0      2       3 2356   72.00   F    3      2       3 2357   85.00   F    0      2       3 2358   58.00   M    1      2       2 2359   55.00   F    0      2       2 2360   57.00   F    8      2       2 2361   58.00   M    0      2       2 2362   75.00   M    0      2       3 2363   80.00   M    1      2       3 2364   84.00   M    0      2       3 2365   70.00   F    0      2       3 2366   66.00   M    2      2       3 2367   50.00   F    0      2       2 2368   54.00   F    0      2       2 2369   60.00   M    0      2       3 2370   80.00   M    2      2       3 2371   31.00   M    4      2       2 2372   80.00   M    5      2       3 2373   20.00   F    0      2       2 2374   40.00   M    0      2       2 2375   55.00   M    1      2       2 2376   81.00   M    0      2       3 2377   78.00   F    1      2       3 2378   62.00   M    2      2       3 2379   68.00   F   14      2       3 2380   73.00   M   16      2       3 2381   50.00   M   13      2       2 2382   31.00   F    4      2       2 2383   72.00   M   11      2       3 2384   54.00   M    5      2       2 2385   26.00   F    5      2       2 2386   35.00   M   10      2       2 2387   52.00   M    2      2       2 2388   58.00   M    0      2       2 2389   66.00   M    5      2       3 2390   70.00   F    8      2       3 2391   72.00   F   11      2       3 2392   64.00   M    0      2       3 2393   45.00   M    6      2       2 2394   86.00   M    9      2       3 2395   75.00   M    1      2       3 2396   60.00   M    9      2       3 2397   70.00   M    7      2       3 2398   75.00   M    0      2       3 2399   48.00   M    7      2       2 2400   50.00   M    9      2       2 2401   60.00   F    2      2       3 2402   80.00   F    6      2       3 2403   60.00   M    6      2       3 2404   33.00   M    0      2       2 2405   77.00   F    6      2       3 2406   54.00   M    6      2       2 2407   58.00   F   32      2       2 2408   55.00   F    0      2       2 2409   83.00   F    6      2       3 2410   64.00   M    6      2       3 2411   50.00   M    2      2       2 2412   58.00   M    1      2       2 2413   51.00   F    1      2       2 2414   74.00   F    6      2       3 2415   71.00   M    2      2       3 2416   36.00   M    6      2       2 2417   54.00   M    1      2       2 2418   58.00   M    4      2       2 2419   65.00   F    4      2       3 2420   61.00   F    2      2       3 2421   29.00   M    0      2       2 2422   62.00   F    1      2       3 2423   81.00   F    0      2       3 2424   47.00   F    6      2       2 2425   51.00   F    4      2       2 2426   64.00   M    4      2       3 2427   53.00   F    0      2       2 2428   47.00   M    3      2       2 2429   70.00   M    3      2       3 2430   76.00   F    4      2       3 2431   70.00   M    5      2       3 2432   76.00   M    0      2       3 2433   46.00   F    0      2       2 2434   51.00   M    3      2       2 2435   35.00   F    0      2       2 2436   56.00   M    6      2       2 2437   48.00   F    0      2       2 2438   40.00   M    1      2       2 2439   85.00   F    0      2       3 2440   53.00   F    4      2       2 2441   55.00   M    2      2       2 2442   85.00   M    3      2       3 2443   78.00   M    4      2       3 2444   54.00   F    2      2       2 2445   75.00   M    2      2       3 2446   59.00   M    0      2       2 2447   51.00   F    8      2       2 2448   70.00   M    2      2       3 2449   70.00   M    2      2       3 2450   65.00   M    0      2       3 2451   68.00   M    2      2       3 2452   70.00   F    2      2       3 2453   74.00   F    0      2       3 2454   70.00   M    0      2       3 2455   54.00   M    1      2       2 2456   66.00   M    2      2       3 2457   70.00   M    1      2       3 2458   70.00   M    0      2       3 2459   75.00   M    2      2       3 2460   79.00   M    0      2       3 2461   50.00   M    3      2       2 2462   68.00   F    5      2       3 2463   49.00   M    0      2       2 2464   60.00   M    2      2       3 2465   70.00   F    1      2       3 2466   55.00   F    1      2       2 2467   69.00   F   17      2       3 2468   70.00   F    0      2       3 2469   58.00   M    2      2       2 2470   86.00   M    2      2       3 2471   50.00   M    0      2       2 2472   70.00   M    0      2       3 2473   56.00   M    0      2       2 2474   51.00   M    2      2       2 2475   65.00   M    0      2       3 2476   68.00   M    1      2       3 2477   82.00   M   17      2       3 2478   62.00   M    5      2       3 2479   66.00   M    8      2       3 2480   62.00   M    2      2       3 2481   70.00   M    9      2       3 2482   49.00   F   16      2       2 2483   50.00   M    8      2       2 2484   55.00   F    2      2       2 2485   73.00   M    5      2       3 2486   70.00   F    4      2       3 2487   60.00   M    2      2       3 2488   62.00   M    0      2       3 2489   64.00   M   15      2       3 2490   55.00   F    0      2       2 2491   79.00   M    2      2       3 2492   65.00   M    8      2       3 2493   56.00   M   11      2       2 2494   67.00   M   11      2       3 2495   65.00   M   14      2       3 2496   55.00   F    2      2       2 2497   60.00   M    8      2       3 2498   58.00   M    2      2       2 2499   68.00   M    0      2       3 2500   52.00   M   10      2       2 2501   58.00   M    1      2       2 2502   80.00   F    9      2       3 2503   67.00   M    7      2       3 2504   57.00   M    7      2       2 2505   63.00   F    6      2       3 2506   68.00   M    7      2       3 2507   68.00   F    8      2       3 2508   38.00   M    0      2       2 2509   40.00   M    0      2       2 2510   54.00   M    0      2       2 2511   70.00   M    6      2       3 2512   45.00   M    1      2       2 2513   68.00   F    1      2       3 2514   65.00   M    2      2       3 2515   19.00   F    7      2       2 2516   40.00   F    4      2       2 2517   63.00   M    2      2       3 2518   56.00   F    7      2       2 2519   58.00   M   10      2       2 2520   61.00   M    1      2       3 2521   40.00   M    4      2       2 2522   52.00   F    6      2       2 2523   74.00   M    5      2       3 2524   78.00   F    5      2       3 2525   45.00   F    3      2       2 2526   55.00   F    3      2       2 2527   80.00   F    0      2       3 2528   55.00   F    5      2       2 2529   39.00   M    0      2       2 2530   74.00   M    2      2       3 2531   60.00   F    0      2       3 2532   81.00   F    2      2       3 2533   52.00   M    0      2       2 2534   60.00   F    0      2       3 2535   57.00   M    0      2       2 2536   54.00   M    1      2       2 2537   38.00   M    1      2       2 2538   45.00   M    1      2       2 2539   55.00   M    4      2       2 2540   84.00   M    1      2       3 2541   41.00   M    6      2       2 2542   68.00   F    2      2       3 2543   42.00   M    2      2       2 2544   40.00   M    3      2       2 2545   62.00   M   11      2       3 2546   65.00   M    3      2       3 2547   45.00   M    4      2       2 2548   35.00   F    0      2       2 2549   48.00   M    0      2       2 2550   62.00   M    0      2       3 2551   69.00   M    0      2       3 2552   70.00   M    3      2       3 2553   85.00   F    9      2       3 2554   60.00   F    2      2       3 2555   64.00   M    0      2       3 2556   45.00   F    1      2       2 2557   54.00   M    0      2       2 2558   67.00   M    1      2       3 2559   69.00   M   16      2       3 2560   62.00   F    2      2       3 2561   45.00   F    2      2       2 2562   65.00   M    1      2       3 2563   85.00   M    2      2       3 2564   68.00   F    1      2       3 2565   72.00   M    0      2       3 2566   44.00   M    1      2       2 2567   60.00   F    2      2       3 2568   57.00   F    1      2       2 2569   40.00   M    0      2       2 2570   76.00   F    1      2       3 2571   58.00   M    0      2       2 2572   60.00   M    2      2       3 2573   60.00   F    2      2       3 2574   69.00   M    0      2       3 2575   79.00   M    0      2       3 2576   68.00   F    9      2       3 2577   54.00   M    1      2       2 2578   60.00   M    1      2       3 2579   58.00   M    1      2       2 2580   70.00   M    0      2       3 2581   46.00   M    0      2       2 2582   60.00   M    2      2       3 2583    3.00   F    0      2       1 2584   23.00   F    0      2       2 2585   52.00   F    0      2       2 2586   55.00   F    4      2       2 2587   65.00   M   18      2       3 2588   40.00   M   20      2       2 2589   70.00   M   12      2       3 2590   39.00   M   14      2       2 2591   75.00   F    2      2       3 2592   62.00   F    1      2       3 2593   69.00   M    0      2       3 2594   70.00   M   13      2       3 2595   45.00   F    0      2       2 2596   54.00   M    0      2       2 2597   77.00   M    9      2       3 2598   58.00   F    3      2       2 2599   85.00   F    4      2       3 2600   48.00   F    4      2       2 2601   48.00   M    6      2       2 2602   35.00   M    3      2       2 2603   77.00   M    9      2       3 2604   75.00   M    7      2       3 2605   56.00   M   12      2       2 2606   73.00   M    0      2       3 2607   65.00   M    7      2       3 2608   58.00   M    1      2       2 2609   88.00   M    1      2       3 2610   63.00   M    1      2       3 2611   76.00   F    5      2       3 2612   80.00   F    1      2       3 2613   64.00   F    9      2       3 2614   72.00   M    7      2       3 2615   30.00   F    8      2       2 2616   30.00   M   10      2       2 2617   73.00   F    8      2       3 2618   62.00   F    8      2       3 2619   46.00   M    1      2       2 2620   66.00   F    0      2       3 2621   74.00   F    8      2       3 2622   55.00   F    7      2       2 2623   65.00   M   10      2       3 2624   65.00   M    2      2       3 2625   50.00   M    0      2       2 2626   60.00   F    6      2       3 2627   75.00   F    4      2       3 2628   68.00   M    6      2       3 2629   62.00   M    5      2       3 2630   65.00   M    5      2       3 2631   58.00   F    0      2       2 2632   53.00   F    6      2       2 2633   60.00   F    4      2       3 2634   23.00   F    0      2       2 2635   38.00   M    6      2       2 2636   53.00   M    0      2       2 2637   36.00   F    4      2       2 2638   49.00   M    3      2       2 2639   82.00   M    0      2       3 2640   70.00   M    3      2       3 2641   43.00   M    1      2       2 2642   67.00   M    1      2       3 2643   38.00   F    0      2       2 2644   62.00   M    3      2       3 2645   70.00   M    3      2       3 2646   42.00   M    0      2       2 2647   70.00   M    3      2       3 2648   68.00   M    1      2       3 2649   73.00   M    2      2       3 2650   40.00   M    2      2       2 2651   83.00   M    0      2       3 2652   58.00   F    2      2       2 2653   48.00   F    0      2       2 2654   75.00   M    2      2       3 2655   80.00   M    3      2       3 2656   65.00   M    1      2       3 2657   40.00   M    0      2       2 2658   67.00   M    1      2       3 2659   50.00   F    3      2       2 2660   71.00   F    0      2       3 2661   61.00   M    4      2       3 2662   58.00   M    1      2       2 2663   46.00   M    0      2       2 2664   64.00   F    1      2       3 2665   44.00   M    2      2       2 2666   65.00   F    0      2       3 2667   93.00   M    0      2       3 2668   50.00   M    0      2       2 2669   52.00   F    1      2       2 2670   60.00   M    0      2       3 2671   90.00   M    1      2       3 2672   72.00   F    1      2       3 2673   74.00   F    1      2       3 2674   90.00   M    3      2       3 2675   63.00   M    1      2       3 2676   70.00   F    1      2       3 2677   82.00   F    2      2       3 2678   67.00   M    2      2       3 2679   65.00   F    0      2       3 2680   79.00   M    0      2       3 2681   70.00   M    2      2       3 2682   65.00   M    0      2       3 2683   65.00   M   32      2       3 2684   70.00   M    2      2       3 2685   70.00   F    0      2       3 2686   64.00   M    1      2       3 2687   73.00   F   13      2       3 2688   50.00   M    1      2       2 2689   66.00   F    1      2       3 2690   87.00   F   14      2       3 2691   70.00   M   12      2       3 2692   50.00   M    0      2       2 2693   62.00   F    1      2       3 2694   65.00   F   13      2       3 2695   70.00   F   17      2       3 2696   59.00   M   10      2       2 2697   73.00   M   13      2       3 2698   78.00   F    8      2       3 2699   38.00   M   11      2       2 2700   62.00   F    1      2       3 2701   82.00   M   12      2       3 2702   82.00   M   11      2       3 2703   45.00   F    6      2       2 2704   65.00   M    9      2       3 2705   55.00   F    0      2       2 2706   40.00   M    7      2       2 2707   41.00   F    1      2       2 2708   55.00   M    8      2       2 2709   70.00   F    7      2       3 2710   70.00   F    8      2       3 2711   45.00   M    0      2       2 2712   46.00   F    8      2       2 2713   60.00   M    0      2       3 2714   72.00   M    7      2       3 2715   62.00   M    2      2       3 2716   67.00   F    9      2       3 2717   70.00   M    6      2       3 2718   65.00   M    2      2       3 2719   70.00   F    1      2       3 2720   86.00   M    6      2       3 2721   70.00   M    6      2       3 2722   62.00   M    5      2       3 2723   43.00   M   12      2       2 2724   78.00   M    6      2       3 2725   36.00   M    4      2       2 2726   62.00   M    8      2       3 2727   60.00   M    4      2       3 2728   59.00   M    1      2       2 2729   65.00   M    3      2       3 2730   56.00   M    5      2       2 2731   62.00   M    4      2       3 2732   45.00   F    1      2       2 2733   63.00   M    3      2       3 2734   46.00   M    5      2       2 2735   68.00   M    4      2       3 2736   40.00   M    0      2       2 2737   68.00   F    0      2       3 2738   62.00   F    1      2       3 2739   45.00   M    0      2       2 2740   62.00   M    4      2       3 2741   69.00   M    2      2       3 2742   65.00   M    4      2       3 2743   65.00   M    0      2       3 2744   62.00   M    0      2       3 2745   75.00   M    2      2       3 2746   60.00   M    4      2       3 2747   74.00   M    0      2       3 2748   67.00   M    0      2       3 2749   60.00   M    0      2       3 2750   36.00   M    0      2       2 2751   75.00   M    0      2       3 2752   80.00   M    0      2       3 2753   62.00   M    1      2       3 2754   60.00   M    8      2       3 2755   77.00   M    1      2       3 2756   59.00   M    2      2       2 2757   60.00   M    1      2       3 2758   43.00   M    0      2       2 2759   70.00   M    2      2       3 2760   72.00   M    1      2       3 2761   49.00   F    1      2       2 2762   80.00   F    0      2       3 2763   60.00   M    1      2       3 2764   52.00   M    2      2       2 2765   67.00   M    1      2       3 2766   67.00   M    0      2       3 2767   48.00   F    1      2       2 2768   56.00   F    0      2       2 2769   65.00   F    1      2       3 2770   52.00   M    0      2       2 2771   43.00   M    0      2       2 2772   70.00   F    1      2       3 2773   55.00   M    0      2       2 2774   58.00   F    0      2       2 2775   69.00   M    0      2       3 2776   80.00   F    0      2       3 2777   52.00   M    1      2       2 2778   65.00   M    0      2       3 2779   66.00   M   20      2       3 2780   25.00   F    1      2       2 2781   42.00   M    0      2       2 2782   63.00   F    4      2       3 2783   23.00   M    0      2       2 2784   78.00   M    1      2       3 2785   60.00   M    4      2       3 2786   53.00   M   12      2       2 2787   58.00   M   10      2       2 2788   64.00   M    1      2       3 2789   50.00   M    2      2       2 2790   48.00   M    2      2       2 2791   65.00   M    3      2       3 2792   60.00   M   14      2       3 2793   73.00   F    8      2       3 2794   57.00   M    1      2       2 2795   77.00   M   14      2       3 2796   69.00   M   10      2       3 2797   67.00   M    1      2       3 2798   40.00   M   14      2       2 2799   65.00   M   10      2       3 2800   63.00   F    9      2       3 2801   45.00   M    4      2       2 2802   35.00   F    0      2       2 2803   50.00   M    1      2       2 2804   59.00   M    3      2       2 2805   53.00   M    9      2       2 2806   58.00   M    4      2       2 2807   78.00   F    9      2       3 2808   43.00   F    2      2       2 2809   21.00   M    7      2       2 2810   46.00   M    4      2       2 2811   70.00   M   11      2       3 2812   73.00   M    2      2       3 2813   67.00   M   10      2       3 2814   74.00   M    3      2       3 2815   75.00   M    5      2       3 2816   27.00   M    2      2       2 2817   70.00   F    1      2       3 2818   46.00   F    3      2       2 2819   49.00   M   17      2       2 2820   66.00   M    0      2       3 2821   58.00   M   11      2       2 2822   52.00   F    0      2       2 2823   75.00   M    6      2       3 2824   59.00   M    0      2       2 2825   58.00   M    6      2       2 2826   54.00   M    3      2       2 2827   74.00   M    5      2       3 2828   75.00   M    2      2       3 2829   70.00   F    4      2       3 2830   60.00   M    3      2       3 2831   60.00   F    0      2       3 2832   55.00   M    1      2       2 2833   69.00   M    1      2       3 2834   59.00   M    4      2       2 2835   50.00   M    0      2       2 2836   53.00   M    2      2       2 2837   65.00   F    3      2       3 2838   50.00   F    0      2       2 2839   51.00   M    3      2       2 2840   28.00   F    3      2       2 2841   56.00   M    0      2       2 2842   85.00   M    7      2       3 2843   52.00   M    1      2       2 2844   81.00   M    0      2       3 2845   59.00   F    3      2       2 2846   85.00   M    0      2       3 2847   68.00   M    0      2       3 2848   40.00   F    0      2       2 2849   65.00   F   11      2       3 2850   67.00   M    1      2       3 2851   54.00   F    1      2       2 2852   76.00   F    2      2       3 2853   56.00   M    3      2       2 2854   35.00   M    0      2       2 2855   46.00   F    2      2       2 2856   65.00   M    1      2       3 2857   70.00   M    0      2       3 2858   30.00   M    0      2       2 2859   22.00   M    0      2       2 2860   75.00   M    3      2       3 2861   37.00   F    0      2       2 2862   60.00   M    1      2       3 2863   40.00   M    2      2       2 2864   60.00   M    1      2       3 2865   65.00   M    1      2       3 2866   55.00   M    1      2       2 2867   58.00   F    0      2       2 2868   29.00   M    3      2       2 2869   65.00   M    0      2       3 2870   75.00   M    0      2       3 2871   35.00   F    2      2       2 2872   35.00   F    0      2       2 2873   65.00   F    0      2       3 2874   44.00   M    2      2       2 2875   61.00   M    0      2       3 2876   85.00   F    1      2       3 2877   72.00   F    8      2       3 2878   72.00   F    0      2       3 2879   55.00   M    0      2       2 2880   55.00   M   18      2       2 2881   49.00   M    1      2       2 2882   66.00   M   15      2       3 2883   58.00   M    9      2       2 2884   74.00   M   12      2       3 2885   67.00   M    7      2       3 2886   63.00   M    8      2       3 2887   51.00   M    7      2       2 2888   72.00   M   30      2       3 2889   38.00   M   17      2       2 2890   60.00   F   18      2       3 2891   80.00   F   12      2       3 2892   54.00   F    5      2       2 2893   59.00   M    5      2       2 2894   82.00   M   14      2       3 2895   54.00   M    0      2       2 2896   53.00   F    3      2       2 2897   64.00   M    7      2       3 2898   59.00   M    3      2       2 2899   46.00   M    7      2       2 2900   65.00   M    5      2       3 2901   78.00   F    7      2       3 2902   50.00   M   21      2       2 2903   70.00   M   10      2       3 2904   55.00   F   11      2       2 2905   50.00   M    9      2       2 2906   65.00   M   11      2       3 2907   81.00   M    9      2       3 2908   60.00   M    8      2       3 2909   65.00   F    0      2       3 2910   39.00   M    2      2       2 2911   62.00   M   10      2       3 2912   80.00   F    9      2       3 2913   50.00   F   10      2       2 2914   51.00   M   16      2       2 2915   21.00   M    3      2       2 2916   58.00   F    8      2       2 2917   41.00   F    9      2       2 2918   45.00   M    0      2       2 2919   72.00   F    3      2       3 2920   46.00   M    1      2       2 2921   51.00   M    5      2       2 2922   57.00   M    1      2       2 2923   36.00   M    6      2       2 2924   65.00   M    0      2       3 2925   91.00   M    1      2       3 2926   83.00   M    6      2       3 2927   82.00   F    5      2       3 2928   65.00   M    4      2       3 2929   60.00   M    4      2       3 2930   70.00   M    6      2       3 2931   67.00   F    7      2       3 2932   60.00   F    1      2       3 2933   51.00   F    1      2       2 2934   69.00   M    3      2       3 2935   45.00   F    0      2       2 2936   49.00   M    4      2       2 2937   65.00   F    4      2       3 2938   55.00   F    9      2       2 2939   72.00   M    1      2       3 2940   77.00   M    4      2       3 2941   55.00   M    2      2       2 2942   73.00   M    0      2       3 2943   90.00   M    1      2       3 2944   39.00   M    4      2       2 2945   56.00   M    4      2       2 2946   75.00   F    2      2       3 2947   60.00   F    4      2       3 2948   74.00   M    3      2       3 2949   49.00   M    4      2       2 2950   58.00   M    2      2       2 2951   40.00   F    3      2       2 2952   54.00   M    1      2       2 2953   78.00   M    2      2       3 2954   65.00   M    2      2       3 2955   60.00   M    2      2       3 2956   48.00   M    2      2       2 2957   44.00   M    7      2       2 2958   55.00   M    0      2       2 2959   76.00   M    0      2       3 2960   55.00   M    1      2       2 2961   75.00   M    1      2       3 2962   82.00   M    2      2       3 2963   70.00   F    3      2       3 2964   50.00   M    5      2       2 2965   52.00   M    2      2       2 2966   55.00   M    2      2       2 2967   58.00   F    0      2       2 2968   50.00   F    1      2       2 2969   38.00   M    1      2       2 2970   75.00   M    2      2       3 2971   70.00   M    0      2       3 2972   75.00   F    4      2       3 2973   64.00   M    8      2       3 2974   58.00   F   11      2       2 2975   63.00   M    0      2       3 2976   80.00   F    9      2       3 2977   64.00   F    2      2       3 2978   71.00   M    6      2       3 2979   67.00   M    3      2       3 2980   42.00   M    7      2       2 2981   37.00   M    7      2       2 2982   85.00   M    5      2       3 2983   50.00   M    0      2       2 2984   61.00   F   16      2       3 2985   40.00   M   14      2       2 2986   51.00   M   16      2       2 2987   60.00   M   13      2       3 2988   74.00   F   14      2       3 2989   58.00   F   15      2       2 2990   65.00   M   13      2       3 2991   52.00   M   10      2       2 2992   69.00   M   11      2       3 2993   61.00   M   12      2       3 2994   80.00   M   11      2       3 2995   61.00   F   11      2       3 2996   46.00   F    0      2       2 2997   50.00   F    8      2       2 2998   65.00   M   12      2       3 2999   49.00   M    3      2       2 3000   53.00   M    7      2       2 3001   65.00   F    3      2       3 3002   72.00   F    7      2       3 3003   89.00   M    1      2       3 3004   65.00   M    1      2       3 3005   38.00   M    5      2       2 3006   45.00   M    1      2       2 3007   50.00   F    0      2       2 3008   62.00   F    9      2       3 3009   48.00   M    4      2       2 3010   40.00   M    7      2       2 3011   62.00   M    0      2       3 3012   67.00   F    9      2       3 3013   65.00   F    9      2       3 3014   62.00   M    2      2       3 3015   35.00   M    7      2       2 3016   65.00   M    0      2       3 3017   71.00   F    0      2       3 3018   47.00   M    7      2       2 3019   57.00   M    7      2       2 3020   62.00   F    0      2       3 3021   75.00   F    6      2       3 3022   48.00   M    1      2       2 3023   55.00   M    1      2       2 3024   49.00   M    6      2       2 3025   57.00   M    2      2       2 3026   43.00   F    1      2       2 3027   77.00   M    5      2       3 3028   76.00   M    5      2       3 3029   50.00   M    4      2       2 3030   82.00   F    1      2       3 3031   64.00   M    0      2       3 3032   74.00   F    3      2       3 3033   59.00   M    4      2       2 3034   55.00   F    1      2       2 3035   54.00   M    1      2       2 3036   33.00   M    5      2       2 3037   78.00   F   10      2       3 3038   57.00   M    1      2       2 3039   63.00   M    4      2       3 3040   50.00   F    0      2       2 3041   63.00   M    1      2       3 3042   80.00   M    3      2       3 3043   58.00   M    0      2       2 3044   55.00   M    1      2       2 3045   60.00   M    1      2       3 3046   45.00   F    0      2       2 3047   66.00   M    5      2       3 3048   68.00   M    0      2       3 3049   48.00   M    3      2       2 3050   75.00   F   14      2       3 3051   27.00   M    0      2       2 3052   57.00   M    1      2       2 3053   73.00   M    1      2       3 3054   55.00   M    0      2       2 3055   80.00   M    0      2       3 3056   70.00   M    1      2       3 3057   55.00   M    0      2       2 3058   71.00   M    2      2       3 3059   60.00   F    1      2       3 3060   32.00   M    5      2       2 3061   70.00   F    0      2       3 3062   60.00   F    2      2       3 3063   55.00   F    0      2       2 3064   23.00   F    1      2       2 3065   55.00   F    2      2       2 3066   40.00   M   14      2       2 3067   75.00   M    0      2       3 3068   38.00   M    2      2       2 3069   30.00   M    0      2       2 3070   49.00   M    0      2       2 3071   75.00   M    3      2       3 3072   56.00   M    2      2       2 3073   55.00   F    1      2       2 3074   75.00   M    0      2       3 3075   54.00   M    0      2       2 3076   55.00   M    1      2       2 3077   65.00   M    2      2       3 3078   55.00   M    1      2       2 3079   70.00   F    0      2       3 3080   46.00   F   19      2       2 3081   80.00   M    8      2       3 3082   73.00   M    0      2       3 3083   77.00   M    2      2       3 3084   77.00   M    1      2       3 3085   54.00   F    5      2       2 3086   58.00   M   11      2       2 3087   56.00   F    8      2       2 3088   57.00   M    5      2       2 3089   62.00   M    0      2       3 3090   86.00   F   15      2       3 3091   50.00   F    0      2       2 3092   46.00   M    2      2       2 3093   56.00   M    6      2       2 3094   54.00   F    2      2       2 3095   76.00   F    5      2       3 3096   56.00   M    2      2       2 3097   61.00   M    4      2       3 3098   77.00   M    4      2       3 3099   53.00   F    0      2       2 3100   70.00   M    2      2       3 3101   73.00   F    6      2       3 3102   63.00   F    1      2       3 3103   59.00   M   15      2       2 3104   72.00   F   16      2       3 3105   78.00   M    2      2       3 3106   52.00   F   12      2       2 3107   53.00   M   15      2       2 3108   64.00   M    1      2       3 3109   70.00   F    0      2       3 3110   64.00   M    8      2       3 3111   66.00   M    7      2       3 3112   70.00   M    1      2       3 3113   70.00   F    1      2       3 3114   60.00   M    9      2       3 3115   21.00   M    6      2       2 3116   61.00   M    3      2       3 3117   82.00   M    8      2       3 3118   50.00   M    4      2       2 3119   49.00   M    8      2       2 3120   67.00   M    9      2       3 3121   79.00   M   10      2       3 3122   69.00   M   13      2       3 3123   70.00   M    4      2       3 3124   67.00   M    7      2       3 3125   58.00   M   10      2       2 3126   52.00   M    0      2       2 3127   64.00   M    5      2       3 3128   72.00   M    9      2       3 3129   65.00   M    5      2       3 3130   70.00   F    5      2       3 3131   45.00   F    5      2       2 3132   49.00   M    5      2       2 3133   46.00   M    4      2       2 3134   68.00   F    1      2       3 3135   54.00   M    8      2       2 3136   39.00   M    4      2       2 3137   67.00   M    3      2       3 3138   77.00   M    5      2       3 3139   41.00   M    2      2       2 3140   48.00   M    2      2       2 3141   46.00   M    1      2       2 3142   71.00   F   12      2       3 3143   44.00   M    5      2       2 3144   68.00   M    5      2       3 3145   78.00   F    4      2       3 3146   38.00   M    0      2       2 3147   68.00   M    6      2       3 3148   67.00   M    2      2       3 3149   65.00   M    2      2       3 3150   92.00   M    3      2       3 3151   52.00   F    0      2       2 3152   56.00   M    0      2       2 3153   70.00   M    0      2       3 3154   68.00   M    4      2       3 3155   45.00   F    3      2       2 3156   67.00   F    1      2       3 3157   50.00   M    2      2       2 3158   58.00   F    3      2       2 3159   65.00   M    4      2       3 3160   57.00   M    2      2       2 3161   51.00   M    1      2       2 3162   62.00   M    1      2       3 3163   87.00   M    3      2       3 3164   45.00   F    0      2       2 3165   48.00   F    1      2       2 3166   65.00   M    0      2       3 3167   55.00   M    2      2       2 3168   66.00   M    0      2       3 3169   76.00   F    5      2       3 3170   69.00   M    1      2       3 3171   62.00   M    0      2       3 3172   60.00   M    0      2       3 3173   75.00   M    1      2       3 3174  102.00   M    0      2       3 3175   65.00   F    1      2       3 3176   89.00   F    2      2       3 3177   62.00   M    1      2       3 3178   32.00   F    3      2       2 3179   36.00   M   16      2       2 3180   40.00   F    0      2       2 3181   41.00   M    0      2       2 3182   14.00   M    2      2       1 3183   41.00   M    1      2       2 3184   60.00   M    1      2       3 3185   55.00   M    0      2       2 3186   60.00   M    0      2       3 3187   57.00   M    4      2       2 3188   63.00   M    6      2       3 3189   63.00   F    1      2       3 3190   58.00   M    0      2       2 3191   45.00   F    0      2       2 3192   45.00   F    1      2       2 3193   65.00   M    1      2       3 3194   76.00   M    0      2       3 3195   63.00   M    1      2       3 3196   83.00   F    8      2       3 3197   64.00   F   16      2       3 3198   89.00   M    5      2       3 3199   40.00   M    0      2       2 3200   89.00   M    1      2       3 3201   55.00   M   13      2       2 3202   43.00   F    9      2       2 3203   73.00   M   10      2       3 3204   43.00   F    1      2       2 3205   49.00   M    5      2       2 3206   70.00   M   10      2       3 3207   80.00   M    2      2       3 3208   55.00   M    0      2       2 3209   38.00   M    8      2       2 3210   75.00   M    1      2       3 3211   65.00   F    0      2       3 3212   48.00   M    7      2       2 3213   52.00   F   13      2       2 3214   64.00   M   12      2       3 3215   74.00   M   19      2       3 3216   62.00   F    2      2       3 3217   72.00   M   10      2       3 3218   71.00   M   12      2       3 3219   50.00   M   10      2       2 3220   52.00   M    5      2       2 3221   70.00   M   10      2       3 3222   53.00   M   10      2       2 3223   80.00   M    9      2       3 3224   70.00   F    7      2       3 3225   35.00   M    9      2       2 3226   72.00   M    1      2       3 3227   75.00   M    9      2       3 3228   73.00   M    1      2       3 3229   50.00   F    4      2       2 3230   64.00   F    0      2       3 3231   60.00   F    0      2       3 3232   42.00   F    7      2       2 3233   74.00   F    2      2       3 3234   85.00   F    7      2       3 3235   72.00   F    3      2       3 3236   75.00   F    5      2       3 3237   80.00   M    6      2       3 3238   50.00   F    3      2       2 3239   76.00   M    4      2       3 3240   69.00   M    7      2       3 3241   48.00   M    5      2       2 3242   73.00   F    3      2       3 3243   66.00   M    5      2       3 3244   53.00   M    5      2       2 3245   55.00   F    6      2       2 3246   38.00   M    4      2       2 3247   72.00   F    3      2       3 3248   55.00   M   14      2       2 3249   82.00   M    4      2       3 3250   54.00   M    0      2       2 3251   75.00   F    2      2       3 3252   50.00   M    0      2       2 3253   55.00   M    1      2       2 3254   63.00   M    2      2       3 3255   88.00   F    4      2       3 3256   71.00   F    1      2       3 3257   75.00   F    1      2       3 3258   65.00   F    0      2       3 3259   80.00   M    1      2       3 3260   70.00   F    1      2       3 3261   55.00   M    1      2       2 3262   50.00   M    2      2       2 3263   40.00   M    4      2       2 3264   57.00   F    0      2       2 3265   55.00   M    5      2       2 3266   56.00   M    0      2       2 3267   65.00   M    0      2       3 3268   45.00   F    1      2       2 3269   80.00   F    1      2       3 3270   40.00   F    3      2       2 3271   83.00   M    0      2       3 3272   38.00   M    0      2       2 3273   49.00   F    0      2       2 3274   70.00   F    1      2       3 3275   71.00   M    1      2       3 3276   42.00   M    0      2       2 3277   70.00   F    0      2       3 3278   65.00   M    0      2       3 3279   72.00   F    0      2       3 3280   65.00   M    1      2       3 3281   35.00   M   21      2       2 3282   67.00   M   10      2       3 3283   23.00   M    1      2       2 3284   66.00   F    7      2       3 3285   28.00   M   12      2       2 3286   68.00   F    1      2       3 3287   61.00   F    0      2       3 3288   63.00   F   12      2       3 3289   57.00   F    0      2       2 3290   53.00   M   11      2       2 3291   65.00   F    9      2       3 3292   53.00   M    7      2       2 3293   66.00   M   12      2       3 3294   52.00   F    1      2       2 3295   60.00   M   13      2       3 3296   77.00   M    7      2       3 3297   43.00   F    1      2       2 3298   61.00   F    0      2       3 3299   56.00   M    6      2       2 3300   46.00   F    4      2       2 3301   72.00   M    8      2       3 3302   75.00   M   11      2       3 3303   65.00   F    1      2       3 3304   18.00   M   15      2       2 3305   81.00   M   12      2       3 3306   70.00   M    7      2       3 3307   33.00   M   11      2       2 3308   62.00   M    6      2       3 3309   50.00   M    2      2       2 3310   55.00   F    1      2       2 3311   77.00   F    8      2       3 3312   66.00   M    5      2       3 3313   31.00   M   11      2       2 3314   71.00   M    7      2       3 3315   67.00   M    3      2       3 3316   66.00   M    4      2       3 3317   65.00   M   11      2       3 3318   50.00   M    0      2       2 3319   65.00   F    9      2       3 3320   78.00   M    4      2       3 3321   80.00   M    7      2       3 3322   45.00   F    1      2       2 3323   73.00   F    3      2       3 3324   56.00   M    1      2       2 3325   55.00   M    5      2       2 3326   48.00   M    6      2       2 3327   52.00   F    5      2       2 3328   37.00   M    7      2       2 3329   92.00   M    8      2       3 3330   75.00   F    1      2       3 3331   65.00   F    0      2       3 3332   53.00   M    6      2       2 3333   75.00   M    6      2       3 3334   74.00   M    0      2       3 3335   23.00   M    7      2       2 3336   61.00   M    1      2       3 3337   70.00   M    7      2       3 3338   78.00   M    4      2       3 3339   58.00   F   37      2       2 3340   60.00   M   11      2       3 3341   72.00   M    3      2       3 3342   72.00   M    1      2       3 3343   75.00   M    6      2       3 3344   68.00   M    9      2       3 3345   82.00   M    4      2       3 3346   68.00   F   17      2       3 3347   66.00   M    4      2       3 3348   40.00   M    0      2       2 3349   11.00   M    5      2       1 3350   55.00   F    0      2       2 3351   86.00   M    5      2       3 3352   37.00   M    2      2       2 3353   69.00   M    2      2       3 3354   34.00   M    1      2       2 3355   65.00   M    3      2       3 3356   52.00   M    7      2       2 3357   70.00   F    4      2       3 3358   66.00   M    3      2       3 3359   65.00   F   14      2       3 3360   57.00   M    1      2       2 3361   65.00   M    1      2       3 3362   74.00   M    2      2       3 3363   49.00   M    0      2       2 3364   23.00   M    1      2       2 3365   56.00   M    2      2       2 3366   63.00   M    2      2       3 3367   75.00   M    0      2       3 3368   69.00   M    4      2       3 3369   82.00   M    0      2       3 3370   64.00   M    0      2       3 3371   43.00   F    1      2       2 3372   75.00   M    2      2       3 3373   67.00   M    0      2       3 3374   90.00   M    3      2       3 3375   67.00   M    0      2       3 3376   70.00   M    1      2       3 3377   63.00   M    1      2       3 3378   60.00   M    0      2       3 3379   67.00   M    0      2       3 3380   85.00   F    2      2       3 3381   45.00   M    0      2       2 3382   70.00   M    1      2       3 3383   82.00   M    1      2       3 3384   67.00   M    0      2       3 3385   55.00   M    2      2       2 3386   60.00   M    2      2       3 3387   65.00   M    0      2       3 3388   67.00   F    0      2       3 3389   49.00   F    8      2       2 3390   90.00   F    1      2       3 3391   61.00   M    2      2       3 3392   45.00   F    1      2       2 3393   90.00   M    7      2       3 3394   56.00   F    1      2       2 3395   65.00   F    4      2       3 3396   67.00   M   10      2       3 3397   68.00   M    2      2       3 3398   68.00   M   17      2       3 3399   65.00   M    3      2       3 3400   68.00   M    0      2       3 3401   58.00   M   13      2       2 3402   58.00   F    2      2       2 3403   60.00   F    6      2       3 3404   67.00   F   13      2       3 3405   51.00   F   19      2       2 3406   76.00   F    1      2       3 3407   51.00   M    3      2       2 3408   83.00   M    9      2       3 3409   76.00   M    1      2       3 3410   74.00   F    6      2       3 3411   87.00   F   19      2       3 3412   79.00   F   16      2       3 3413   76.00   M    6      2       3 3414   76.00   M    1      2       3 3415   70.00   M    3      2       3 3416   88.00   M    2      2       3 3417   22.00   F   16      2       2 3418   65.00   M    6      2       3 3419   54.00   M    6      2       2 3420   74.00   M   13      2       3 3421   57.00   M    6      2       2 3422   70.00   M   13      2       3 3423   60.00   F    0      2       3 3424   56.00   M   14      2       2 3425   68.00   M    2      2       3 3426   65.00   F   10      2       3 3427   45.00   M    1      2       2 3428   75.00   M    6      2       3 3429   61.00   M    8      2       3 3430   71.00   M    5      2       3 3431   47.00   M    6      2       2 3432   35.00   M    8      2       2 3433   67.00   F    8      2       3 3434   45.00   M    8      2       2 3435   80.00   F    9      2       3 3436   54.00   M   25      2       2 3437   58.00   M    3      2       2 3438   52.00   F    0      2       2 3439   87.00   M    6      2       3 3440   80.00   M    6      2       3 3441   75.00   M    7      2       3 3442   64.00   M    8      2       3 3443   43.00   M    0      2       2 3444   34.00   M    2      2       2 3445   35.00   F    0      2       2 3446   50.00   M    6      2       2 3447   56.00   M    7      2       2 3448   48.00   M    6      2       2 3449   68.00   F    1      2       3 3450   70.00   M    4      2       3 3451   83.00   M    5      2       3 3452   50.00   M    4      2       2 3453   75.00   M    2      2       3 3454   34.00   M   12      2       2 3455   58.00   M    3      2       2 3456   53.00   F    4      2       2 3457   65.00   F    6      2       3 3458   78.00   F    0      2       3 3459   70.00   F    3      2       3 3460   65.00   M    5      2       3 3461   77.00   M    0      2       3 3462   80.00   F    4      2       3 3463   65.00   M    3      2       3 3464   82.00   M    2      2       3 3465   58.00   M    5      2       2 3466   57.00   M   31      2       2 3467   60.00   M    2      2       3 3468   35.00   F    4      2       2 3469   52.00   M    2      2       2 3470   58.00   F    6      2       2 3471   45.00   M    0      2       2 3472   80.00   M    1      2       3 3473   56.00   M    5      2       2 3474   80.00   M    2      2       3 3475   70.00   F    1      2       3 3476   95.00   F    0      2       3 3477   39.00   M    0      2       2 3478   70.00   M    0      2       3 3479   59.00   M    2      2       2 3480   60.00   M    2      2       3 3481   48.00   M    2      2       2 3482   57.00   M    2      2       2 3483   58.00   F    3      2       2 3484   70.00   M    1      2       3 3485   65.00   M    2      2       3 3486   77.00   M   21      2       3 3487   70.00   M    0      2       3 3488   65.00   M    1      2       3 3489   24.00   M    1      2       2 3490   50.00   M    1      2       2 3491   70.00   M    0      2       3 3492   62.00   M    1      2       3 3493   80.00   M    1      2       3 3494   80.00   M    0      2       3 3495   78.00   M    0      2       3 3496   55.00   M    1      2       2 3497   58.00   M    0      2       2 3498   42.00   M    3      2       2 3499   74.00   F    0      2       3 3500   54.00   F    0      2       2 3501   53.00   F   16      2       2 3502   58.00   M    2      2       2 3503   69.00   M    7      2       3 3504   58.00   M    3      2       2 3505   34.00   M    0      2       2 3506   73.00   M    0      2       3 3507   86.00   F    8      2       3 3508   46.00   M    1      2       2 3509   79.00   M    5      2       3 3510   62.00   F    3      2       3 3511   59.00   F    4      2       2 3512   75.00   M    0      2       3 3513   80.00   M    4      2       3 3514   65.00   F    7      2       3 3515   60.00   F    0      2       3 3516   76.00   F   18      2       3 3517   65.00   M    3      2       3 3518   70.00   F    0      2       3 3519   84.00   M    8      2       3 3520   46.00   F    0      2       2 3521   55.00   F    1      2       2 3522   57.00   M    3      2       2 3523   55.00   M    0      2       2 3524   66.00   M   15      2       3 3525   72.00   M   18      2       3 3526   70.00   F    3      2       3 3527   63.00   M   14      2       3 3528   66.00   M   14      2       3 3529   64.00   F    7      2       3 3530   60.00   M    0      2       3 3531   73.00   M    4      2       3 3532   55.00   M   15      2       2 3533   65.00   M    2      2       3 3534   86.00   M    0      2       3 3535   88.00   F   13      2       3 3536   68.00   M   13      2       3 3537   58.00   M   11      2       2 3538   58.00   F   12      2       2 3539   82.00   M   10      2       3 3540   64.00   M    6      2       3 3541   67.00   M   11      2       3 3542   64.00   F    0      2       3 3543   72.00   M   10      2       3 3544   64.00   M   11      2       3 3545   55.00   F    2      2       2 3546   44.00   M    9      2       2 3547   73.00   M    7      2       3 3548   68.00   M    0      2       3 3549   40.00   F    0      2       2 3550   70.00   M    1      2       3 3551   80.00   M    4      2       3 3552   65.00   M    3      2       3 3553   52.00   M    1      2       2 3554   72.00   M   16      2       3 3555   70.00   M    1      2       3 3556   75.00   M    0      2       3 3557   48.00   M    6      2       2 3558   60.00   F    6      2       3 3559   72.00   M    9      2       3 3560   33.00   F   10      2       2 3561   53.00   M    7      2       2 3562   30.00   M    2      2       2 3563   52.00   M    0      2       2 3564   62.00   M    4      2       3 3565   76.00   M    5      2       3 3566   87.00   M    9      2       3 3567   80.00   M    6      2       3 3568   53.00   F    2      2       2 3569   86.00   M    5      2       3 3570   67.00   M    0      2       3 3571   33.00   M    8      2       2 3572   52.00   F    1      2       2 3573   48.00   F    2      2       2 3574   78.00   F    4      2       3 3575   80.00   M    3      2       3 3576   67.00   M    0      2       3 3577   58.00   M    3      2       2 3578   61.00   F    5      2       3 3579   73.00   M    2      2       3 3580   52.00   F    2      2       2 3581   50.00   M    0      2       2 3582   85.00   M    2      2       3 3583   70.00   M    2      2       3 3584   60.00   M    4      2       3 3585   55.00   F    0      2       2 3586   69.00   M    0      2       3 3587   80.00   F    2      2       3 3588   50.00   M    3      2       2 3589   64.00   F    0      2       3 3590   65.00   M    1      2       3 3591   70.00   M    1      2       3 3592   82.00   M    3      2       3 3593   61.00   M    0      2       3 3594   72.00   M    6      2       3 3595   68.00   M    0      2       3 3596   60.00   F    0      2       3 3597   68.00   M    1      2       3 3598   62.00   F    0      2       3 3599   50.00   M    0      2       2 3600   71.00   M    9      2       3 3601   45.00   M    0      2       2 3602   45.00   M    0      2       2 3603   70.00   F    0      2       3 3604   54.00   M    1      2       2 3605   53.00   F    4      2       2 3606   49.00   M    4      2       2 3607   49.00   M    6      2       2 3608   65.00   F    4      2       3 3609   62.00   M   17      2       3 3610   70.00   M   22      2       3 3611   50.00   F    7      2       2 3612   56.00   F   18      2       2 3613   46.00   F   20      2       2 3614   49.00   M    8      2       2 3615   58.00   M    1      2       2 3616   60.00   M    2      2       3 3617   72.00   M    7      2       3 3618   60.00   F   19      2       3 3619   45.00   F    1      2       2 3620   40.00   M   10      2       2 3621   75.00   M    3      2       3 3622   65.00   M    2      2       3 3623   55.00   M    9      2       2 3624   43.00   M   11      2       2 3625   42.00   M   14      2       2 3626   53.00   M   13      2       2 3627   32.00   M   16      2       2 3628   66.00   M   11      2       3 3629   66.00   F   12      2       3 3630   38.00   M   11      2       2 3631   68.00   F   10      2       3 3632   70.00   M   11      2       3 3633   62.00   F   11      2       3 3634   55.00   M    6      2       2 3635   68.00   M   12      2       3 3636   72.00   F   12      2       3 3637   70.00   M    0      2       3 3638   85.00   F    9      2       3 3639   48.00   M    7      2       2 3640   50.00   M    4      2       2 3641   70.00   M    1      2       3 3642   63.00   M    2      2       3 3643   76.00   M    6      2       3 3644   71.00   M    9      2       3 3645   80.00   M    9      2       3 3646   92.00   M    0      2       3 3647   68.00   M    0      2       3 3648   64.00   M    6      2       3 3649   66.00   M   26      2       3 3650   76.00   M    4      2       3 3651   60.00   F    0      2       3 3652   67.00   M    6      2       3 3653   68.00   M   33      2       3 3654   80.00   M    7      2       3 3655   78.00   M    4      2       3 3656   50.00   M    0      2       2 3657   25.00   F    1      2       2 3658   37.00   F    0      2       2 3659   55.00   M    6      2       2 3660   70.00   F    1      2       3 3661   77.00   M    7      2       3 3662   50.00   F    1      2       2 3663   65.00   M    1      2       3 3664   60.00   M    0      2       3 3665   72.00   M    5      2       3 3666   53.00   M    6      2       2 3667   74.00   M    0      2       3 3668   70.00   M    1      2       3 3669   49.00   M    0      2       2 3670   64.00   F    4      2       3 3671   78.00   M    5      2       3 3672   36.00   M    3      2       2 3673   58.00   M    1      2       2 3674   67.00   M   22      2       3 3675   65.00   M    3      2       3 3676   54.00   F    5      2       2 3677   62.00   F    0      2       3 3678   69.00   M    2      2       3 3679   80.00   M   21      2       3 3680   44.00   F    0      2       2 3681   39.00   M    3      2       2 3682   61.00   M    3      2       3 3683   73.00   M    2      2       3 3684   70.00   M    3      2       3 3685   34.00   M   10      2       2 3686   62.00   M    5      2       3 3687   48.00   M    2      2       2 3688   75.00   F    2      2       3 3689   38.00   F    0      2       2 3690   65.00   M    0      2       3 3691   87.00   F    0      2       3 3692   59.00   M    1      2       2 3693   55.00   M    1      2       2 3694   45.00   F    2      2       2 3695   48.00   F    0      2       2 3696   28.00   M    0      2       2 3697   52.00   M    5      2       2 3698   56.00   F    6      2       2 3699   60.00   M   11      2       3 3700   74.00   M    2      2       3 3701   65.00   F    2      2       3 3702   48.00   M    0      2       2 3703   65.00   M    1      2       3 3704   65.00   M    2      2       3 3705   60.00   M    0      2       3 3706   40.00   M    0      2       2 3707   70.00   F    1      2       3 3708   62.00   F    6      2       3 3709   39.00   M    1      2       2 3710   63.00   M    1      2       3 3711   70.00   M    1      2       3 3712   70.00   F    0      2       3 3713   86.00   M    0      2       3 3714   63.00   F    0      2       3 3715   48.00   M    0      2       2 3716   65.00   M    0      2       3 3717   69.00   M    0      2       3 3718   55.00   F    0      2       2 3719   71.00   M    0      2       3 3720   75.00   M    0      2       3 3721   58.00   F    0      2       2 3722   71.00   F    0      2       3 3723   78.00   M    0      2       3 3724   71.00   F    0      2       3 3725   67.00   F    0      2       3 3726   67.00   F    0      2       3 3727   70.00   M    0      2       3 3728   53.00   M    0      2       2 3729   72.00   M    0      2       3 3730   65.00   M    0      2       3 3731   58.00   M    0      2       2 3732   44.00   F    0      2       2 3733   58.00   F    0      2       2 3734   79.00   M    0      2       3 3735   40.00   M    0      2       2 3736   65.00   F    0      2       3 3737   80.00   F    0      2       3 3738   70.00   M    0      2       3 3739   40.00   F    0      2       2 3740   75.00   M    0      2       3 3741   63.00   M    0      2       3 3742   74.00   M    0      2       3 3743   70.00   M    0      2       3 3744   42.00   M    0      2       2 3745   78.00   M    0      2       3 3746   71.00   M    0      2       3 3747   46.00   F    0      2       2 3748   76.00   M    0      2       3 3749   63.00   M    0      2       3 3750   73.00   M    0      2       3 3751   80.00   M    0      2       3 3752   85.00   M    0      2       3 3753   62.00   M    0      2       3 3754   73.00   M    0      2       3 3755   46.00   M    0      2       2 3756   78.00   F    0      2       3 3757   55.00   M    0      2       2 3758   66.00   M    0      2       3 3759   60.00   F    0      2       3 3760   70.00   F    0      2       3 3761   68.00   F    0      2       3 3762   62.00   M    0      2       3 3763   75.00   F    0      2       3 3764   62.00   M    0      2       3 3765   71.00   M    0      2       3 3766   63.00   M    0      2       3 3767   23.00   M    0      2       2 3768   50.00   M    0      2       2 3769   60.00   M    0      2       3 3770   70.00   M    0      2       3 3771   76.00   M    0      2       3 3772   70.00   M    0      2       3 3773   68.00   M    0      2       3 3774   79.00   F    0      2       3 3775   67.00   M    0      2       3 3776   80.00   M    0      2       3 3777   26.00   F    0      2       2 3778   72.00   F    0      2       3 3779   82.00   M    0      2       3 3780   62.00   M    0      2       3 3781   48.00   M    0      2       2 3782   50.00   M    0      2       2 3783   72.00   F    0      2       3 3784   75.00   M    0      2       3 3785   63.00   M    0      2       3 3786   65.00   F    0      2       3 3787   57.00   M    0      2       2 3788   46.00   M    0      2       2 3789   42.00   F    0      2       2 3790   75.00   M    0      2       3 3791   56.00   F    0      2       2 3792   56.00   M    0      2       2 3793   61.00   F    0      2       3 3794   77.00   F    0      2       3 3795   35.00   M    0      2       2 3796   41.00   M    0      2       2 3797   72.00   M    0      2       3 3798   58.00   M    0      2       2 3799   65.00   M    0      2       3 3800   65.00   F    0      2       3 3801   60.00   M    0      2       3 3802   60.00   F    0      2       3 3803   64.00   M    0      2       3 3804   65.00   M    0      2       3 3805   62.00   M    0      2       3 3806   77.00   F    0      2       3 3807   62.00   F    0      2       3 3808   56.00   M    0      2       2 3809   50.00   M    0      2       2 3810   65.00   F    0      2       3 3811   80.00   F    0      2       3 3812   65.00   F    0      2       3 3813   56.00   M    0      2       2 3814   70.00   M    0      2       3 3815   73.00   M    0      2       3 3816   70.00   M    0      2       3 3817   77.00   M    0      2       3 3818   78.00   M    0      2       3 3819   80.00   M    0      2       3 3820   80.00   F    0      2       3 3821   60.00   F    0      2       3 3822   63.00   M    0      2       3 3823   46.00   M    0      2       2 3824   50.00   F    0      2       2 3825   40.00   M    0      2       2 3826   80.00   M    0      2       3 3827   38.00   M    0      2       2 3828   56.00   M    0      2       2 3829   51.00   F    0      2       2 3830   76.00   M    0      2       3 3831   67.00   F    0      2       3 3832   16.00   M    0      2       1 3833   68.00   M    0      2       3 3834   68.00   M    0      2       3 3835   66.00   M    0      2       3 3836   58.00   M    0      2       2 3837   50.00   M    0      2       2 3838   52.00   M    6      2       2 3839   52.00   F    9      2       2 3840   61.00   M   24      2       3 3841   77.00   M    7      2       3 3842   85.00   F    4      2       3 3843   44.00   F    1      2       2 3844   59.00   M    9      2       2 3845   57.00   M    3      2       2 3846   48.00   M    1      2       2 3847   48.00   M    3      2       2 3848   74.00   F    5      2       3 3849   50.00   M   24      2       2 3850   33.00   M    5      2       2 3851   49.00   M   12      2       2 3852   72.00   M   28      2       3 3853   65.00   M    7      2       3 3854   66.00   M    4      2       3 3855   70.00   M   11      2       3 3856   49.00   F   11      2       2 3857   78.00   M   21      2       3 3858   42.00   M    2      2       2 3859   55.00   M    4      2       2 3860   62.00   M    2      2       3 3861   70.00   M   48      2       3 3862   70.00   F   18      2       3 3863   73.00   M    4      2       3 3864   68.00   F    3      2       3 3865   56.00   F    9      2       2 3866   70.00   M    6      2       3 3867   85.00   M    2      2       3 3868   64.00   M    5      2       3 3869   52.00   M   18      2       2 3870   60.00   F   10      2       3 3871   33.00   M   11      2       2 3872   30.00   F    1      2       2 3873   65.00   M   14      2       3 3874   86.00   M    9      2       3 3875   51.00   M   11      2       2 3876   59.00   M    0      2       2 3877   57.00   M   12      2       2 3878   30.00   M    0      2       2 3879   58.00   M   17      2       2 3880   64.00   M    0      2       3 3881   55.00   M    9      2       2 3882   51.00   M   37      2       2 3883   69.00   M    1      2       3 3884   50.00   M   16      2       2 3885   41.00   M    8      2       2 3886   62.00   M   10      2       3 3887   81.00   M    7      2       3 3888   56.00   M    6      2       2 3889   67.00   M    5      2       3 3890   48.00   F    9      2       2 3891   50.00   M    6      2       2 3892   72.00   M    3      2       3 3893   90.00   F    8      2       3 3894   59.00   F    5      2       2 3895   60.00   M    5      2       3 3896   42.00   M    1      2       2 3897   56.00   M    5      2       2 3898   54.00   M    0      2       2 3899   55.00   M    6      2       2 3900   45.00   M    4      2       2 3901   65.00   M    1      2       3 3902   78.00   M    6      2       3 3903   82.00   M   11      2       3 3904   70.00   M    2      2       3 3905   72.00   M    2      2       3 3906   45.00   F    2      2       2 3907   65.00   M    1      2       3 3908   49.00   F    5      2       2 3909   50.00   M    3      2       2 3910   35.00   M    0      2       2 3911   72.00   M    6      2       3 3912   38.00   M    1      2       2 3913   60.00   M    1      2       3 3914   63.00   M    1      2       3 3915   42.00   M    4      2       2 3916   29.00   F    6      2       2 3917   68.00   M    4      2       3 3918   75.00   M    1      2       3 3919   72.00   F    0      2       3 3920   55.00   M    0      2       2 3921   87.00   M    1      2       3 3922   72.00   M    1      2       3 3923   80.00   F    1      2       3 3924   58.00   F    2      2       2 3925   70.00   M    3      2       3 3926   60.00   F    0      2       3 3927   85.00   F    3      2       3 3928   38.00   F    2      2       2 3929   86.00   F    4      2       3 3930   60.00   M    2      2       3 3931   67.00   F    1      2       3 3932   80.00   F    8      2       3 3933   74.00   M    0      2       3 3934   42.00   M    3      2       2 3935   43.00   F    4      2       2 3936   35.00   F    0      2       2 3937   73.00   M    1      2       3 3938   50.00   F    0      2       2 3939   50.00   M    0      2       2 3940   69.00   M    2      2       3 3941   57.00   F    0      2       2 3942   50.00   M    0      2       2 3943   51.00   M    0      2       2 3944   62.00   M    0      2       3 3945   47.00   F    2      2       2 3946   53.00   M    1      2       2 3947   70.00   F    2      2       3 3948   76.00   M    2      2       3 3949   75.00   M    0      2       3 3950   40.00   M    4      2       2 3951   50.00   M    0      2       2 3952   65.00   M    0      2       3 3953   66.00   M    4      2       3 3954   37.00   F    3      2       2 3955   42.00   F    6      2       2 3956   58.00   M   14      2       2 3957   51.00   M   10      2       2 3958   73.00   M    1      2       3 3959   92.00   M    0      2       3 3960   62.00   F    0      2       3 3961   38.00   F   10      2       2 3962   67.00   M   20      2       3 3963   65.00   F    0      2       3 3964   67.00   M   20      2       3 3965   73.00   M   23      2       3 3966   56.00   M   16      2       2 3967   80.00   M   13      2       3 3968   55.00   M   17      2       2 3969   63.00   F    6      2       3 3970   39.00   F    8      2       2 3971   55.00   M   12      2       2 3972   75.00   F    6      2       3 3973   42.00   M    2      2       2 3974   75.00   F   19      2       3 3975   87.00   M    7      2       3 3976   75.00   F    6      2       3 3977   26.00   M    7      2       2 3978   63.00   M    5      2       3 3979   60.00   M   18      2       3 3980   45.00   M   10      2       2 3981   68.00   M   18      2       3 3982   68.00   M   12      2       3 3983   62.00   F    1      2       3 3984   65.00   F   10      2       3 3985   57.00   M   19      2       2 3986   54.00   M    2      2       2 3987   62.00   M    7      2       3 3988   34.00   M   14      2       2 3989   41.00   M   16      2       2 3990   55.00   F    4      2       2 3991   60.00   M    2      2       3 3992   50.00   M    2      2       2 3993   74.00   M    6      2       3 3994   48.00   M   10      2       2 3995   56.00   M   10      2       2 3996   35.00   F   10      2       2 3997   75.00   M    0      2       3 3998   80.00   M   13      2       3 3999   72.00   M   11      2       3 4000   83.00   M    1      2       3 4001   48.00   M    8      2       2 4002   65.00   M   22      2       3 4003   84.00   M    0      2       3 4004   77.00   M    8      2       3 4005   56.00   M    8      2       2 4006   75.00   M    0      2       3 4007   38.00   M    0      2       2 4008   63.00   F    0      2       3 4009   72.00   M    4      2       3 4010   62.00   M   10      2       3 4011   54.00   F    0      2       2 4012   75.00   M   10      2       3 4013   67.00   F    2      2       3 4014   52.00   M    3      2       2 4015   75.00   F    3      2       3 4016   64.00   M    1      2       3 4017   72.00   F   11      2       3 4018   75.00   F    0      2       3 4019   57.00   M   11      2       2 4020   65.00   F    3      2       3 4021   94.00   M    3      2       3 4022   63.00   M    1      2       3 4023   60.00   M    8      2       3 4024   42.00   M   11      2       2 4025   56.00   F    7      2       2 4026   58.00   M    7      2       2 4027   68.00   M    5      2       3 4028   60.00   M    6      2       3 4029   65.00   F   31      2       3 4030   40.00   M    2      2       2 4031   67.00   M    5      2       3 4032   68.00   M    3      2       3 4033   48.00   M    3      2       2 4034   38.00   M    4      2       2 4035   37.00   M    8      2       2 4036   67.00   M    1      2       3 4037   62.00   F    1      2       3 4038   52.00   M    2      2       2 4039   53.00   M    4      2       2 4040   67.00   F    1      2       3 4041   48.00   M    7      2       2 4042   75.00   M    3      2       3 4043   80.00   M    1      2       3 4044   40.00   M    0      2       2 4045   41.00   M    0      2       2 4046   65.00   M    1      2       3 4047   63.00   M    0      2       3 4048   45.00   M    5      2       2 4049   49.00   M    0      2       2 4050   55.00   F    5      2       2 4051   62.00   M    4      2       3 4052   45.00   M    4      2       2 4053   60.00   F    1      2       3 4054   60.00   M    0      2       3 4055   80.00   M    3      2       3 4056   50.00   M    0      2       2 4057   55.00   M    2      2       2 4058   78.00   M    0      2       3 4059   67.00   F    0      2       3 4060   45.00   M    0      2       2 4061   90.00   F    0      2       3 4062   62.00   M    0      2       3 4063   53.00   M    0      2       2 4064   60.00   M    0      2       3 4065   64.00   F    0      2       3 4066   37.00   F    0      2       2 4067   24.00   F    2      2       2 4068   75.00   M    0      2       3 4069   22.00   F    4      2       2 4070   75.00   F    0      2       3 4071   54.00   F    1      2       2 4072   34.00   F    2      2       2 4073   64.00   M    2      2       3 4074    0.00   M    0      2       1 4075   44.00   F    0      2       2 4076   65.00   M    1      2       3 4077   64.00   M    4      2       3 4078   60.00   M    3      2       3 4079   65.00   F    1      2       3 4080   56.00   M    0      2       2 4081   47.00   M    0      2       2 4082   65.00   M    2      2       3 4083   51.00   M    2      2       2 4084   65.00   F    0      2       3 4085   80.00   M    0      2       3 4086   55.00   M    1      2       2 4087   60.00   M    0      2       3 4088   65.00   F    1      2       3 4089   32.00   M    0      2       2 4090   59.00   F    0      2       2 4091   24.00   M    1      2       2 4092   74.00   F    9      2       3 4093   63.00   M   20      2       3 4094   72.00   M   21      2       3 4095   85.00   F   20      2       3 4096   42.00   M    9      2       2 4097   79.00   M   14      2       3 4098   65.00   M    5      2       3 4099   49.00   M   17      2       2 4100   70.00   M   22      2       3 4101   46.00   M    5      2       2 4102   70.00   F    7      2       3 4103   84.00   M    4      2       3 4104   67.00   F    5      2       3 4105   45.00   M    6      2       2 4106   48.00   M    1      2       2 4107   47.00   M   13      2       2 4108   82.00   M    7      2       3 4109   65.00   M    1      2       3 4110   78.00   M    7      2       3 4111   70.00   M    0      2       3 4112   38.00   M    0      2       2 4113   45.00   M    0      2       2 4114   80.00   M    5      2       3 4115   57.00   M    6      2       2 4116   50.00   M    0      2       2 4117   80.00   M    4      2       3 4118   76.00   M    4      2       3 4119   45.00   M    1      2       2 4120   73.00   M    1      2       3 4121   72.00   M    5      2       3 4122   65.00   M    3      2       3 4123   65.00   M    0      2       3 4124   68.00   M   13      2       3 4125   52.00   M    9      2       2 4126   45.00   F    1      2       2 4127   52.00   M    4      2       2 4128   64.00   M    9      2       3 4129   76.00   M    3      2       3 4130   84.00   M    4      2       3 4131   36.00   M   11      2       2 4132   46.00   F    2      2       2 4133   64.00   M    9      2       3 4134   53.00   M    9      2       2 4135   75.00   F    0      2       3 4136   82.00   M    5      2       3 4137   56.00   F    8      2       2 4138   58.00   M    9      2       2 4139   73.00   F    4      2       3 4140   62.00   M    7      2       3 4141   45.00   M    5      2       2 4142   55.00   F    7      2       2 4143   55.00   F    4      2       2 4144   55.00   M    8      2       2 4145   53.00   F    6      2       2 4146   42.00   M    0      2       2 4147   78.00   M    6      2       3 4148   60.00   M    8      2       3 4149   62.00   M    0      2       3 4150   71.00   M    7      2       3 4151   27.00   M    6      2       2 4152   60.00   F    7      2       3 4153   66.00   F    9      2       3 4154   61.00   F    4      2       3 4155   55.00   M    5      2       2 4156   65.00   M    0      2       3 4157   73.00   M    4      2       3 4158   75.00   M    4      2       3 4159   64.00   F    5      2       3 4160   52.00   F    6      2       2 4161   85.00   M    0      2       3 4162   40.00   F    1      2       2 4163   52.00   F    4      2       2 4164   31.00   M    0      2       2 4165   65.00   M    3      2       3 4166   50.00   M    3      2       2 4167   67.00   M    1      2       3 4168   58.00   M    2      2       2 4169   60.00   F    0      2       3 4170   56.00   M   29      2       2 4171   55.00   M    2      2       2 4172   65.00   M    3      2       3 4173   53.00   M    0      2       2 4174   60.00   M    3      2       3 4175   50.00   F    1      2       2 4176   55.00   M    0      2       2 4177   72.00   F    2      2       3 4178   85.00   F    1      2       3 4179   64.00   M    4      2       3 4180   67.00   F    0      2       3 4181   42.00   M    4      2       2 4182   38.00   M    4      2       2 4183   58.00   M    3      2       2 4184   56.00   M    0      2       2 4185   61.00   M    3      2       3 4186   65.00   F    0      2       3 4187   20.00   M    1      2       2 4188   89.00   M    1      2       3 4189   70.00   M    3      2       3 4190   65.00   F    2      2       3 4191   47.00   M    1      2       2 4192   71.00   F    0      2       3 4193   70.00   M    1      2       3 4194   47.00   M    0      2       2 4195   88.00   M    1      2       3 4196   46.00   M    0      2       2 4197   71.00   M    9      2       3 4198   45.00   M    0      2       2 4199   70.00   F   22      2       3 4200   75.00   M    0      2       3 4201   70.00   M    0      2       3 4202   58.00   M    2      2       2 4203   55.00   F    2      2       2 4204   59.00   M    0      2       2 4205   55.00   F    0      2       2 4206   50.00   F    0      2       2 4207   75.00   M    1      2       3 4208   46.00   M    0      2       2 4209   33.00   M    0      2       2 4210   60.00   M    3      2       3 4211   78.00   M    0      2       3 4212   73.00   F   10      2       3 4213   65.00   M    0      2       3 4214   70.00   F    0      2       3 4215   60.00   M    0      2       3 4216   50.00   F    1      2       2 4217   48.00   M    2      2       2 4218   75.00   M   16      2       3 4219   72.00   F    9      2       3 4220   52.00   F    6      2       2 4221   10.00   F    1      2       1 4222   55.00   F    5      2       2 4223   67.00   M   15      2       3 4224   61.00   M    2      2       3 4225   53.00   F    4      2       2 4226   60.00   M    8      2       3 4227   68.00   F    9      2       3 4228   62.00   M   15      2       3 4229   50.00   F    4      2       2 4230   71.00   M    2      2       3 4231   72.00   F    0      2       3 4232   53.00   M    4      2       2 4233   36.00   M   11      2       2 4234   75.00   F    8      2       3 4235   70.00   M    2      2       3 4236   55.00   M    6      2       2 4237   52.00   F    9      2       2 4238   62.00   M    4      2       3 4239   70.00   M   11      2       3 4240   42.00   F    3      2       2 4241   68.00   F   11      2       3 4242   56.00   M   11      2       2 4243   56.00   F    9      2       2 4244   59.00   M    6      2       2 4245   60.00   F   10      2       3 4246   63.00   M    8      2       3 4247   75.00   F    8      2       3 4248   64.00   M    9      2       3 4249   75.00   M    0      2       3 4250   34.00   F    2      2       2 4251   61.00   F    6      2       3 4252   40.00   M   13      2       2 4253   70.00   M    8      2       3 4254   55.00   F    9      2       2 4255   74.00   M    1      2       3 4256   36.00   M    6      2       2 4257   68.00   F    3      2       3 4258   40.00   M    6      2       2 4259   45.00   F    2      2       2 4260   68.00   M    6      2       3 4261   64.00   M    3      2       3 4262   63.00   M    4      2       3 4263   62.00   M    4      2       3 4264   65.00   M    5      2       3 4265   43.00   M    5      2       2 4266   50.00   M    2      2       2 4267   62.00   M    6      2       3 4268   42.00   M    2      2       2 4269   71.00   M    5      2       3 4270   64.00   M    3      2       3 4271   40.00   M    3      2       2 4272   55.00   F    3      2       2 4273   68.00   M    5      2       3 4274   60.00   F    4      2       3 4275   68.00   M    1      2       3 4276   60.00   M    4      2       3 4277   45.00   F    3      2       2 4278   84.00   M    4      2       3 4279   60.00   F    5      2       3 4280   55.00   M    1      2       2 4281   45.00   M    1      2       2 4282   82.00   M    4      2       3 4283   57.00   F    3      2       2 4284   30.00   M    2      2       2 4285    0.00   F    3      2       1 4286   58.00   F    1      2       2 4287   45.00   M    8      2       2 4288   55.00   M    0      2       2 4289   42.00   M    3      2       2 4290   65.00   F    1      2       3 4291   65.00   F    0      2       3 4292   46.00   F    3      2       2 4293   53.00   M    1      2       2 4294   75.00   F    2      2       3 4295   65.00   M    0      2       3 4296   65.00   M    2      2       3 4297   51.00   F    0      2       2 4298   69.00   F    1      2       3 4299   52.00   M    1      2       2 4300   55.00   M    3      2       2 4301   68.00   F    1      2       3 4302   51.00   M    4      2       2 4303   27.00   M    2      2       2 4304   40.00   F    2      2       2 4305   52.00   M    4      2       2 4306   30.00   M    9      2       2 4307   25.00   F    1      2       2 4308   65.00   F    0      2       3 4309   57.00   M    0      2       2 4310   63.00   M    1      2       3 4311   70.00   M    5      2       3 4312   65.00   M    0      2       3 4313   71.00   M    1      2       3 4314   65.00   M    1      2       3 4315   50.00   F    1      2       2 4316   52.00   M    0      2       2 4317   63.00   M    1      2       3 4318   55.00   M    0      2       2 4319   60.00   M    1      2       3 4320   52.00   F    1      2       2 4321   75.00   F    2      2       3 4322   38.00   M    6      2       2 4323   55.00   F    4      2       2 4324   73.00   M    3      2       3 4325   60.00   F   42      2       3 4326   88.00   F    6      2       3 4327   70.00   F    2      2       3 4328   75.00   M   18      2       3 4329   56.00   M    8      2       2 4330   52.00   M    7      2       2 4331   55.00   F    4      2       2 4332   65.00   F   10      2       3 4333   60.00   M   11      2       3 4334   66.00   M   11      2       3 4335   75.00   M    3      2       3 4336   76.00   M   11      2       3 4337   65.00   M   10      2       3 4338   63.00   M    5      2       3 4339   70.00   M    1      2       3 4340   65.00   M   12      2       3 4341   71.00   M   11      2       3 4342   85.00   F    1      2       3 4343   41.00   F    8      2       2 4344   49.00   M   10      2       2 4345    7.00   F    0      2       1 4346   69.00   M    9      2       3 4347   41.00   M    6      2       2 4348   53.00   M   10      2       2 4349   62.00   F    7      2       3 4350   58.00   M    0      2       2 4351   61.00   M    0      2       3 4352   53.00   M    2      2       2 4353   65.00   F    0      2       3 4354   68.00   F    3      2       3 4355   92.00   M    0      2       3 4356   60.00   M    6      2       3 4357   75.00   F    5      2       3 4358   78.00   M    6      2       3 4359   58.00   M    9      2       2 4360   76.00   F    9      2       3 4361   77.00   M    4      2       3 4362   78.00   M    4      2       3 4363   52.00   M    4      2       2 4364   60.00   M    1      2       3 4365   60.00   F    4      2       3 4366   57.00   M    7      2       2 4367   55.00   F    2      2       2 4368   64.00   M    6      2       3 4369   65.00   M    4      2       3 4370   72.00   M    6      2       3 4371   60.00   M    4      2       3 4372   50.00   M    2      2       2 4373   65.00   F    6      2       3 4374   60.00   M    5      2       3 4375   68.00   F    4      2       3 4376   60.00   F    1      2       3 4377   62.00   M    3      2       3 4378   50.00   M    0      2       2 4379   65.00   F    3      2       3 4380   73.00   M    1      2       3 4381   48.00   F    6      2       2 4382   59.00   M    2      2       2 4383   65.00   F    1      2       3 4384   68.00   M    0      2       3 4385   53.00   M    0      2       2 4386   77.00   M    0      2       3 4387   35.00   M    3      2       2 4388   68.00   M    4      2       3 4389   59.00   F    0      2       2 4390   38.00   M    0      2       2 4391   60.00   F    7      2       3 4392   82.00   M    3      2       3 4393   95.00   M   10      2       3 4394   60.00   M    0      2       3 4395   92.00   F    0      2       3 4396   70.00   M    0      2       3 4397   76.00   M    1      2       3 4398   29.00   M    1      2       2 4399   20.00   M    0      2       2 4400   85.00   M    0      2       3 4401   55.00   M    0      2       2 4402   75.00   F    0      2       3 4403   75.00   M    0      2       3 4404   48.00   M    6      2       2 4405   74.00   F    1      2       3 4406   61.00   M    6      2       3 4407   35.00   M    1      2       2 4408   60.00   F    1      2       3 4409   64.00   M    7      2       3 4410   30.00   M    0      2       2 4411   74.00   F    0      2       3 4412   58.00   F    0      2       2 4413   62.00   M    0      2       3 4414   86.00   M    1      2       3 4415   58.00   M    7      2       2 4416   80.00   M    1      2       3 4417   66.00   M   12      2       3 4418   80.00   M    3      2       3 4419   91.00   F   14      2       3 4420   57.00   M   13      2       2 4421   68.00   F    1      2       3 4422   75.00   M   12      2       3 4423   72.00   M    7      2       3 4424   76.00   M    6      2       3 4425   70.00   M   12      2       3 4426   70.00   M   11      2       3 4427   75.00   M   10      2       3 4428   56.00   F    9      2       2 4429   68.00   M   10      2       3 4430   70.00   M    8      2       3 4431   59.00   M    9      2       2 4432   52.00   M    6      2       2 4433   54.00   F    0      2       2 4434   75.00   M    7      2       3 4435   63.00   M   10      2       3 4436   60.00   M    1      2       3 4437   45.00   M    8      2       2 4438   75.00   M    5      2       3 4439   69.00   M    6      2       3 4440   70.00   M    9      2       3 4441   77.00   F    7      2       3 4442   62.00   M    6      2       3 4443   65.00   M    4      2       3 4444   64.00   M    2      2       3 4445   58.00   M    8      2       2 4446   56.00   M    4      2       2 4447   70.00   M    9      2       3 4448   49.00   M    3      2       2 4449   33.00   M    6      2       2 4450   84.00   M    5      2       3 4451   82.00   M    6      2       3 4452   43.00   M    5      2       2 4453   66.00   F    4      2       3 4454   60.00   M    5      2       3 4455   67.00   M    2      2       3 4456   82.00   M    2      2       3 4457   40.00   F    3      2       2 4458   70.00   F    3      2       3 4459   78.00   F    4      2       3 4460   60.00   F    2      2       3 4461   41.00   M    0      2       2 4462   78.00   M    3      2       3 4463   67.00   M    1      2       3 4464   42.00   M    0      2       2 4465   58.00   F    1      2       2 4466   60.00   M    0      2       3 4467   75.00   F    0      2       3 4468   55.00   F    0      2       2 4469   85.00   M    3      2       3 4470   64.00   F    4      2       3 4471   56.00   M    1      2       2 4472   38.00   M    2      2       2 4473   68.00   F    3      2       3 4474   65.00   M    0      2       3 4475   78.00   F    2      2       3 4476   74.00   F    1      2       3 4477   78.00   M    1      2       3 4478   67.00   M    1      2       3 4479   50.00   M    1      2       2 4480   84.00   F    1      2       3 4481   57.00   M    5      2       2 4482   45.00   M    0      2       2 4483   69.00   M    0      2       3 4484   55.00   F    2      2       2 4485   83.00   M    0      2       3 4486   48.00   M    1      2       2 4487   44.00   M    0      2       2 4488   63.00   M    0      2       3 4489   48.00   M    2      2       2 4490   80.00   M    0      2       3 4491   58.00   M    1      2       2 4492   72.00   M   18      2       3 4493   52.00   M    1      2       2 4494   47.00   M    2      2       2 4495   85.00   M    3      2       3 4496   65.00   M    4      2       3 4497   45.00   M    1      2       2 4498   49.00   F    2      2       2 4499   45.00   F    2      2       2 4500   43.00   M    1      2       2 4501   40.00   M    0      2       2 4502   65.00   M    0      2       3 4503   65.00   M    1      2       3 4504   63.00   F    3      2       3 4505   55.00   M    1      2       2 4506   65.00   F    2      2       3 4507   60.00   M   16      2       3 4508   54.00   M    2      2       2 4509   28.00   F   15      2       2 4510   68.00   F    5      2       3 4511   45.00   M    3      2       2 4512   55.00   M    0      2       2 4513   50.00   M   10      2       2 4514   55.00   M   10      2       2 4515   76.00   F    9      2       3 4516   60.00   M    2      2       3 4517   96.00   M   10      2       3 4518   72.00   M    1      2       3 4519   49.00   M    5      2       2 4520   56.00   M   13      2       2 4521   65.00   F    7      2       3 4522   70.00   F    8      2       3 4523   61.00   M    4      2       3 4524   35.00   M   10      2       2 4525   70.00   M    9      2       3 4526   65.00   F    3      2       3 4527   80.00   M    9      2       3 4528   32.00   M   10      2       2 4529   70.00   M    4      2       3 4530   76.00   M    4      2       3 4531   42.00   M    1      2       2 4532   55.00   F    4      2       2 4533   55.00   F    2      2       2 4534   55.00   M    1      2       2 4535   61.00   F    3      2       3 4536   48.00   M    1      2       2 4537   64.00   M    5      2       3 4538   74.00   M    5      2       3 4539   60.00   M    4      2       3 4540   80.00   F    2      2       3 4541   65.00   F    0      2       3 4542   60.00   M    6      2       3 4543   50.00   F    3      2       2 4544   54.00   M    1      2       2 4545   80.00   M    5      2       3 4546   55.00   M    4      2       2 4547   60.00   F    3      2       3 4548   66.00   M    3      2       3 4549   44.00   M    1      2       2 4550   86.00   F    1      2       3 4551   45.00   M    0      2       2 4552   73.00   M    1      2       3 4553   60.00   M    1      2       3 4554   38.00   F    0      2       2 4555   51.00   F    0      2       2 4556   52.00   F   17      2       2 4557   72.00   M    2      2       3 4558   70.00   M    1      2       3 4559   55.00   M    0      2       2 4560   60.00   F    0      2       3 4561   65.00   F    0      2       3 4562   60.00   M    0      2       3 4563   45.00   F    5      2       2 4564   74.00   M    0      2       3 4565   75.00   M    2      2       3 4566   63.00   M    0      2       3 4567   70.00   M    0      2       3 4568   58.00   M    1      2       2 4569   39.00   M    0      2       2 4570   60.00   M    1      2       3 4571   60.00   M    1      2       3 4572   89.00   F    1      2       3 4573   58.00   M    1      2       2 4574   77.00   F    3      2       3 4575   35.00   M   11      2       2 4576   65.00   M    0      2       3 4577   74.00   M   13      2       3 4578   56.00   M   11      2       2 4579   80.00   M    1      2       3 4580   51.00   M    2      2       2 4581   82.00   M   16      2       3 4582   76.00   F    1      2       3 4583   82.00   M   13      2       3 4584   31.00   M   11      2       2 4585   60.00   F    9      2       3 4586   78.00   F   10      2       3 4587   55.00   M    6      2       2 4588   76.00   M    8      2       3 4589   75.00   M    5      2       3 4590   60.00   M    0      2       3 4591   65.00   M    2      2       3 4592   45.00   M   10      2       2 4593   57.00   M   10      2       2 4594   74.00   M    6      2       3 4595   72.00   F   10      2       3 4596   40.00   M   10      2       2 4597   48.00   F    5      2       2 4598   60.00   M    4      2       3 4599   40.00   M    7      2       2 4600   65.00   F   10      2       3 4601   59.00   M    3      2       2 4602   32.00   M    1      2       2 4603   25.00   M    0      2       2 4604   70.00   M    7      2       3 4605   71.00   F    3      2       3 4606   57.00   M    1      2       2 4607   60.00   F    5      2       3 4608   65.00   M   19      2       3 4609   60.00   F   13      2       3 4610   92.00   F    8      2       3 4611   68.00   F    7      2       3 4612   50.00   M    7      2       2 4613   56.00   M    1      2       2 4614   47.00   M    5      2       2 4615   71.00   F    9      2       3 4616   54.00   F    8      2       2 4617   45.00   M    1      2       2 4618   38.00   M    9      2       2 4619   66.00   M    2      2       3 4620   72.00   M    6      2       3 4621   72.00   F    0      2       3 4622   65.00   F    3      2       3 4623   69.00   M    7      2       3 4624   68.00   M    4      2       3 4625   55.00   M    1      2       2 4626   78.00   M    6      2       3 4627   60.00   M    3      2       3 4628   61.00   F    6      2       3 4629   40.00   M    3      2       2 4630   67.00   M    0      2       3 4631   60.00   M    1      2       3 4632   40.00   F    7      2       2 4633   73.00   M    6      2       3 4634   88.00   F    5      2       3 4635   40.00   F    9      2       2 4636   35.00   F    4      2       2 4637   55.00   F    1      2       2 4638   75.00   F    1      2       3 4639   62.00   F    5      2       3 4640   65.00   F    7      2       3 4641   65.00   M    4      2       3 4642   73.00   M    2      2       3 4643   55.00   M    1      2       2 4644   57.00   M    7      2       2 4645   75.00   M    6      2       3 4646   58.00   M    7      2       2 4647   70.00   M    2      2       3 4648   46.00   M    2      2       2 4649   57.00   F    9      2       2 4650   49.00   F    8      2       2 4651   55.00   M    6      2       2 4652   51.00   F    4      2       2 4653   37.00   M    3      2       2 4654   46.00   M    0      2       2 4655   39.00   M    3      2       2 4656   55.00   M    1      2       2 4657   65.00   F    4      2       3 4658   40.00   F    0      2       2 4659   53.00   F    1      2       2 4660   38.00   F    1      2       2 4661   50.00   F    1      2       2 4662   85.00   M    1      2       3 4663   55.00   M    0      2       2 4664   60.00   F    1      2       3 4665   57.00   M    2      2       2 4666   50.00   F    2      2       2 4667   55.00   M    0      2       2 4668   58.00   F    3      2       2 4669   57.00   M    5      2       2 4670   58.00   F    0      2       2 4671   61.00   M    8      2       3 4672   63.00   F    2      2       3 4673   85.00   F    2      2       3 4674   77.00   F    2      2       3 4675   61.00   F    2      2       3 4676   55.00   M    0      2       2 4677   65.00   M    2      2       3 4678   42.00   M    3      2       2 4679   75.00   F    2      2       3 4680   60.00   F    3      2       3 4681   64.00   F    5      2       3 4682   65.00   M    0      2       3 4683   58.00   F    4      2       2 4684   65.00   M    0      2       3 4685   44.00   M    0      2       2 4686   85.00   M    0      2       3 4687   55.00   F    4      2       2 4688   26.00   M    1      2       2 4689   50.00   M    0      2       2 4690   54.00   M    0      2       2 4691   45.00   M    1      2       2 4692   62.00   M    0      2       3 4693   85.00   M    0      2       3 4694   85.00   F    0      2       3 4695   72.00   F    1      2       3 4696   72.00   M    1      2       3 4697   40.00   F    0      2       2 4698   70.00   F    0      2       3 4699   64.00   M    0      2       3 4700   60.00   F    0      2       3 4701   70.00   M    0      2       3 4702   75.00   M    1      2       3 4703   73.00   M    0      2       3 4704   54.00   M   14      2       2 4705   55.00   M   17      2       2 4706   69.00   F    4      2       3 4707   34.00   M   23      2       2 4708   38.00   F   19      2       2 4709   58.00   M    9      2       2 4710   61.00   M   17      2       3 4711   64.00   M    6      2       3 4712   63.00   F    2      2       3 4713   50.00   M    8      2       2 4714   64.00   M   13      2       3 4715   54.00   F   32      2       2 4716   86.00   M    7      2       3 4717   64.00   F    3      2       3 4718   71.00   M    1      2       3 4719   60.00   M   12      2       3 4720   70.00   M    1      2       3 4721   45.00   F   13      2       2 4722   35.00   M   13      2       2 4723   70.00   M   20      2       3 4724   72.00   M    1      2       3 4725   64.00   M    3      2       3 4726   53.00   M    0      2       2 4727   57.00   F    1      2       2 4728   58.00   M    1      2       2 4729   62.00   M    1      2       3 4730   71.00   M    5      2       3 4731   57.00   M   11      2       2 4732   50.00   F    8      2       2 4733   72.00   M    6      2       3 4734   65.00   M    6      2       3 4735   55.00   M   13      2       2 4736   50.00   M   10      2       2 4737   57.00   M    5      2       2 4738   54.00   F    7      2       2 4739   80.00   M    7      2       3 4740   40.00   M    5      2       2 4741   79.00   M    4      2       3 4742   56.00   M    0      2       2 4743   49.00   F    3      2       2 4744   46.00   M    0      2       2 4745   62.00   M    7      2       3 4746   58.00   F    7      2       2 4747   53.00   M    0      2       2 4748   45.00   M    7      2       2 4749   69.00   M   32      2       3 4750   65.00   F    8      2       3 4751   52.00   M    9      2       2 4752   67.00   M   10      2       3 4753   46.00   F    0      2       2 4754   76.00   F    2      2       3 4755   50.00   F    8      2       2 4756   60.00   F    1      2       3 4757   80.00   M    1      2       3 4758   45.00   M    0      2       2 4759   78.00   M    1      2       3 4760   72.00   M    0      2       3 4761   82.00   F    0      2       3 4762   45.00   M    0      2       2 4763   62.00   M    0      2       3 4764   55.00   F    4      2       2 4765   60.00   F    0      2       3 4766   73.00   M   10      2       3 4767   48.00   F   13      2       2 4768   42.00   M    8      2       2 4769   65.00   M    3      2       3 4770   49.00   M    0      2       2 4771   73.00   F    1      2       3 4772   78.00   M    7      2       3 4773   83.00   F    1      2       3 4774   47.00   M    0      2       2 4775   74.00   M    4      2       3 4776   42.00   M    3      2       2 4777   58.00   M   10      2       2 4778   75.00   F    0      2       3 4779   43.00   F    3      2       2 4780   61.00   M    1      2       3 4781   35.00   M    0      2       2 4782   94.00   M    5      2       3 4783   62.00   M    3      2       3 4784   50.00   M    6      2       2 4785   68.00   M    6      2       3 4786   48.00   M    8      2       2 4787   53.00   M    4      2       2 4788   60.00   F    8      2       3 4789   50.00   M    6      2       2 4790   56.00   M    2      2       2 4791   72.00   M    5      2       3 4792   60.00   F    5      2       3 4793   52.00   M    4      2       2 4794   58.00   F    6      2       2 4795   73.00   F    3      2       3 4796   53.00   F    2      2       2 4797   65.00   M    4      2       3 4798   92.00   M    1      2       3 4799   59.00   M    0      2       2 4800   61.00   M    6      2       3 4801   63.00   M    5      2       3 4802   50.00   M    6      2       2 4803   63.00   M    0      2       3 4804   84.00   M    3      2       3 4805   75.00   F    5      2       3 4806   65.00   M    3      2       3 4807   52.00   M    5      2       2 4808   42.00   M    3      2       2 4809   48.00   M    2      2       2 4810   70.00   M    5      2       3 4811   61.00   F    1      2       3 4812   56.00   M    1      2       2 4813   75.00   F    2      2       3 4814   65.00   M    1      2       3 4815   30.00   F    1      2       2 4816   78.00   M    3      2       3 4817   78.00   F    9      2       3 4818   69.00   M    1      2       3 4819   30.00   F    3      2       2 4820   62.00   F    2      2       3 4821   52.00   M    2      2       2 4822   55.00   F    6      2       2 4823   73.00   M    6      2       3 4824   95.00   M    1      2       3 4825   72.00   M    3      2       3 4826   74.00   M    0      2       3 4827   75.00   M    1      2       3 4828   55.00   M    1      2       2 4829   76.00   M    0      2       3 4830   44.00   F    0      2       2 4831   64.00   M    2      2       3 4832   58.00   M    0      2       2 4833   50.00   M    0      2       2 4834   59.00   F    1      2       2 4835   45.00   F    0      2       2 4836   60.00   F    0      2       3 4837   67.00   M    0      2       3 4838   70.00   M    1      2       3 4839   67.00   M    3      2       3 4840   80.00   M    3      2       3 4841   66.00   M    2      2       3 4842   87.00   M    1      2       3 4843   50.00   M    1      2       2 4844   64.00   M    0      2       3 4845   61.00   M    0      2       3 4846   58.00   M    0      2       2 4847   80.00   F    0      2       3 4848   56.00   M    1      2       2 4849   38.00   M    2      2       2 4850   96.00   F    0      2       3 4851   52.00   M    0      2       2 4852   60.00   M    4      2       3 4853   56.00   M    8      2       2 4854   55.00   F    0      2       2 4855   75.00   F   11      2       3 4856   45.00   F    9      2       2 4857   81.00   M    8      2       3 4858   55.00   M   20      2       2 4859   56.00   M    7      2       2 4860   56.00   F    6      2       2 4861   33.00   M   16      2       2 4862   60.00   M   12      2       3 4863   92.00   F   14      2       3 4864   66.00   M    3      2       3 4865   58.00   F   11      2       2 4866   44.00   M   12      2       2 4867   78.00   M   11      2       3 4868   65.00   F    4      2       3 4869   32.00   M   14      2       2 4870   65.00   M    6      2       3 4871   70.00   F    3      2       3 4872   73.00   M    6      2       3 4873   60.00   F    6      2       3 4874   37.00   M    8      2       2 4875   56.00   M   12      2       2 4876   53.00   F    4      2       2 4877   88.00   M   13      2       3 4878   55.00   M   10      2       2 4879   41.00   F    0      2       2 4880   70.00   M    9      2       3 4881   31.00   M    5      2       2 4882   60.00   M    6      2       3 4883   46.00   M   11      2       2 4884   65.00   M    7      2       3 4885   60.00   F   11      2       3 4886   51.00   F    2      2       2 4887   79.00   M   10      2       3 4888   62.00   M    4      2       3 4889   72.00   M   12      2       3 4890   70.00   M    9      2       3 4891   67.00   F    4      2       3 4892   42.00   M    4      2       2 4893   65.00   F    3      2       3 4894   40.00   M    7      2       2 4895   81.00   F    7      2       3 4896   69.00   F   10      2       3 4897   50.00   F    7      2       2 4898   84.00   M    6      2       3 4899   65.00   M    6      2       3 4900   66.00   M    6      2       3 4901   31.00   M    7      2       2 4902   46.00   M    8      2       2 4903   60.00   M    5      2       3 4904   63.00   F    2      2       3 4905   58.00   F    5      2       2 4906   70.00   F    1      2       3 4907   50.00   F    0      2       2 4908   49.00   M    8      2       2 4909   62.00   M    0      2       3 4910   36.00   M    0      2       2 4911   78.00   F    7      2       3 4912   66.00   M    5      2       3 4913   67.00   M    3      2       3 4914   63.00   M    7      2       3 4915   75.00   M    6      2       3 4916   58.00   M    2      2       2 4917   76.00   M    4      2       3 4918   70.00   F    1      2       3 4919   72.00   M    3      2       3 4920   73.00   M    7      2       3 4921   61.00   M    5      2       3 4922   57.00   M    4      2       2 4923   64.00   M    1      2       3 4924   64.00   F    1      2       3 4925   58.00   M    6      2       2 4926   65.00   M    0      2       3 4927   60.00   M    0      2       3 4928   58.00   M    4      2       2 4929   70.00   M    5      2       3 4930   88.00   F    1      2       3 4931   67.00   M   12      2       3 4932   60.00   M    0      2       3 4933   78.00   F    1      2       3 4934   75.00   F    1      2       3 4935   65.00   M    2      2       3 4936   24.00   M    5      2       2 4937   67.00   M    1      2       3 4938   65.00   F    0      2       3 4939   55.00   M    0      2       2 4940   40.00   M    0      2       2 4941   65.00   M    1      2       3 4942   55.00   M    3      2       2 4943   58.00   M    3      2       2 4944   62.00   M    1      2       3 4945   49.00   M    0      2       2 4946   71.00   M    1      2       3 4947   70.00   M    0      2       3 4948   57.00   M    0      2       2 4949   65.00   F    2      2       3 4950   55.00   M    1      2       2 4951   85.00   M    1      2       3 4952   60.00   M    4      2       3 4953   60.00   F    2      2       3 4954   60.00   M    7      2       3 4955   58.00   M    1      2       2 4956   57.00   M    0      2       2 4957   65.00   M    6      2       3 4958   75.00   F    2      2       3 4959   55.00   M    1      2       2 4960   65.00   F    3      2       3 4961   65.00   F    0      2       3 4962   50.00   M    0      2       2 4963   75.00   F    1      2       3 4964   45.00   F    1      2       2 4965   56.00   M    0      2       2 4966   46.00   M    1      2       2 4967   65.00   F    3      2       3 4968   54.00   M    5      2       2 4969   32.00   F    1      2       2 4970   60.00   F    1      2       3 4971   68.00   M    6      2       3 4972   70.00   M    1      2       3 4973   35.00   F    0      2       2 4974   68.00   M    2      2       3 4975   62.00   F    0      2       3 4976   75.00   M    1      2       3 4977   40.00   M    0      2       2 4978   73.00   M    0      2       3 4979   60.00   F    0      2       3 4980   52.00   F    0      2       2 4981   55.00   F    0      2       2 4982   36.00   M    0      2       2 4983   30.00   M    2      2       2 4984   70.00   M    1      2       3 4985   59.00   M    8      2       2 4986   77.00   M    0      2       3 4987   69.00   M    5      2       3 4988   60.00   M   14      2       3 4989   53.00   F    2      2       2 4990   54.00   F   24      2       2 4991   82.00   M    5      2       3 4992   44.00   M    7      2       2 4993   55.00   F    6      2       2 4994   54.00   F    4      2       2 4995   45.00   F    3      2       2 4996   75.00   M    8      2       3 4997   45.00   F    8      2       2 4998   76.00   F    3      2       3 4999   66.00   M   14      2       3 5000   75.00   M    3      2       3 5001   48.00   M   14      2       2 5002   54.00   M   10      2       2 5003   63.00   F    0      2       3 5004   55.00   F    6      2       2 5005   69.00   F    5      2       3 5006   85.00   M   15      2       3 5007   64.00   M    8      2       3 5008   63.00   M    0      2       3 5009   40.00   M   20      2       2 5010   57.00   M   23      2       2 5011   26.00   F    0      2       2 5012   57.00   M    8      2       2 5013   54.00   M    3      2       2 5014   73.00   F   12      2       3 5015   60.00   M    3      2       3 5016   68.00   M    4      2       3 5017   55.00   M    7      2       2 5018   34.00   M    8      2       2 5019   48.00   M    0      2       2 5020   70.00   M    5      2       3 5021   58.00   M    0      2       2 5022   66.00   F    5      2       3 5023   70.00   M    2      2       3 5024   34.00   M   17      2       2 5025   74.00   F   17      2       3 5026   67.00   M   16      2       3 5027   76.00   M    0      2       3 5028   67.00   F   13      2       3 5029   68.00   M   12      2       3 5030   56.00   M   19      2       2 5031   75.00   F   15      2       3 5032   90.00   M   14      2       3 5033   51.00   M   12      2       2 5034   33.00   F    1      2       2 5035   72.00   M   11      2       3 5036   58.00   M   12      2       2 5037   55.00   M   10      2       2 5038   75.00   F    2      2       3 5039   61.00   M   12      2       3 5040   55.00   F    1      2       2 5041   20.00   F    0      2       2 5042   56.00   F    2      2       2 5043   72.00   M   11      2       3 5044   63.00   M    4      2       3 5045   55.00   F    6      2       2 5046   78.00   M   12      2       3 5047   65.00   M    1      2       3 5048   60.00   M   11      2       3 5049   58.00   F    4      2       2 5050   70.00   M    3      2       3 5051   65.00   M    8      2       3 5052   67.00   F    9      2       3 5053   65.00   M    1      2       3 5054   86.00   M   10      2       3 5055   40.00   M    0      2       2 5056   43.00   M    0      2       2 5057   70.00   M    3      2       3 5058   65.00   F    0      2       3 5059   85.00   F    7      2       3 5060   70.00   M    1      2       3 5061   52.00   F    5      2       2 5062   80.00   F    2      2       3 5063   39.00   M    1      2       2 5064   37.00   M    5      2       2 5065   67.00   M    1      2       3 5066   70.00   M    1      2       3 5067   57.00   F    5      2       2 5068   80.00   F    6      2       3 5069   74.00   F    7      2       3 5070   24.00   M    4      2       2 5071   50.00   F    7      2       2 5072   57.00   M    6      2       2 5073   46.00   M    3      2       2 5074   68.00   M    3      2       3 5075   68.00   M    1      2       3 5076   32.00   M    8      2       2 5077   75.00   M    3      2       3 5078   28.00   F    4      2       2 5079   44.00   M    3      2       2 5080   57.00   M    0      2       2 5081   53.00   F    5      2       2 5082   55.00   F    5      2       2 5083   67.00   M    0      2       3 5084   71.00   M    3      2       3 5085   50.00   M    4      2       2 5086   60.00   M    0      2       3 5087   76.00   M    0      2       3 5088   70.00   F    0      2       3 5089   64.00   F    1      2       3 5090   74.00   F    4      2       3 5091   55.00   M    2      2       2 5092   47.00   M    1      2       2 5093   72.00   M    1      2       3 5094   63.00   M    1      2       3 5095   50.00   M    5      2       2 5096   55.00   F    3      2       2 5097   57.00   M    1      2       2 5098   50.00   M    0      2       2 5099   64.00   M    0      2       3 5100   61.00   M    5      2       3 5101   60.00   M    3      2       3 5102   50.00   M    2      2       2 5103   58.00   F    0      2       2 5104   73.00   F    2      2       3 5105   73.00   M    1      2       3 5106   80.00   F    2      2       3 5107   75.00   F    0      2       3 5108   60.00   F    1      2       3 5109   58.00   M    0      2       2 5110   52.00   M    1      2       2 5111   32.00   M   10      2       2 5112   39.00   F    0      2       2 5113   60.00   M    0      2       3 5114   73.00   F    0      2       3 5115   68.00   M    0      2       3 5116   72.00   M    1      2       3 5117   74.00   F    1      2       3 5118   70.00   F    0      2       3 5119   62.00   M    0      2       3 5120   56.00   M    1      2       2 5121   99.00   M    1      2       3 5122   70.00   F    0      2       3 5123   70.00   M    1      2       3 5124   82.00   M    0      2       3 5125   70.00   F    0      2       3 5126   66.00   M    0      2       3 5127   49.00   M    5      2       2 5128   49.00   M    8      2       2 5129   68.00   F    1      2       3 5130   41.00   M    0      2       2 5131   50.00   M    3      2       2 5132   70.00   M   17      2       3 5133   42.00   F    4      2       2 5134   54.00   F   16      2       2 5135   85.00   M   14      2       3 5136   85.00   M   17      2       3 5137   44.00   M   16      2       2 5138   63.00   F   15      2       3 5139   61.00   M    2      2       3 5140   72.00   M    6      2       3 5141   46.00   F   15      2       2 5142   68.00   M   11      2       3 5143   65.00   F   14      2       3 5144   65.00   M    3      2       3 5145   66.00   M   15      2       3 5146   88.00   F    7      2       3 5147   50.00   F    9      2       2 5148   40.00   M   10      2       2 5149   45.00   F   11      2       2 5150   55.00   M    5      2       2 5151   53.00   F    0      2       2 5152   81.00   M    0      2       3 5153   63.00   M    9      2       3 5154   53.00   M    5      2       2 5155   65.00   M    1      2       3 5156   66.00   M   10      2       3 5157   35.00   M   10      2       2 5158   55.00   M    8      2       2 5159   70.00   F    6      2       3 5160   74.00   M    6      2       3 5161   70.00   M   10      2       3 5162   50.00   F    9      2       2 5163   55.00   M    9      2       2 5164   69.00   M    9      2       3 5165   74.00   M    7      2       3 5166   73.00   M   11      2       3 5167   60.00   F   10      2       3 5168   62.00   M    4      2       3 5169   73.00   M    7      2       3 5170   75.00   M    2      2       3 5171   53.00   M    7      2       2 5172   54.00   M    2      2       2 5173   67.00   M    8      2       3 5174   35.00   M    6      2       2 5175   61.00   M   12      2       3 5176   77.00   M    0      2       3 5177   47.00   M    3      2       2 5178   67.00   M    8      2       3 5179   62.00   M    8      2       3 5180   61.00   M    7      2       3 5181   70.00   M    6      2       3 5182   53.00   M    8      2       2 5183   61.00   M    6      2       3 5184   65.00   M    2      2       3 5185   59.00   M    1      2       2 5186   66.00   M    5      2       3 5187   89.00   F    5      2       3 5188   80.00   F    5      2       3 5189   49.00   F    6      2       2 5190   73.00   M    8      2       3 5191   55.00   M    3      2       2 5192   75.00   M    6      2       3 5193   46.00   M    7      2       2 5194   60.00   F    8      2       3 5195   67.00   M    6      2       3 5196   90.00   M    0      2       3 5197   40.00   F    9      2       2 5198   83.00   F    5      2       3 5199   44.00   M    6      2       2 5200   60.00   F    7      2       3 5201   66.00   F    1      2       3 5202   57.00   M    7      2       2 5203   59.00   M    4      2       2 5204   60.00   M    1      2       3 5205   85.00   M    7      2       3 5206   70.00   M    5      2       3 5207   36.00   M    6      2       2 5208   37.00   M    1      2       2 5209   74.00   F    4      2       3 5210   79.00   F    6      2       3 5211   68.00   M    4      2       3 5212   55.00   F    1      2       2 5213   78.00   M    1      2       3 5214   62.00   F   15      2       3 5215   86.00   M    0      2       3 5216   43.00   M   10      2       2 5217   58.00   M    1      2       2 5218   62.00   M    0      2       3 5219   64.00   F    0      2       3 5220   74.00   M    0      2       3 5221   62.00   M    3      2       3 5222   31.00   M    6      2       2 5223   59.00   M    5      2       2 5224   76.00   M    0      2       3 5225   65.00   M    1      2       3 5226   48.00   F    4      2       2 5227   45.00   F    4      2       2 5228   68.00   F    3      2       3 5229   65.00   M    2      2       3 5230   45.00   F    3      2       2 5231   70.00   M    1      2       3 5232   44.00   M    1      2       2 5233   56.00   M    2      2       2 5234   65.00   M    5      2       3 5235   65.00   M    3      2       3 5236   68.00   F    2      2       3 5237   55.00   M    2      2       2 5238   70.00   M    1      2       3 5239   68.00   M    0      2       3 5240   58.00   F    1      2       2 5241   48.00   M    1      2       2 5242   68.00   F    2      2       3 5243   36.00   M    3      2       2 5244   66.00   M   13      2       3 5245   58.00   F    1      2       2 5246   63.00   M    1      2       3 5247   75.00   M    0      2       3 5248   59.00   F    1      2       2 5249   65.00   F    1      2       3 5250   55.00   M    2      2       2 5251   45.00   F    2      2       2 5252   75.00   M    1      2       3 5253   58.00   M    1      2       2 5254   63.00   F    3      2       3 5255   73.00   F    2      2       3 5256   80.00   F    0      2       3 5257   63.00   M    0      2       3 5258   63.00   F    3      2       3 5259   44.00   M    0      2       2 5260   63.00   M    1      2       3 5261   58.00   M    1      2       2 5262   54.00   M    2      2       2 5263   70.00   F    0      2       3 5264   60.00   M   27      2       3 5265   57.00   M    4      2       2 5266   63.00   M   23      2       3 5267   78.00   M   18      2       3 5268   58.00   M   16      2       2 5269   70.00   M   15      2       3 5270   55.00   F   15      2       2 5271   56.00   M    4      2       2 5272   80.00   M    5      2       3 5273   55.00   M   15      2       2 5274   60.00   M    9      2       3 5275   44.00   M   14      2       2 5276   55.00   M   10      2       2 5277   78.00   M   11      2       3 5278   47.00   M   15      2       2 5279   70.00   M    0      2       3 5280   63.00   M    9      2       3 5281   72.00   F    2      2       3 5282   65.00   M    2      2       3 5283   65.00   M   11      2       3 5284   74.00   F    1      2       3 5285   63.00   M   13      2       3 5286   50.00   M    8      2       2 5287   52.00   M    8      2       2 5288   84.00   M    8      2       3 5289   59.00   M    3      2       2 5290   78.00   F    7      2       3 5291   64.00   F    8      2       3 5292   68.00   F   11      2       3 5293   68.00   F    6      2       3 5294   60.00   F    7      2       3 5295   65.00   M    9      2       3 5296   47.00   M    8      2       2 5297   60.00   F    6      2       3 5298   42.00   F   10      2       2 5299   64.00   F    7      2       3 5300   47.00   F    3      2       2 5301   72.00   M    9      2       3 5302   70.00   M    6      2       3 5303   68.00   M   11      2       3 5304   64.00   F    7      2       3 5305   59.00   M    3      2       2 5306   72.00   M    5      2       3 5307   75.00   M    2      2       3 5308   59.00   F    1      2       2 5309   50.00   M   10      2       2 5310   51.00   M    5      2       2 5311   65.00   M   12      2       3 5312   81.00   M    3      2       3 5313   50.00   F    1      2       2 5314   70.00   M    4      2       3 5315   70.00   M    5      2       3 5316   36.00   M    6      2       2 5317   66.00   M    4      2       3 5318   62.00   M    7      2       3 5319   63.00   M    0      2       3 5320   74.00   M    5      2       3 5321   65.00   M    1      2       3 5322   67.00   M    1      2       3 5323   57.00   M    0      2       2 5324   65.00   M    8      2       3 5325   70.00   M    4      2       3 5326   70.00   M    2      2       3 5327   50.00   F    1      2       2 5328   75.00   M    3      2       3 5329   75.00   F    0      2       3 5330   76.00   M    5      2       3 5331   75.00   M    2      2       3 5332   39.00   F    3      2       2 5333   64.00   M   23      2       3 5334   71.00   M    0      2       3 5335   44.00   F    4      2       2 5336   80.00   M    1      2       3 5337   70.00   M    1      2       3 5338   45.00   M    0      2       2 5339   53.00   M    3      2       2 5340   60.00   M    0      2       3 5341   72.00   F    4      2       3 5342   61.00   M    3      2       3 5343   75.00   F    0      2       3 5344   76.00   M    1      2       3 5345   62.00   F    1      2       3 5346   69.00   M    1      2       3 5347   37.00   M    1      2       2 5348   47.00   M    0      2       2 5349   80.00   M    0      2       3 5350   32.00   M    1      2       2 5351   80.00   M    5      2       3 5352   42.00   M    1      2       2 5353   63.00   M    2      2       3 5354   55.00   M    0      2       2 5355   70.00   M    1      2       3 5356   56.00   M    0      2       2 5357   70.00   M    0      2       3 5358   69.00   M    0      2       3 5359   78.00   F    2      2       3 5360   74.00   F    2      2       3 5361   80.00   M    0      2       3 5362   39.00   M    1      2       2 5363   60.00   F   13      2       3 5364   70.00   F    2      2       3 5365   70.00   M    2      2       3 5366   48.00   F    2      2       2 5367   50.00   M    9      2       2 5368   14.00   M    0      2       1 5369   17.00   F    0      2       1 5370   70.00   M    1      2       3 5371   44.00   F    1      2       2 5372   66.00   M    0      2       3 5373   34.00   M    0      2       2 5374   72.00   M    1      2       3 5375   50.00   M    0      2       2 5376   60.00   M    0      2       3 5377   70.00   F    1      2       3 5378   60.00   M   15      2       3 5379   43.00   M   10      2       2 5380   60.00   F   17      2       3 5381   68.00   M   20      2       3 5382   65.00   M   11      2       3 5383   59.00   M    8      2       2 5384   85.00   M    3      2       3 5385   56.00   M   22      2       2 5386   54.00   F   17      2       2 5387   58.00   F    6      2       2 5388   50.00   M    2      2       2 5389   60.00   M   13      2       3 5390   68.00   M   13      2       3 5391   60.00   F    2      2       3 5392   69.00   F    0      2       3 5393   50.00   M   15      2       2 5394   50.00   M    0      2       2 5395   84.00   M   18      2       3 5396   54.00   M    3      2       2 5397   73.00   M    3      2       3 5398   60.00   M   12      2       3 5399   94.00   F   17      2       3 5400   63.00   F   19      2       3 5401   65.00   F   12      2       3 5402   75.00   F    1      2       3 5403   60.00   M   14      2       3 5404   58.00   F    1      2       2 5405   75.00   M    1      2       3 5406   76.00   M    8      2       3 5407   55.00   F    2      2       2 5408   68.00   F    0      2       3 5409   60.00   M    4      2       3 5410   65.00   M   11      2       3 5411   50.00   F    0      2       2 5412   62.00   M   11      2       3 5413   65.00   F    2      2       3 5414   69.00   M    0      2       3 5415   37.00   M    6      2       2 5416   60.00   M   10      2       3 5417   48.00   F   16      2       2 5418   44.00   M    3      2       2 5419   80.00   F    6      2       3 5420   49.00   F    9      2       2 5421   62.00   F    7      2       3 5422   79.00   M    1      2       3 5423   49.00   M   10      2       2 5424   53.00   F    0      2       2 5425   91.00   F    6      2       3 5426   52.00   F    5      2       2 5427   75.00   M    4      2       3 5428   59.00   F    6      2       2 5429   65.00   M    0      2       3 5430   35.00   F    2      2       2 5431   43.00   M    5      2       2 5432   48.00   M    6      2       2 5433   59.00   F    0      2       2 5434   40.00   F    4      2       2 5435   60.00   M    0      2       3 5436   60.00   M   10      2       3 5437   65.00   F    5      2       3 5438   73.00   M    5      2       3 5439   48.00   M    2      2       2 5440   35.00   M    5      2       2 5441   70.00   F    4      2       3 5442   63.00   M    3      2       3 5443   62.00   F    2      2       3 5444   68.00   M    2      2       3 5445   70.00   F    4      2       3 5446   45.00   M    4      2       2 5447   72.00   F    4      2       3 5448   65.00   M    3      2       3 5449   73.00   M    3      2       3 5450   60.00   F    1      2       3 5451   44.00   M    4      2       2 5452   55.00   M    3      2       2 5453   63.00   M    0      2       3 5454   70.00   M    3      2       3 5455   60.00   M    2      2       3 5456   39.00   M    3      2       2 5457   31.00   M    0      2       2 5458   49.00   M    0      2       2 5459   58.00   M    2      2       2 5460   69.00   M    2      2       3 5461   48.00   M    0      2       2 5462   60.00   F    2      2       3 5463   75.00   M    5      2       3 5464   58.00   M    9      2       2 5465   51.00   M    3      2       2 5466   78.00   M    3      2       3 5467   55.00   F    1      2       2 5468   64.00   M    0      2       3 5469   88.00   F    1      2       3 5470   64.00   M    1      2       3 5471   60.00   M    0      2       3 5472   50.00   M    1      2       2 5473   60.00   F    0      2       3 5474   38.00   M    1      2       2 5475   63.00   M    1      2       3 5476   50.00   M    1      2       2 5477   50.00   F    1      2       2 5478   68.00   M    2      2       3 5479   80.00   M    1      2       3 5480   35.00   M    1      2       2 5481   73.00   M    1      2       3 5482   82.00   M    2      2       3 5483   48.00   M   27      2       2 5484   60.00   M   17      2       3 5485   69.00   M   14      2       3 5486   62.00   M   10      2       3 5487   58.00   M   18      2       2 5488   76.00   M    9      2       3 5489   63.00   M   15      2       3 5490   35.00   F   22      2       2 5491   39.00   F   16      2       2 5492   75.00   M   10      2       3 5493   55.00   F   17      2       2 5494   60.00   F   12      2       3 5495   67.00   F   13      2       3 5496   60.00   F    3      2       3 5497   58.00   F    6      2       2 5498   70.00   M    3      2       3 5499   65.00   M    0      2       3 5500   80.00   M   11      2       3 5501   67.00   M   12      2       3 5502   48.00   M   15      2       2 5503   55.00   F    5      2       2 5504   63.00   F    4      2       3 5505   55.00   M   10      2       2 5506   68.00   F    7      2       3 5507   40.00   F   10      2       2 5508   90.00   F    8      2       3 5509   73.00   M    8      2       3 5510   72.00   M    8      2       3 5511   62.00   F   12      2       3 5512   84.00   M    8      2       3 5513   74.00   M   14      2       3 5514   60.00   M    6      2       3 5515   48.00   F    7      2       2 5516   58.00   M    5      2       2 5517   70.00   F   10      2       3 5518   80.00   M    6      2       3 5519   65.00   M    6      2       3 5520   41.00   F    7      2       2 5521   52.00   M    2      2       2 5522   84.00   M    6      2       3 5523   48.00   M    0      2       2 5524   56.00   F    5      2       2 5525   61.00   F    3      2       3 5526   60.00   M    2      2       3 5527   55.00   M    6      2       2 5528   65.00   M    4      2       3 5529   58.00   F    3      2       2 5530   65.00   M    5      2       3 5531   73.00   F    5      2       3 5532   60.00   M    4      2       3 5533   68.00   F    2      2       3 5534   49.00   F    0      2       2 5535   77.00   M   15      2       3 5536   65.00   F    6      2       3 5537   25.00   M   11      2       2 5538   70.00   F    1      2       3 5539   63.00   F    1      2       3 5540   70.00   F    2      2       3 5541   60.00   M    2      2       3 5542   47.00   M    7      2       2 5543   89.00   M    7      2       3 5544   68.00   F    2      2       3 5545   68.00   M    4      2       3 5546   53.00   M    9      2       2 5547   75.00   M    4      2       3 5548   41.00   M    0      2       2 5549   74.00   M    0      2       3 5550   65.00   M    5      2       3 5551   70.00   M    0      2       3 5552   40.00   M    2      2       2 5553   47.00   M    2      2       2 5554   55.00   M    2      2       2 5555   65.00   M    2      2       3 5556   66.00   M    4      2       3 5557   53.00   F    0      2       2 5558   61.00   M    2      2       3 5559   80.00   F    2      2       3 5560   75.00   M    2      2       3 5561   52.00   F    2      2       2 5562   87.00   M    3      2       3 5563   64.00   M    0      2       3 5564   58.00   M    1      2       2 5565   67.00   F    1      2       3 5566   50.00   F    4      2       2 5567   47.00   F    1      2       2 5568   55.00   F    0      2       2 5569   69.00   F    0      2       3 5570   60.00   M    0      2       3 5571   71.00   M    1      2       3 5572   48.00   M    4      2       2 5573   85.00   M    0      2       3 5574   87.00   M    0      2       3 5575   92.00   M    3      2       3 5576   82.00   M    2      2       3 5577   80.00   F    0      2       3 5578   60.00   F    2      2       3 5579   43.00   M    1      2       2 5580   72.00   M    2      2       3 5581   79.00   F    1      2       3 5582   82.00   M    0      2       3 5583   65.00   M    1      2       3 5584   67.00   M    2      2       3 5585   54.00   M    0      2       2 5586   63.00   M    1      2       3 5587   65.00   F    1      2       3 5588   56.00   M    0      2       2 5589   35.00   M    0      2       2 5590   84.00   M    4      2       3 5591   52.00   M    2      2       2 5592   50.00   F    0      2       2 5593   62.00   F    1      2       3 5594   73.00   M    0      2       3 5595   69.00   M    1      2       3 5596   40.00   F    3      2       2 5597   44.00   M    3      2       2 5598   62.00   M    1      2       3 5599   76.00   F   18      2       3 5600   46.00   M   17      2       2 5601   63.00   F   19      2       3 5602   38.00   F    4      2       2 5603   70.00   M    0      2       3 5604   45.00   M    0      2       2 5605   59.00   M   50      2       2 5606   75.00   M   11      2       3 5607   60.00   F   18      2       3 5608   78.00   F   41      2       3 5609   70.00   M    4      2       3 5610   60.00   M    1      2       3 5611   60.00   M    0      2       3 5612   80.00   M    0      2       3 5613   50.00   F   22      2       2 5614   53.00   M    1      2       2 5615   57.00   M   21      2       2 5616   45.00   F    1      2       2 5617   61.00   M    5      2       3 5618   60.00   M    4      2       3 5619   70.00   M    3      2       3 5620   87.00   F    7      2       3 5621   50.00   M    8      2       2 5622   64.00   M   21      2       3 5623   52.00   M    0      2       2 5624   56.00   M    9      2       2 5625   68.00   M    1      2       3 5626   75.00   F    9      2       3 5627   27.00   M   10      2       2 5628   64.00   M    7      2       3 5629   75.00   F    5      2       3 5630   68.00   M   17      2       3 5631   51.00   M   13      2       2 5632   58.00   M   16      2       2 5633   39.00   F   10      2       2 5634   58.00   M   11      2       2 5635   58.00   M    2      2       2 5636   63.00   M   14      2       3 5637   46.00   F   30      2       2 5638   61.00   M   14      2       3 5639   45.00   M   13      2       2 5640   62.00   F    6      2       3 5641   52.00   F    0      2       2 5642   65.00   M    0      2       3 5643   75.00   M    0      2       3 5644   59.00   F   10      2       2 5645   60.00   M   11      2       3 5646   55.00   M    1      2       2 5647   70.00   F   10      2       3 5648   60.00   F    5      2       3 5649   69.00   M   17      2       3 5650   68.00   F    8      2       3 5651   78.00   F    0      2       3 5652   78.00   M    8      2       3 5653   65.00   F   13      2       3 5654   75.00   M    8      2       3 5655   30.00   F   11      2       2 5656   85.00   F    2      2       3 5657   72.00   F    7      2       3 5658   89.00   M    3      2       3 5659   48.00   M   14      2       2 5660   65.00   M    0      2       3 5661   42.00   M    0      2       2 5662   82.00   M    7      2       3 5663   55.00   M    0      2       2 5664   28.00   F    1      2       2 5665   60.00   M    2      2       3 5666   65.00   F    5      2       3 5667   45.00   M    3      2       2 5668   73.00   F    5      2       3 5669   70.00   M    8      2       3 5670   70.00   M    9      2       3 5671   60.00   M    7      2       3 5672   66.00   F    2      2       3 5673   82.00   F    7      2       3 5674   79.00   F   10      2       3 5675   67.00   M    3      2       3 5676   65.00   M    1      2       3 5677   62.00   M    3      2       3 5678   78.00   M    7      2       3 5679   73.00   M    4      2       3 5680   66.00   M    4      2       3 5681   42.00   M    4      2       2 5682   58.00   M    2      2       2 5683   65.00   M    6      2       3 5684   84.00   M    0      2       3 5685   60.00   M    4      2       3 5686   45.00   M    0      2       2 5687   65.00   M    7      2       3 5688   89.00   M    4      2       3 5689   40.00   F    1      2       2 5690   53.00   M    2      2       2 5691   60.00   M    3      2       3 5692   75.00   M    4      2       3 5693   60.00   M    4      2       3 5694   86.00   M    2      2       3 5695   30.00   M    3      2       2 5696   65.00   F    3      2       3 5697   75.00   F    1      2       3 5698   70.00   M    5      2       3 5699   50.00   M    0      2       2 5700   35.00   M    2      2       2 5701   48.00   F    2      2       2 5702   75.00   M    1      2       3 5703   72.00   M    2      2       3 5704   72.00   M    3      2       3 5705   36.00   M    2      2       2 5706   74.00   M    3      2       3 5707   90.00   F    1      2       3 5708   60.00   M    2      2       3 5709   45.00   M    3      2       2 5710   83.00   M    3      2       3 5711   58.00   M    0      2       2 5712   49.00   M    0      2       2 5713   50.00   M    0      2       2 5714   83.00   M    0      2       3 5715   48.00   F    0      2       2 5716   40.00   M    0      2       2 5717   40.00   F    0      2       2 5718   85.00   M    1      2       3 5719   75.00   M    2      2       3 5720   55.00   F    0      2       2 5721   62.00   F    1      2       3 5722   68.00   M    1      2       3 5723   70.00   M    3      2       3 5724   70.00   M    1      2       3 5725   35.00   F    0      2       2 5726   80.00   M    3      2       3 5727   71.00   M    0      2       3 5728   53.00   M    0      2       2 5729   46.00   M    1      2       2 5730   53.00   M    2      2       2 5731   50.00   M    3      2       2 5732   60.00   M    1      2       3 5733   52.00   M    0      2       2 5734   77.00   F   24      2       3 5735   70.00   M    6      2       3 5736   12.00   F    1      2       1 5737   81.00   M    2      2       3 5738   84.00   M   13      2       3 5739   51.00   M    1      2       2 5740   69.00   M   14      2       3 5741   68.00   F    6      2       3 5742   60.00   M   19      2       3 5743   66.00   F   15      2       3 5744   32.00   M   16      2       2 5745   65.00   M   14      2       3 5746   48.00   M   13      2       2 5747   13.00   M    3      2       1 5748   58.00   M    0      2       2 5749   82.00   M    0      2       3 5750   45.00   M   12      2       2 5751   72.00   M   13      2       3 5752   66.00   M   15      2       3 5753   78.00   M    2      2       3 5754   79.00   M    3      2       3 5755   67.00   M   11      2       3 5756   42.00   F    2      2       2 5757   74.00   F    7      2       3 5758   44.00   M   10      2       2 5759   70.00   M    2      2       3 5760   60.00   M   24      2       3 5761   80.00   M    6      2       3 5762   85.00   M    1      2       3 5763   75.00   M    9      2       3 5764   58.00   F    1      2       2 5765   72.00   M    8      2       3 5766   51.00   M    7      2       2 5767   70.00   M    7      2       3 5768   71.00   M    0      2       3 5769   85.00   M    5      2       3 5770   56.00   M    7      2       2 5771   71.00   M    8      2       3 5772   30.00   F    8      2       2 5773   69.00   F    1      2       3 5774   83.00   M    6      2       3 5775   69.00   M    6      2       3 5776   70.00   M    1      2       3 5777   53.00   M    1      2       2 5778   55.00   M    7      2       2 5779   29.00   M    4      2       2 5780   65.00   M    3      2       3 5781   48.00   M    8      2       2 5782   70.00   F    5      2       3 5783   78.00   F    6      2       3 5784   62.00   F    3      2       3 5785   70.00   M    9      2       3 5786   70.00   F    9      2       3 5787   49.00   M    5      2       2 5788   60.00   M    5      2       3 5789   75.00   M    5      2       3 5790   62.00   M    4      2       3 5791   72.00   M    2      2       3 5792   32.00   M    9      2       2 5793   70.00   M    5      2       3 5794   65.00   M    5      2       3 5795   87.00   M    2      2       3 5796   60.00   F    6      2       3 5797   45.00   F    5      2       2 5798   70.00   M    4      2       3 5799   55.00   M    6      2       2 5800   73.00   M    5      2       3 5801   40.00   F    2      2       2 5802   80.00   M    6      2       3 5803   46.00   F    0      2       2 5804   65.00   F    3      2       3 5805   44.00   F    3      2       2 5806   45.00   M    3      2       2 5807   55.00   M    3      2       2 5808   69.00   F    6      2       3 5809   45.00   F    4      2       2 5810   55.00   M    4      2       2 5811   29.00   M    3      2       2 5812   65.00   M    3      2       3 5813   60.00   M    5      2       3 5814   53.00   M    4      2       2 5815   60.00   F    1      2       3 5816   85.00   M    1      2       3 5817   59.00   M    2      2       2 5818   70.00   M    5      2       3 5819   50.00   M    0      2       2 5820   66.00   F    5      2       3 5821   62.00   M    2      2       3 5822   82.00   M    4      2       3 5823   84.00   M    1      2       3 5824   70.00   F    3      2       3 5825   73.00   F    4      2       3 5826   55.00   M    1      2       2 5827   65.00   F    2      2       3 5828   86.00   F    1      2       3 5829   38.00   M    0      2       2 5830   45.00   F    3      2       2 5831   57.00   M    0      2       2 5832   49.00   M    1      2       2 5833   62.00   F    1      2       3 5834   85.00   M    0      2       3 5835   81.00   M    0      2       3 5836   70.00   M    2      2       3 5837   70.00   M    1      2       3 5838   90.00   M    0      2       3 5839   50.00   M    0      2       2 5840   28.00   F    0      2       2 5841   80.00   M    0      2       3 5842   75.00   M    4      2       3 5843   40.00   F    0      2       2 5844   78.00   F   19      2       3 5845   76.00   M    0      2       3 5846   60.00   F    0      2       3 5847   61.00   M   19      2       3 5848   70.00   F   19      2       3 5849   60.00   M    1      2       3 5850   50.00   M    1      2       2 5851   68.00   F   19      2       3 5852   38.00   M   18      2       2 5853   55.00   M   16      2       2 5854   45.00   F   31      2       2 5855   60.00   M    2      2       3 5856   47.00   M   13      2       2 5857   62.00   M   13      2       3 5858   65.00   M    3      2       3 5859   57.00   F   13      2       2 5860   60.00   M   11      2       3 5861   55.00   M    4      2       2 5862   60.00   F    9      2       3 5863   75.00   F    8      2       3 5864   68.00   M    0      2       3 5865   60.00   M    2      2       3 5866   70.00   M    3      2       3 5867   45.00   F    1      2       2 5868   65.00   M    1      2       3 5869   70.00   M   10      2       3 5870   75.00   M    1      2       3 5871   65.00   M    8      2       3 5872   54.00   M    9      2       2 5873   45.00   M   10      2       2 5874   52.00   M   11      2       2 5875   64.00   M    9      2       3 5876   64.00   M   10      2       3 5877   59.00   M    5      2       2 5878   56.00   F    4      2       2 5879   65.00   M    8      2       3 5880   75.00   F    5      2       3 5881   54.00   F    5      2       2 5882   76.00   M    8      2       3 5883   59.00   M   10      2       2 5884   79.00   M    4      2       3 5885   45.00   M    7      2       2 5886   69.00   M    7      2       3 5887   35.00   M    0      2       2 5888   72.00   M    2      2       3 5889   70.00   M    0      2       3 5890   33.00   M    8      2       2 5891   48.00   M    5      2       2 5892   65.00   M    4      2       3 5893   42.00   F    3      2       2 5894   53.00   M    7      2       2 5895   65.00   M    5      2       3 5896   85.00   M    4      2       3 5897   55.00   M   13      2       2 5898   67.00   F    5      2       3 5899   49.00   F    4      2       2 5900   75.00   M    2      2       3 5901   48.00   M    4      2       2 5902   65.00   M    0      2       3 5903   57.00   F    4      2       2 5904   73.00   M    6      2       3 5905   72.00   M    2      2       3 5906   70.00   F    4      2       3 5907   46.00   M    5      2       2 5908   74.00   F    0      2       3 5909   61.00   F    4      2       3 5910   50.00   F    0      2       2 5911   65.00   F    5      2       3 5912   32.00   F    1      2       2 5913   47.00   F    3      2       2 5914   65.00   M    4      2       3 5915   68.00   M    1      2       3 5916   57.00   F    2      2       2 5917   58.00   M    7      2       2 5918   63.00   M    2      2       3 5919   65.00   M    3      2       3 5920   48.00   F    2      2       2 5921   50.00   F    1      2       2 5922   63.00   F    1      2       3 5923   56.00   F    1      2       2 5924   63.00   F   13      2       3 5925   60.00   M    1      2       3 5926   60.00   M    0      2       3 5927   78.00   M    0      2       3 5928   71.00   M    5      2       3 5929   65.00   M    2      2       3 5930   58.00   M    5      2       2 5931   50.00   F    5      2       2 5932   75.00   F    0      2       3 5933   80.00   M    0      2       3 5934   55.00   M    2      2       2 5935   68.00   F    2      2       3 5936   52.00   F    2      2       2 5937   50.00   M    0      2       2 5938   84.00   M    2      2       3 5939   45.00   M    0      2       2 5940   55.00   M    1      2       2 5941   75.00   F    0      2       3 5942   68.00   M    0      2       3 5943   70.00   M    0      2       3 5944   46.00   M    0      2       2 5945   90.00   M    0      2       3 5946   78.00   M    0      2       3 5947   70.00   F    0      2       3 5948   60.00   M    2      2       3 5949   67.00   M   20      2       3 5950   47.00   F    1      2       2 5951   55.00   M    2      2       2 5952   44.00   M   24      2       2 5953   73.00   M   24      2       3 5954   60.00   M    2      2       3 5955   62.00   M   16      2       3 5956   61.00   M    9      2       3 5957   56.00   F    1      2       2 5958   62.00   M   17      2       3 5959   62.00   M   16      2       3 5960   61.00   F   16      2       3 5961   83.00   M    8      2       3 5962   60.00   F    0      2       3 5963   74.00   M    4      2       3 5964   50.00   M    5      2       2 5965   25.00   F   13      2       2 5966   68.00   M   11      2       3 5967   68.00   F   13      2       3 5968   75.00   M    5      2       3 5969   70.00   M   10      2       3 5970   65.00   M   12      2       3 5971   60.00   M   14      2       3 5972   59.00   M   13      2       2 5973   62.00   M    8      2       3 5974   72.00   M    5      2       3 5975   42.00   F    5      2       2 5976   29.00   F    3      2       2 5977   74.00   M    1      2       3 5978   70.00   M    2      2       3 5979   42.00   M   10      2       2 5980   65.00   F   10      2       3 5981   73.00   M    2      2       3 5982   58.00   M    9      2       2 5983   72.00   F    9      2       3 5984   52.00   F   10      2       2 5985   66.00   M   10      2       3 5986   45.00   M    7      2       2 5987   45.00   M    1      2       2 5988   69.00   M    0      2       3 5989   84.00   M    7      2       3 5990   75.00   M    3      2       3 5991   65.00   M    5      2       3 5992   68.00   F   10      2       3 5993   68.00   M    7      2       3 5994   66.00   M    0      2       3 5995   62.00   F    7      2       3 5996   65.00   F    5      2       3 5997   70.00   F    0      2       3 5998   92.00   M    5      2       3 5999   70.00   M    0      2       3 6000   75.00   F    3      2       3 6001   69.00   M   12      2       3 6002   43.00   M    6      2       2 6003   58.00   M    5      2       2 6004   81.00   F    0      2       3 6005   65.00   F    0      2       3 6006   59.00   M    0      2       2 6007   91.00   M    0      2       3 6008   65.00   M    3      2       3 6009   75.00   F    2      2       3 6010   65.00   M    5      2       3 6011   77.00   M    2      2       3 6012   23.00   M    2      2       2 6013   63.00   M    0      2       3 6014   62.00   M    3      2       3 6015   79.00   M    1      2       3 6016   49.00   M    5      2       2 6017   72.00   M    0      2       3 6018   75.00   M    5      2       3 6019   70.00   F    0      2       3 6020   70.00   M    5      2       3 6021   62.00   F    3      2       3 6022   61.00   F    1      2       3 6023   45.00   F    2      2       2 6024   68.00   M    1      2       3 6025   72.00   M    2      2       3 6026   73.00   M    3      2       3 6027   58.00   M    0      2       2 6028   74.00   M    3      2       3 6029   78.00   F    5      2       3 6030   85.00   M    2      2       3 6031   71.00   M    2      2       3 6032   60.00   F    2      2       3 6033   74.00   F    3      2       3 6034   29.00   M    1      2       2 6035   64.00   F    3      2       3 6036   39.00   M    1      2       2 6037   43.00   F    2      2       2 6038   76.00   M    2      2       3 6039   60.00   M    0      2       3 6040   67.00   M    2      2       3 6041   70.00   F    1      2       3 6042   67.00   M    0      2       3 6043   50.00   M    0      2       2 6044   45.00   M    0      2       2 6045   75.00   M    3      2       3 6046   46.00   F    0      2       2 6047   61.00   F    1      2       3 6048   61.00   M    1      2       3 6049   56.00   M    0      2       2 6050   58.00   M    2      2       2 6051   65.00   F    6      2       3 6052   86.00   M    0      2       3 6053   35.00   M    1      2       2 6054   85.00   M    2      2       3 6055   55.00   F    0      2       2 6056   47.00   M    0      2       2 6057   57.00   M    0      2       2 6058   84.00   M    1      2       3 6059   79.00   M    1      2       3 6060   65.00   F    1      2       3 6061   65.00   M    0      2       3 6062   68.00   M    0      2       3 6063   60.00   F    2      2       3 6064   30.00   M    7      2       2 6065   34.00   M    0      2       2 6066   60.00   M   23      2       3 6067   29.00   M   18      2       2 6068   68.00   F    4      2       3 6069   54.00   M   19      2       2 6070   49.00   M   14      2       2 6071   70.00   M    1      2       3 6072   53.00   M   15      2       2 6073   75.00   M    5      2       3 6074   78.00   F   14      2       3 6075   70.00   M   15      2       3 6076   79.00   M   14      2       3 6077   51.00   M   11      2       2 6078   67.00   M    0      2       3 6079   49.00   M   12      2       2 6080   85.00   M   13      2       3 6081   84.00   M    0      2       3 6082   58.00   F    8      2       2 6083   50.00   F    0      2       2 6084   60.00   M    0      2       3 6085   85.00   F    7      2       3 6086   48.00   M    0      2       2 6087   70.00   M   10      2       3 6088   65.00   M    5      2       3 6089   70.00   M   11      2       3 6090   35.00   M    9      2       2 6091   45.00   M    4      2       2 6092   80.00   M    3      2       3 6093   58.00   F   12      2       2 6094   80.00   M    6      2       3 6095   78.00   M    3      2       3 6096   62.00   M    5      2       3 6097   66.00   M    5      2       3 6098   82.00   M    8      2       3 6099   58.00   M    1      2       2 6100   58.00   M    9      2       2 6101   68.00   M    7      2       3 6102   86.00   M    0      2       3 6103   17.00   M    6      2       1 6104   68.00   M    5      2       3 6105   64.00   M    4      2       3 6106   58.00   F   10      2       2 6107   62.00   M    7      2       3 6108   60.00   M    9      2       3 6109   82.00   F    3      2       3 6110   60.00   F    0      2       3 6111   55.00   F    2      2       2 6112   41.00   M    3      2       2 6113   62.00   M    1      2       3 6114   54.00   M    8      2       2 6115   66.00   F    2      2       3 6116   57.00   F    7      2       2 6117   70.00   M    6      2       3 6118   79.00   F    6      2       3 6119   70.00   F    5      2       3 6120   70.00   F    0      2       3 6121   78.00   M    7      2       3 6122   72.00   M    6      2       3 6123   52.00   F    4      2       2 6124   74.00   M    6      2       3 6125   65.00   F   10      2       3 6126   70.00   M    6      2       3 6127   68.00   F    7      2       3 6128   75.00   F    0      2       3 6129   41.00   F    3      2       2 6130   52.00   M    3      2       2 6131   65.00   M    0      2       3 6132   84.00   F    4      2       3 6133   50.00   F    5      2       2 6134   55.00   M    3      2       2 6135   57.00   M    5      2       2 6136   28.00   M    1      2       2 6137   69.00   M    6      2       3 6138   90.00   M    2      2       3 6139   61.00   M    4      2       3 6140   49.00   M    4      2       2 6141   75.00   M    4      2       3 6142   70.00   M    4      2       3 6143   60.00   M    6      2       3 6144   68.00   F    5      2       3 6145   61.00   M    2      2       3 6146   45.00   M    4      2       2 6147   62.00   M    3      2       3 6148   70.00   M    3      2       3 6149   79.00   M    3      2       3 6150   38.00   F    5      2       2 6151   60.00   M    1      2       3 6152   83.00   F   12      2       3 6153   76.00   M    0      2       3 6154   47.00   F    0      2       2 6155   67.00   M    5      2       3 6156   40.00   M    0      2       2 6157   92.00   M    2      2       3 6158   67.00   M    0      2       3 6159   55.00   F    1      2       2 6160   65.00   F    3      2       3 6161   49.00   M    1      2       2 6162   25.00   M   11      2       2 6163   51.00   F    1      2       2 6164   72.00   M   16      2       3 6165   60.00   F    1      2       3 6166   65.00   M    2      2       3 6167   34.00   M    2      2       2 6168   81.00   M    7      2       3 6169   87.00   M    0      2       3 6170   46.00   F    0      2       2 6171   80.00   F    2      2       3 6172   65.00   F    2      2       3 6173   70.00   M    3      2       3 6174   58.00   M    0      2       2 6175   65.00   M    2      2       3 6176   75.00   M    3      2       3 6177   35.00   M    1      2       2 6178   44.00   M    3      2       2 6179   38.00   M    8      2       2 6180   86.00   F    0      2       3 6181   83.00   M    1      2       3 6182   52.00   M    1      2       2 6183   68.00   F    2      2       3 6184   50.00   F    2      2       2 6185   83.00   M    2      2       3 6186   35.00   M    1      2       2 6187   65.00   M    1      2       3 6188   60.00   M    0      2       3 6189   66.00   F    1      2       3 6190   52.00   M    0      2       2 6191   82.00   M    2      2       3 6192   71.00   M   27      2       3 6193   26.00   M    3      2       2 6194   65.00   M    1      2       3 6195   72.00   M    1      2       3 6196   66.00   M   20      2       3 6197   50.00   F   10      2       2 6198   48.00   F    1      2       2 6199   71.00   M   14      2       3 6200   66.00   M    2      2       3 6201   65.00   M    2      2       3 6202   51.00   M   12      2       2 6203   82.00   F    7      2       3 6204   50.00   F    1      2       2 6205   87.00   M    7      2       3 6206   39.00   M    3      2       2 6207   75.00   M    1      2       3 6208   68.00   M    5      2       3 6209   65.00   F   15      2       3 6210   68.00   M    3      2       3 6211   79.00   F    6      2       3 6212   59.00   M    4      2       2 6213   50.00   M    1      2       2 6214   50.00   M   12      2       2 6215   74.00   M    0      2       3 6216   70.00   M    0      2       3 6217   58.00   M    7      2       2 6218   46.00   M   13      2       2 6219   55.00   F    0      2       2 6220   65.00   F    0      2       3 6221   45.00   M    3      2       2 6222   45.00   F    0      2       2 6223   69.00   M    0      2       3 6224   73.00   F    1      2       3 6225   25.00   M    6      2       2 6226   72.00   M    2      2       3 6227   64.00   M    9      2       3 6228   43.00   M    4      2       2 6229   72.00   M    6      2       3 6230   39.00   F    6      2       2 6231   40.00   M    8      2       2 6232   51.00   F   11      2       2 6233   65.00   M    4      2       3 6234   51.00   M    6      2       2 6235   92.00   M    4      2       3 6236   70.00   M    5      2       3 6237   79.00   M    7      2       3 6238   73.00   M    1      2       3 6239   55.00   M    6      2       2 6240   64.00   M    2      2       3 6241   60.00   F    1      2       3 6242   52.00   M    6      2       2 6243   61.00   M    4      2       3 6244   55.00   M    1      2       2 6245   65.00   F    5      2       3 6246   52.00   M    0      2       2 6247   50.00   M   27      2       2 6248   63.00   M    3      2       3 6249   75.00   F    1      2       3 6250   45.00   F    3      2       2 6251   46.00   M    1      2       2 6252   73.00   M    3      2       3 6253   72.00   M    1      2       3 6254   56.00   M    2      2       2 6255   40.00   M    1      2       2 6256   51.00   M    6      2       2 6257   65.00   M    5      2       3 6258   40.00   M    3      2       2 6259   52.00   M    0      2       2 6260   63.00   M    5      2       3 6261   65.00   F    1      2       3 6262   69.00   F    1      2       3 6263   60.00   M    1      2       3 6264   45.00   F    1      2       2 6265   40.00   M    1      2       2 6266   68.00   M    3      2       3 6267   67.00   M    2      2       3 6268   70.00   F    0      2       3 6269   50.00   F    3      2       2 6270   65.00   M    2      2       3 6271   63.00   F    1      2       3 6272   63.00   M    5      2       3 6273   45.00   M    0      2       2 6274   70.00   M    0      2       3 6275   60.00   M    1      2       3 6276   86.00   M    1      2       3 6277   65.00   F    1      2       3 6278   75.00   F    2      2       3 6279   32.00   M    1      2       2 6280   66.00   M    1      2       3 6281   65.00   F    2      2       3 6282   82.00   M    2      2       3 6283   52.00   M    1      2       2 6284   78.00   F    1      2       3 6285   79.00   M    0      2       3 6286   75.00   M    1      2       3 6287   73.00   M    7      2       3 6288   65.00   M   13      2       3 6289   55.00   M    4      2       2 6290   74.00   M    3      2       3 6291   60.00   F    8      2       3 6292   64.00   M   13      2       3 6293   69.00   F    7      2       3 6294   39.00   M    0      2       2 6295   47.00   M    6      2       2 6296   24.00   M    0      2       2 6297   80.00   F   15      2       3 6298   54.00   F    0      2       2 6299   84.00   F   17      2       3 6300   60.00   M    2      2       3 6301   58.00   M    0      2       2 6302   78.00   F    9      2       3 6303   68.00   F    0      2       3 6304   70.00   M   24      2       3 6305   66.00   M    2      2       3 6306   75.00   M    6      2       3 6307   76.00   M   11      2       3 6308   84.00   F    0      2       3 6309   58.00   M    1      2       2 6310   52.00   M    6      2       2 6311   90.00   M    5      2       3 6312   22.00   M   11      2       2 6313   57.00   M   18      2       2 6314   76.00   F    1      2       3 6315   86.00   M    8      2       3 6316   71.00   M    4      2       3 6317   63.00   M    7      2       3 6318   68.00   M   12      2       3 6319   88.00   M   14      2       3 6320   56.00   F    2      2       2 6321   47.00   M    6      2       2 6322    7.00   M    2      2       1 6323   81.00   M    4      2       3 6324   53.00   F    7      2       2 6325   19.00   M    0      2       2 6326   70.00   M    0      2       3 6327   81.00   M    2      2       3 6328    8.00   M    1      2       1 6329   65.00   F   20      2       3 6330   74.00   M    0      2       3 6331   73.00   M    7      2       3 6332   60.00   M    2      2       3 6333   63.00   M    8      2       3 6334   75.00   F    5      2       3 6335   75.00   M    1      2       3 6336   73.00   F    7      2       3 6337   58.00   F    2      2       2 6338   57.00   F    1      2       2 6339   80.00   F    5      2       3 6340   65.00   M    1      2       3 6341   80.00   M    4      2       3 6342   70.00   F    6      2       3 6343   55.00   F    5      2       2 6344   45.00   F   15      2       2 6345   49.00   M    8      2       2 6346   75.00   M    1      2       3 6347   45.00   M    0      2       2 6348   58.00   F   12      2       2 6349   60.00   F    3      2       3 6350   59.00   M    3      2       2 6351   75.00   F    0      2       3 6352   61.00   M    4      2       3 6353   79.00   M    2      2       3 6354   49.00   F    7      2       2 6355   85.00   M   12      2       3 6356   65.00   F    0      2       3 6357   70.00   M    2      2       3 6358   90.00   M    0      2       3 6359   61.00   M    5      2       3 6360   53.00   M    7      2       2 6361   60.00   F    8      2       3 6362   53.00   F    9      2       2 6363   62.00   M   12      2       3 6364   50.00   M    5      2       2 6365   67.00   M    0      2       3 6366   65.00   M    0      2       3 6367   77.00   F    2      2       3 6368   65.00   M    3      2       3 6369   38.00   M   11      2       2 6370   48.00   M    8      2       2 6371   64.00   M    9      2       3 6372   72.00   M    3      2       3 6373   72.00   M    6      2       3 6374   78.00   M    4      2       3 6375   55.00   F    8      2       2 6376   65.00   M    6      2       3 6377   64.00   M    0      2       3 6378   21.00   M    8      2       2 6379   35.00   F    2      2       2 6380   58.00   M    7      2       2 6381   55.00   M    5      2       2 6382   55.00   M    4      2       2 6383   77.00   F    3      2       3 6384   57.00   M    5      2       2 6385   57.00   M    2      2       2 6386   54.00   M    4      2       2 6387   55.00   M   12      2       2 6388   45.00   M    6      2       2 6389   95.00   M    0      2       3 6390   52.00   F    3      2       2 6391   71.00   M    4      2       3 6392   72.00   F    5      2       3 6393   70.00   M    1      2       3 6394   53.00   F    5      2       2 6395   42.00   F    4      2       2 6396   84.00   M    5      2       3 6397   50.00   F    2      2       2 6398   80.00   M    0      2       3 6399   36.00   M    8      2       2 6400   33.00   M    2      2       2 6401   65.00   M    1      2       3 6402   52.00   F    0      2       2 6403   86.00   M    1      2       3 6404   76.00   F    2      2       3 6405   68.00   M    1      2       3 6406   41.00   M    2      2       2 6407   67.00   M    2      2       3 6408   64.00   F    3      2       3 6409   54.00   F    1      2       2 6410   58.00   M    1      2       2 6411   52.00   M    2      2       2 6412   48.00   M    1      2       2 6413   55.00   M    5      2       2 6414   67.00   M    4      2       3 6415   70.00   M    1      2       3 6416   65.00   M    2      2       3 6417   88.00   F    1      2       3 6418   30.00   M    0      2       2 6419   60.00   M    0      2       3 6420   50.00   M    1      2       2 6421   50.00   M    1      2       2 6422   66.00   F    1      2       3 6423   52.00   F    1      2       2 6424   62.00   M    5      2       3 6425   45.00   F    3      2       2 6426   56.00   M    2      2       2 6427   75.00   F    0      2       3 6428   64.00   M   24      2       3 6429   63.00   M   22      2       3 6430   54.00   M   10      2       2 6431   60.00   M    7      2       3 6432   51.00   F    3      2       2 6433   47.00   F    4      2       2 6434   78.00   M   19      2       3 6435   60.00   M   16      2       3 6436   66.00   M   15      2       3 6437   80.00   M   18      2       3 6438   65.00   F    2      2       3 6439   49.00   M   19      2       2 6440   55.00   M   18      2       2 6441   80.00   F   15      2       3 6442   82.00   F   16      2       3 6443   34.00   F   13      2       2 6444   30.00   F   13      2       2 6445   65.00   F    5      2       3 6446   67.00   M    1      2       3 6447   58.00   F   11      2       2 6448   75.00   F    3      2       3 6449   32.00   F    2      2       2 6450   70.00   F   13      2       3 6451   56.00   M   10      2       2 6452   76.00   F   11      2       3 6453   78.00   M    9      2       3 6454   56.00   M   13      2       2 6455   60.00   F    1      2       3 6456   65.00   F   15      2       3 6457   80.00   M   12      2       3 6458   60.00   F    9      2       3 6459   42.00   M    5      2       2 6460   81.00   F   10      2       3 6461   65.00   M    5      2       3 6462   88.00   M   10      2       3 6463   65.00   M    7      2       3 6464   56.00   F    0      2       2 6465   85.00   M   12      2       3 6466   58.00   M    8      2       2 6467   53.00   M    6      2       2 6468   55.00   M    8      2       2 6469   50.00   M   11      2       2 6470   50.00   F    3      2       2 6471   62.00   M    1      2       3 6472   65.00   F   12      2       3 6473   68.00   M    6      2       3 6474   65.00   M    3      2       3 6475   60.00   M    2      2       3 6476   53.00   M   31      2       2 6477   55.00   F    4      2       2 6478   54.00   M    7      2       2 6479   46.00   M    0      2       2 6480   42.00   F    6      2       2 6481   70.00   F    9      2       3 6482   65.00   M    7      2       3 6483   47.00   M    2      2       2 6484   55.00   F    3      2       2 6485   60.00   F    0      2       3 6486   65.00   M    8      2       3 6487   71.00   M    6      2       3 6488   63.00   M    7      2       3 6489   75.00   F    7      2       3 6490   66.00   M    8      2       3 6491   68.00   M    1      2       3 6492   55.00   M    7      2       2 6493   55.00   M    6      2       2 6494   42.00   M    4      2       2 6495   60.00   F   10      2       3 6496   67.00   M    2      2       3 6497   62.00   M    5      2       3 6498   61.00   M    3      2       3 6499   64.00   M    0      2       3 6500   45.00   F    4      2       2 6501   77.00   F    5      2       3 6502   48.00   F    6      2       2 6503   51.00   M    5      2       2 6504   48.00   M    1      2       2 6505   70.00   M    0      2       3 6506   48.00   M    2      2       2 6507   54.00   M    5      2       2 6508   68.00   F    3      2       3 6509   62.00   M    0      2       3 6510   46.00   M    4      2       2 6511   79.00   M    5      2       3 6512   61.00   M    4      2       3 6513   80.00   F    4      2       3 6514   73.00   M    4      2       3 6515   40.00   M    0      2       2 6516   69.00   F    4      2       3 6517   58.00   M    6      2       2 6518   62.00   F    2      2       3 6519   77.00   M    1      2       3 6520   63.00   F    2      2       3 6521   75.00   M    1      2       3 6522   35.00   M    0      2       2 6523   45.00   M    1      2       2 6524   66.00   M    3      2       3 6525   54.00   M    2      2       2 6526   67.00   M    2      2       3 6527   62.00   M    4      2       3 6528   59.00   M    0      2       2 6529   57.00   M    3      2       2 6530   64.00   F    3      2       3 6531   52.00   M    0      2       2 6532   87.00   F    0      2       3 6533   55.00   M    0      2       2 6534   60.00   M    1      2       3 6535   75.00   M    3      2       3 6536   73.00   M    5      2       3 6537   63.00   M    0      2       3 6538   88.00   M    0      2       3 6539   55.00   F    0      2       2 6540   47.00   M    5      2       2 6541   50.00   M    4      2       2 6542   92.00   F    2      2       3 6543   63.00   F    2      2       3 6544   77.00   M    3      2       3 6545   36.00   M    0      2       2 6546   75.00   M    3      2       3 6547   35.00   M    3      2       2 6548   70.00   F    0      2       3 6549   74.00   F    3      2       3 6550   54.00   F    0      2       2 6551   58.00   M    2      2       2 6552   84.00   M    0      2       3 6553   78.00   M    0      2       3 6554   42.00   M    2      2       2 6555   66.00   M    3      2       3 6556   65.00   M   10      2       3 6557   59.00   F    0      2       2 6558   50.00   M    1      2       2 6559   65.00   M    0      2       3 6560   57.00   M    2      2       2 6561   55.00   F    0      2       2 6562   76.00   M    0      2       3 6563   65.00   F    3      2       3 6564   49.00   M    0      2       2 6565   47.00   F    2      2       2 6566   59.00   F    1      2       2 6567   58.00   M    0      2       2 6568   75.00   M    0      2       3 6569   82.00   F    1      2       3 6570   57.00   M    1      2       2 6571   45.00   M    0      2       2 6572   60.00   M    0      2       3 6573   42.00   M    0      2       2 6574   65.00   M    3      2       3 6575   64.00   M    0      2       3 6576   59.00   M   24      2       2 6577   50.00   M    3      2       2 6578   55.00   M   10      2       2 6579   67.00   M   22      2       3 6580   53.00   M   16      2       2 6581   45.00   M   16      2       2 6582   70.00   M   16      2       3 6583   78.00   M    0      2       3 6584   49.00   M   15      2       2 6585   67.00   F   12      2       3 6586   47.00   M   10      2       2 6587   50.00   M    8      2       2 6588   59.00   M    5      2       2 6589   84.00   M   13      2       3 6590   52.00   M    7      2       2 6591   44.00   M    9      2       2 6592   54.00   F    9      2       2 6593   64.00   M    0      2       3 6594   75.00   F   34      2       3 6595   74.00   M    2      2       3 6596   34.00   M    6      2       2 6597   80.00   F   12      2       3 6598   68.00   F    0      2       3 6599   68.00   M    9      2       3 6600   45.00   M    3      2       2 6601   52.00   F    1      2       2 6602   45.00   F    0      2       2 6603   85.00   M    4      2       3 6604   80.00   F    3      2       3 6605   64.00   M    2      2       3 6606   35.00   M    0      2       2 6607   60.00   F    1      2       3 6608   70.00   F   35      2       3 6609   68.00   F    7      2       3 6610   70.00   M    5      2       3 6611   75.00   M    7      2       3 6612   70.00   F    9      2       3 6613   35.00   M   10      2       2 6614   84.00   F   11      2       3 6615   66.00   F    0      2       3 6616   47.00   M    0      2       2 6617   55.00   F    8      2       2 6618   70.00   M    0      2       3 6619   62.00   M   35      2       3 6620   68.00   M    7      2       3 6621   68.00   F    4      2       3 6622   74.00   M    8      2       3 6623   31.00   M    7      2       2 6624   60.00   M    7      2       3 6625   63.00   M    8      2       3 6626   80.00   M    6      2       3 6627   47.00   F    9      2       2 6628   78.00   M    7      2       3 6629   53.00   M    7      2       2 6630   84.00   M    5      2       3 6631   26.00   F    4      2       2 6632   54.00   M   10      2       2 6633   56.00   M    5      2       2 6634   75.00   M    1      2       3 6635   72.00   M    8      2       3 6636   98.00   M    7      2       3 6637   27.00   M    1      2       2 6638   73.00   M    2      2       3 6639   76.00   M    7      2       3 6640   80.00   M    5      2       3 6641   55.00   F    4      2       2 6642   69.00   M    4      2       3 6643   61.00   M   34      2       3 6644   32.00   M    0      2       2 6645   44.00   M    4      2       2 6646   45.00   M    4      2       2 6647   60.00   M    2      2       3 6648   64.00   M    6      2       3 6649   61.00   M    1      2       3 6650   33.00   F    4      2       2 6651   70.00   F    6      2       3 6652  100.00   M    3      2       3 6653   70.00   M    7      2       3 6654   59.00   M    4      2       2 6655   45.00   M    5      2       2 6656   81.00   F    2      2       3 6657   90.00   F    5      2       3 6658   60.00   M    2      2       3 6659   52.00   M   14      2       2 6660   50.00   M    5      2       2 6661   62.00   F    5      2       3 6662   48.00   M    4      2       2 6663   55.00   F    4      2       2 6664   68.00   F    0      2       3 6665   78.00   M   19      2       3 6666   77.00   F    2      2       3 6667   74.00   M    5      2       3 6668   80.00   M    3      2       3 6669   60.00   F    1      2       3 6670   45.00   M    3      2       2 6671   60.00   F    9      2       3 6672   70.00   F    2      2       3 6673   55.00   F    1      2       2 6674   74.00   M    2      2       3 6675   64.00   M    4      2       3 6676   44.00   F    2      2       2 6677   40.00   M    1      2       2 6678   73.00   M   33      2       3 6679   48.00   M    1      2       2 6680   64.00   M    0      2       3 6681   64.00   M    1      2       3 6682   42.00   M    1      2       2 6683   61.00   M    0      2       3 6684   77.00   M    2      2       3 6685   40.00   M    0      2       2 6686   17.00   M    0      2       1 6687   56.00   F    1      2       2 6688   60.00   M    2      2       3 6689   72.00   F    0      2       3 6690   80.00   M    0      2       3 6691   35.00   F    0      2       2 6692   60.00   F    0      2       3 6693   62.00   M    0      2       3 6694   53.00   F    1      2       2 6695   83.00   M    1      2       3 6696   70.00   M    0      2       3 6697   37.00   M    1      2       2 6698   73.00   M    1      2       3 6699   72.00   F    0      2       3 6700   48.00   M    4      2       2 6701   78.00   F    0      2       3 6702   65.00   M   12      2       3 6703   50.00   M    4      2       2 6704   65.00   F    1      2       3 6705   48.00   M    1      2       2 6706   65.00   M    4      2       3 6707   49.00   F   21      2       2 6708   55.00   M   23      2       2 6709   65.00   M   33      2       3 6710   65.00   M    8      2       3 6711   22.00   F   14      2       2 6712   62.00   F   16      2       3 6713   80.00   M    2      2       3 6714   85.00   F    5      2       3 6715   74.00   F   10      2       3 6716   70.00   M    2      2       3 6717   48.00   M   13      2       2 6718   65.00   F    5      2       3 6719   65.00   M    2      2       3 6720   76.00   M    2      2       3 6721   54.00   M    2      2       2 6722   87.00   M   11      2       3 6723   89.00   M    4      2       3 6724   55.00   F    7      2       2 6725   70.00   M    5      2       3 6726   70.00   M    2      2       3 6727   67.00   M    0      2       3 6728   61.00   F    5      2       3 6729   69.00   M    2      2       3 6730   62.00   F    5      2       3 6731   65.00   F    6      2       3 6732   78.00   M    6      2       3 6733   78.00   F    5      2       3 6734   65.00   M    9      2       3 6735   42.00   M    7      2       2 6736   67.00   M    6      2       3 6737   63.00   F    4      2       3 6738   56.00   M    7      2       2 6739   29.00   M    9      2       2 6740   79.00   M    8      2       3 6741   69.00   M    3      2       3 6742   86.00   M    9      2       3 6743   55.00   F    7      2       2 6744   76.00   F    4      2       3 6745   75.00   M    6      2       3 6746   75.00   F    5      2       3 6747   76.00   F    5      2       3 6748   70.00   M    7      2       3 6749   80.00   F    5      2       3 6750   73.00   F    7      2       3 6751   38.00   F    0      2       2 6752   83.00   M    7      2       3 6753   55.00   F    2      2       2 6754   58.00   M    5      2       2 6755   45.00   M    4      2       2 6756   65.00   M    8      2       3 6757   70.00   M    3      2       3 6758   33.00   M    5      2       2 6759   70.00   M    0      2       3 6760   68.00   M    3      2       3 6761   75.00   M    7      2       3 6762   59.00   M    5      2       2 6763   79.00   M    7      2       3 6764   59.00   M    5      2       2 6765   58.00   M    4      2       2 6766   42.00   F    4      2       2 6767   59.00   M    4      2       2 6768   59.00   F    6      2       2 6769   74.00   M    2      2       3 6770   79.00   M    1      2       3 6771   67.00   M    4      2       3 6772   60.00   F    6      2       3 6773   72.00   M    4      2       3 6774   83.00   M    6      2       3 6775   78.00   M    2      2       3 6776   55.00   M    0      2       2 6777   41.00   F    5      2       2 6778   76.00   M    5      2       3 6779   49.00   M    0      2       2 6780   52.00   F   23      2       2 6781   84.00   M    2      2       3 6782   65.00   M    1      2       3 6783   47.00   F    3      2       2 6784   65.00   M    1      2       3 6785   58.00   M   34      2       2 6786   80.00   M    2      2       3 6787   60.00   F    3      2       3 6788   83.00   M    0      2       3 6789   84.00   M    3      2       3 6790   65.00   M    3      2       3 6791   58.00   F    3      2       2 6792   60.00   M    3      2       3 6793   60.00   F    1      2       3 6794   68.00   F    3      2       3 6795   70.00   F    6      2       3 6796   66.00   M    0      2       3 6797   80.00   M    0      2       3 6798   59.00   F    2      2       2 6799   65.00   F    1      2       3 6800   60.00   M    0      2       3 6801   32.00   F    2      2       2 6802   56.00   M    2      2       2 6803   55.00   F    1      2       2 6804   45.00   M    1      2       2 6805   60.00   M    4      2       3 6806   40.00   F    2      2       2 6807   56.00   M    0      2       2 6808   55.00   F    1      2       2 6809   42.00   M    0      2       2 6810    0.00   M    1      2       1 6811   50.00   M    1      2       2 6812   59.00   M    1      2       2 6813   56.00   M    1      2       2 6814   52.00   F    4      2       2 6815   35.00   M    0      2       2 6816   50.00   M    2      2       2 6817   60.00   M    1      2       3 6818   73.00   M    1      2       3 6819   54.00   M    4      2       2 6820   60.00   M    2      2       3 6821   75.00   M    1      2       3 6822   64.00   M    0      2       3 6823   48.00   M    1      2       2 6824   80.00   F    3      2       3 6825   58.00   F    1      2       2 6826   25.00   M    1      2       2 6827   55.00   F    0      2       2 6828   72.00   M    0      2       3 6829   68.00   M    2      2       3 6830   49.00   F    0      2       2 6831   70.00   M    7      2       3 6832   70.00   F    0      2       3 6833   60.00   M    8      2       3 6834   52.00   F    7      2       2 6835   81.00   M    2      2       3 6836   71.00   M   11      2       3 6837   69.00   M    7      2       3 6838   77.00   M   15      2       3 6839   48.00   F   11      2       2 6840   68.00   M    6      2       3 6841   64.00   F   13      2       3 6842   55.00   M    1      2       2 6843   63.00   M   12      2       3 6844   63.00   M    6      2       3 6845   55.00   M    5      2       2 6846   64.00   M    3      2       3 6847   57.00   F    7      2       2 6848   52.00   M    4      2       2 6849   74.00   M    2      2       3 6850   75.00   M    3      2       3 6851   66.00   M   12      2       3 6852   59.00   M    5      2       2 6853   70.00   M    7      2       3 6854   69.00   F    0      2       3 6855   80.00   M    0      2       3 6856   46.00   M   11      2       2 6857   60.00   M   12      2       3 6858   65.00   M   12      2       3 6859   60.00   M    0      2       3 6860   68.00   M   13      2       3 6861   74.00   M    4      2       3 6862   60.00   F    8      2       3 6863   40.00   M    4      2       2 6864   50.00   M    7      2       2 6865   66.00   M    8      2       3 6866   75.00   M    4      2       3 6867   65.00   M    2      2       3 6868   73.00   M    9      2       3 6869   55.00   M    1      2       2 6870   70.00   M    6      2       3 6871   80.00   F    4      2       3 6872   70.00   F    5      2       3 6873   42.00   M    9      2       2 6874   60.00   M    3      2       3 6875   67.00   F    8      2       3 6876   72.00   M    3      2       3 6877   53.00   M    6      2       2 6878   70.00   M    2      2       3 6879   70.00   F    8      2       3 6880   65.00   M    2      2       3 6881   54.00   M    1      2       2 6882   51.00   M    9      2       2 6883   22.00   F    1      2       2 6884   56.00   F    8      2       2 6885   59.00   M    4      2       2 6886   68.00   M    2      2       3 6887   43.00   F    3      2       2 6888   54.00   M   11      2       2 6889   48.00   M    2      2       2 6890   76.00   M    6      2       3 6891   63.00   M    5      2       3 6892   90.00   M    9      2       3 6893   72.00   M    5      2       3 6894   57.00   M   10      2       2 6895   67.00   M    4      2       3 6896   73.00   M    7      2       3 6897   74.00   M    1      2       3 6898   70.00   M    4      2       3 6899   35.00   F    4      2       2 6900   48.00   F    5      2       2 6901   40.00   M    4      2       2 6902   73.00   M    6      2       3 6903   65.00   F    3      2       3 6904   60.00   M    2      2       3 6905   46.00   M    4      2       2 6906   71.00   M    6      2       3 6907   66.00   M    7      2       3 6908   69.00   M    0      2       3 6909   64.00   M    4      2       3 6910   65.00   F    1      2       3 6911   63.00   M    3      2       3 6912   67.00   M    5      2       3 6913   70.00   F   35      2       3 6914   55.00   F    0      2       2 6915   54.00   F    1      2       2 6916   75.00   F    5      2       3 6917   70.00   M    6      2       3 6918   58.00   M    5      2       2 6919   69.00   M    1      2       3 6920   52.00   M    2      2       2 6921   48.00   F    2      2       2 6922   90.00   M    1      2       3 6923   50.00   F    3      2       2 6924   60.00   F    6      2       3 6925   47.00   F    2      2       2 6926   52.00   M    1      2       2 6927   58.00   F    3      2       2 6928   52.00   M    5      2       2 6929   74.00   M    3      2       3 6930   55.00   M    1      2       2 6931   40.00   M    0      2       2 6932   85.00   M    2      2       3 6933   66.00   F    0      2       3 6934   38.00   M    6      2       2 6935   60.00   M    2      2       3 6936   26.00   M    2      2       2 6937   50.00   M    2      2       2 6938   59.00   F    0      2       2 6939   50.00   F    3      2       2 6940   45.00   M    1      2       2 6941   74.00   M    0      2       3 6942   83.00   F    4      2       3 6943   72.00   F    2      2       3 6944   55.00   F    3      2       2 6945   60.00   F    2      2       3 6946   75.00   M    2      2       3 6947   72.00   F    1      2       3 6948   64.00   M    0      2       3 6949   73.00   M    2      2       3 6950   63.00   F    1      2       3 6951   54.00   M    2      2       2 6952   70.00   M    2      2       3 6953   85.00   M    1      2       3 6954   70.00   F    1      2       3 6955   40.00   M    2      2       2 6956   72.00   M    0      2       3 6957   59.00   M    0      2       2 6958   53.00   M   34      2       2 6959   50.00   M    0      2       2 6960   52.00   F    1      2       2 6961   87.00   M    7      2       3 6962   54.00   F    5      2       2 6963   67.00   F   21      2       3 6964   43.00   M    8      2       2 6965   71.00   M    2      2       3 6966   48.00   F   15      2       2 6967   70.00   M   21      2       3 6968   32.00   M    0      2       2 6969   56.00   M   20      2       2 6970   75.00   M   12      2       3 6971   79.00   M    6      2       3 6972   67.00   M    5      2       3 6973   42.00   F    8      2       2 6974   75.00   M    8      2       3 6975   75.00   M    5      2       3 6976   69.00   M    5      2       3 6977   86.00   M   18      2       3 6978   51.00   M    5      2       2 6979   61.00   M    8      2       3 6980   67.00   F    5      2       3 6981   84.00   M    5      2       3 6982   76.00   M   10      2       3 6983   22.00   F   11      2       2 6984   75.00   M   11      2       3 6985   85.00   M    3      2       3 6986   55.00   M    4      2       2 6987   61.00   M    4      2       3 6988   55.00   M    4      2       2 6989   78.00   M    4      2       3 6990   89.00   F    9      2       3 6991   70.00   M    3      2       3 6992   47.00   M   14      2       2 6993   43.00   F   10      2       2 6994   80.00   M    8      2       3 6995   57.00   M    7      2       2 6996   38.00   M   33      2       2 6997   65.00   F    8      2       3 6998   60.00   M    9      2       3 6999   80.00   F    9      2       3 7000   65.00   M    0      2       3 7001   82.00   M    8      2       3 7002   65.00   F    3      2       3 7003   75.00   M   36      2       3 7004   58.00   M    0      2       2 7005   59.00   M    2      2       2 7006   57.00   M    7      2       2 7007   65.00   F    8      2       3 7008   65.00   M    4      2       3 7009   45.00   M    5      2       2 7010   84.00   F    1      2       3 7011   40.00   M    3      2       2 7012   41.00   M    3      2       2 7013   60.00   M    2      2       3 7014   46.00   M    1      2       2 7015   63.00   F   15      2       3 7016   87.00   F    6      2       3 7017   68.00   F    6      2       3 7018   80.00   F    3      2       3 7019   75.00   M    1      2       3 7020   65.00   M   11      2       3 7021   27.00   F    6      2       2 7022   70.00   F    7      2       3 7023    7.00   M    1      2       1 7024   73.00   M    0      2       3 7025   65.00   M    3      2       3 7026   50.00   M    3      2       2 7027   56.00   M    0      2       2 7028   71.00   M    5      2       3 7029   55.00   M    2      2       2 7030   83.00   M    5      2       3 7031   65.00   M    1      2       3 7032   67.00   M    0      2       3 7033   42.00   M    3      2       2 7034   83.00   F    0      2       3 7035   72.00   M    4      2       3 7036   65.00   M    3      2       3 7037   66.00   M    1      2       3 7038   70.00   F    2      2       3 7039   58.00   F    7      2       2 7040   35.00   F    3      2       2 7041   56.00   F    3      2       2 7042   57.00   F    4      2       2 7043   70.00   F    1      2       3 7044   80.00   M    3      2       3 7045   82.00   M    0      2       3 7046   55.00   M    1      2       2 7047   78.00   M    0      2       3 7048   56.00   M    1      2       2 7049   70.00   M    9      2       3 7050   60.00   F    0      2       3 7051   40.00   M    0      2       2 7052   73.00   M    1      2       3 7053   75.00   M    0      2       3 7054   37.00   M    1      2       2 7055   36.00   F    2      2       2 7056   32.00   F    5      2       2 7057   23.00   F    2      2       2 7058   72.00   F   45      2       3 7059   56.00   F    8      2       2 7060   33.00   F    4      2       2 7061   72.00   M    2      2       3 7062   67.00   F   22      2       3 7063   80.00   F    0      2       3 7064   56.00   M    7      2       2 7065   75.00   F   20      2       3 7066   58.00   M    7      2       2 7067   59.00   M   15      2       2 7068   59.00   M    0      2       2 7069   65.00   F    7      2       3 7070   64.00   F    6      2       3 7071   48.00   M   15      2       2 7072   58.00   F   14      2       2 7073   73.00   F   11      2       3 7074   68.00   F    6      2       3 7075   48.00   M    7      2       2 7076   72.00   M   13      2       3 7077   60.00   M    8      2       3 7078   45.00   M    9      2       2 7079   50.00   M    4      2       2 7080   70.00   M    9      2       3 7081   55.00   M    2      2       2 7082   55.00   F    1      2       2 7083   55.00   M    2      2       2 7084   63.00   F    6      2       3 7085   90.00   F    8      2       3 7086   79.00   M   11      2       3 7087   57.00   M   10      2       2 7088   64.00   M   10      2       3 7089   36.00   M    8      2       2 7090   66.00   M    8      2       3 7091   61.00   M   11      2       3 7092   61.00   M    6      2       3 7093   65.00   F    7      2       3 7094   65.00   M    6      2       3 7095   70.00   M    7      2       3 7096   60.00   F    2      2       3 7097   65.00   M    3      2       3 7098   52.00   F    2      2       2 7099   44.00   F   10      2       2 7100   96.00   M   13      2       3 7101   74.00   M    3      2       3 7102   63.00   F    4      2       3 7103   61.00   M    1      2       3 7104   65.00   F    6      2       3 7105   21.00   M    8      2       2 7106   52.00   M    3      2       2 7107   70.00   M    3      2       3 7108   63.00   F    4      2       3 7109   68.00   M    2      2       3 7110   33.00   M    3      2       2 7111   52.00   M    8      2       2 7112   69.00   M    9      2       3 7113   64.00   M    8      2       3 7114   70.00   F    1      2       3 7115   68.00   M   10      2       3 7116   74.00   F    6      2       3 7117   72.00   M    3      2       3 7118   50.00   F    6      2       2 7119   73.00   M    6      2       3 7120   70.00   M    4      2       3 7121   68.00   M    5      2       3 7122   59.00   M    7      2       2 7123   62.00   M    1      2       3 7124   87.00   M    5      2       3 7125   48.00   F    6      2       2 7126   58.00   F    4      2       2 7127   43.00   M    3      2       2 7128   73.00   F    5      2       3 7129   60.00   M    5      2       3 7130   49.00   F    5      2       2 7131   59.00   M    1      2       2 7132   68.00   F    1      2       3 7133   43.00   M    4      2       2 7134   70.00   F    4      2       3 7135   77.00   F    4      2       3 7136   74.00   F    3      2       3 7137   50.00   M    1      2       2 7138   40.00   F    0      2       2 7139   52.00   M    4      2       2 7140   35.00   M    0      2       2 7141   58.00   F    1      2       2 7142   40.00   F    0      2       2 7143   62.00   M    2      2       3 7144   75.00   M    5      2       3 7145   65.00   F    3      2       3 7146   78.00   F    3      2       3 7147   74.00   F    1      2       3 7148   60.00   F    0      2       3 7149   70.00   F    1      2       3 7150   69.00   M    0      2       3 7151   56.00   F    3      2       2 7152   83.00   M    1      2       3 7153   82.00   F    0      2       3 7154   39.00   M    7      2       2 7155   55.00   M    1      2       2 7156   73.00   M    1      2       3 7157   60.00   M   11      2       3 7158   70.00   F    0      2       3 7159   70.00   M   23      2       3 7160   85.00   M    4      2       3 7161   60.00   M    0      2       3 7162   65.00   F    0      2       3 7163   70.00   F    4      2       3 7164   44.00   M    1      2       2 7165   65.00   M    2      2       3 7166   57.00   M    4      2       2 7167   65.00   M   13      2       3 7168   55.00   M    2      2       2 7169   66.00   M   10      2       3 7170   71.00   M    6      2       3 7171   75.00   M   13      2       3 7172   56.00   M   15      2       2 7173   32.00   F    9      2       2 7174   42.00   M    3      2       2 7175   88.00   M   12      2       3 7176   74.00   M    6      2       3 7177   65.00   F    5      2       3 7178   54.00   M   13      2       2 7179   76.00   M   13      2       3 7180   86.00   M    4      2       3 7181   78.00   M    7      2       3 7182   54.00   M    1      2       2 7183   53.00   M    4      2       2 7184   64.00   M    7      2       3 7185   90.00   M    8      2       3 7186   40.00   F    1      2       2 7187   55.00   M    4      2       2 7188   65.00   F    6      2       3 7189   62.00   M    1      2       3 7190   72.00   M    3      2       3 7191   65.00   F    0      2       3 7192   51.00   M    5      2       2 7193   60.00   M    5      2       3 7194   60.00   M    1      2       3 7195   55.00   M    8      2       2 7196   71.00   M    3      2       3 7197   70.00   M    5      2       3 7198   71.00   F    4      2       3 7199   38.00   M    2      2       2 7200   29.00   M    7      2       2 7201   70.00   F    9      2       3 7202   55.00   M    7      2       2 7203   62.00   M    3      2       3 7204   64.00   M    4      2       3 7205   45.00   M    1      2       2 7206   76.00   M    7      2       3 7207   70.00   M    2      2       3 7208   42.00   M    6      2       2 7209   72.00   M    8      2       3 7210   58.00   M    1      2       2 7211   55.00   F    4      2       2 7212   55.00   M    1      2       2 7213   63.00   M    5      2       3 7214   65.00   M    8      2       3 7215   72.00   M    2      2       3 7216   42.00   F    3      2       2 7217   26.00   F    6      2       2 7218   69.00   M    7      2       3 7219   72.00   M    4      2       3 7220   75.00   M    8      2       3 7221   80.00   M    2      2       3 7222   56.00   M    7      2       2 7223   71.00   F    1      2       3 7224   55.00   M    2      2       2 7225   70.00   F    1      2       3 7226   75.00   M    5      2       3 7227   54.00   M    6      2       2 7228   58.00   F    0      2       2 7229   50.00   M    2      2       2 7230   65.00   F    2      2       3 7231   61.00   M    1      2       3 7232   46.00   F    1      2       2 7233   69.00   M    0      2       3 7234   69.00   M    0      2       3 7235   84.00   M    7      2       3 7236   73.00   M    2      2       3 7237   57.00   M    2      2       2 7238   63.00   F   10      2       3 7239   82.00   M    3      2       3 7240   43.00   M    1      2       2 7241   47.00   M    3      2       2 7242   52.00   M    5      2       2 7243   60.00   F    2      2       3 7244   65.00   M    0      2       3 7245   77.00   F    0      2       3 7246   60.00   M    4      2       3 7247   39.00   F    0      2       2 7248   47.00   M    0      2       2 7249   70.00   F    1      2       3 7250   71.00   F    0      2       3 7251   65.00   M    5      2       3 7252   50.00   M    1      2       2 7253   60.00   F    1      2       3 7254   76.00   M    0      2       3 7255   65.00   M    2      2       3 7256   65.00   F    1      2       3 7257   60.00   M    2      2       3 7258   70.00   F    3      2       3 7259   45.00   M    0      2       2 7260   54.00   M    1      2       2 7261   63.00   F    0      2       3 7262   62.00   F    2      2       3 7263   70.00   M    8      2       3 7264   53.00   M   11      2       2 7265   70.00   M    1      2       3 7266   63.00   M    2      2       3 7267   78.00   M    2      2       3 7268   52.00   M    3      2       2 7269   54.00   F    5      2       2 7270   62.00   M    4      2       3 7271   67.00   M    0      2       3 7272   61.00   M    0      2       3 7273   43.00   F    0      2       2 7274   65.00   M    0      2       3 7275   35.00   M    1      2       2 7276   72.00   M    0      2       3 7277   45.00   M    1      2       2 7278   76.00   M   12      2       3 7279   71.00   F   10      2       3 7280   48.00   M    3      2       2 7281   60.00   M    5      2       3 7282   35.00   M    1      2       2 7283   69.00   M    6      2       3 7284   67.00   M   18      2       3 7285   62.00   M    4      2       3 7286   65.00   F    0      2       3 7287   66.00   F    1      2       3 7288   63.00   M   25      2       3 7289   67.00   F   15      2       3 7290   50.00   M   11      2       2 7291   67.00   M   11      2       3 7292   67.00   M    2      2       3 7293   85.00   M    0      2       3 7294   45.00   M    1      2       2 7295   71.00   F    0      2       3 7296   49.00   M    7      2       2 7297   20.00   M   11      2       2 7298   77.00   F   11      2       3 7299   48.00   M    2      2       2 7300   73.00   F   14      2       3 7301   62.00   F    0      2       3 7302   45.00   M   12      2       2 7303   60.00   M    5      2       3 7304   45.00   M    1      2       2 7305   57.00   F    1      2       2 7306   83.00   M    0      2       3 7307   70.00   M    1      2       3 7308   60.00   F   14      2       3 7309   74.00   M    3      2       3 7310   26.00   M   10      2       2 7311   66.00   M   10      2       3 7312   50.00   M   12      2       2 7313   49.00   M   10      2       2 7314   53.00   F    0      2       2 7315   32.00   M    3      2       2 7316   57.00   M    3      2       2 7317   56.00   M    0      2       2 7318   65.00   M    1      2       3 7319   65.00   F    0      2       3 7320   45.00   F    7      2       2 7321   70.00   F    3      2       3 7322   42.00   M    9      2       2 7323   63.00   M    0      2       3 7324   66.00   M    5      2       3 7325   60.00   M    2      2       3 7326   65.00   M    5      2       3 7327   65.00   F    5      2       3 7328   54.00   M    0      2       2 7329   59.00   M    4      2       2 7330   58.00   M    2      2       2 7331   60.00   F    2      2       3 7332   54.00   M    2      2       2 7333   30.00   M   11      2       2 7334   50.00   F    5      2       2 7335   70.00   M    3      2       3 7336   58.00   M    6      2       2 7337   49.00   M    5      2       2 7338   68.00   M    2      2       3 7339   80.00   M    7      2       3 7340   58.00   M    8      2       2 7341   93.00   M    2      2       3 7342   60.00   M    5      2       3 7343   70.00   M    3      2       3 7344   65.00   M    1      2       3 7345   55.00   M    2      2       2 7346   42.00   M    3      2       2 7347   26.00   M    0      2       2 7348   46.00   F    3      2       2 7349   48.00   M    2      2       2 7350   55.00   F    1      2       2 7351   60.00   M    6      2       3 7352   49.00   M    2      2       2 7353   62.00   M    2      2       3 7354   66.00   M    2      2       3 7355   84.00   F    3      2       3 7356   73.00   F    0      2       3 7357   32.00   M    2      2       2 7358   59.00   M    0      2       2 7359   65.00   M    7      2       3 7360   77.00   M    4      2       3 7361   60.00   M    3      2       3 7362   58.00   M    0      2       2 7363   62.00   F    0      2       3 7364   68.00   F    1      2       3 7365   55.00   F    2      2       2 7366   52.00   F    2      2       2 7367   55.00   M    5      2       2 7368   41.00   M    0      2       2 7369   54.00   F    2      2       2 7370   65.00   F    0      2       3 7371   52.00   M    0      2       2 7372   57.00   M    2      2       2 7373   40.00   F    0      2       2 7374   63.00   M    0      2       3 7375   55.00   M    0      2       2 7376   41.00   M    0      2       2 7377   45.00   M    1      2       2 7378   50.00   M   23      2       2 7379   35.00   M    5      2       2 7380   80.00   M   16      2       3 7381   39.00   M   13      2       2 7382   65.00   M   16      2       3 7383   60.00   M    3      2       3 7384   76.00   M   15      2       3 7385   55.00   M    5      2       2 7386   70.00   M    0      2       3 7387   55.00   F    6      2       2 7388   55.00   F    3      2       2 7389   46.00   M    6      2       2 7390   37.00   M    9      2       2 7391   67.00   M    9      2       3 7392   45.00   F   10      2       2 7393   72.00   M    9      2       3 7394   80.00   M    9      2       3 7395   72.00   F    7      2       3 7396   59.00   F    8      2       2 7397   57.00   F    8      2       2 7398   50.00   F    7      2       2 7399   56.00   F    7      2       2 7400   70.00   M   10      2       3 7401   60.00   M    5      2       3 7402   75.00   M    2      2       3 7403   74.00   M    6      2       3 7404   65.00   F    7      2       3 7405   79.00   F    5      2       3 7406   72.00   M    5      2       3 7407   65.00   F    5      2       3 7408   63.00   F    6      2       3 7409   65.00   M    2      2       3 7410   43.00   M    1      2       2 7411   46.00   M    5      2       2 7412   51.00   M    0      2       2 7413   71.00   F    8      2       3 7414   69.00   F    4      2       3 7415   80.00   M    2      2       3 7416   70.00   M    2      2       3 7417   69.00   M    3      2       3 7418   74.00   M    1      2       3 7419   40.00   M    2      2       2 7420   35.00   M    2      2       2 7421   77.00   F    7      2       3 7422   91.00   F    1      2       3 7423   70.00   M    6      2       3 7424   42.00   M    0      2       2 7425   65.00   F    3      2       3 7426   70.00   M    2      2       3 7427   67.00   M    8      2       3 7428   56.00   M    1      2       2 7429   36.00   M    0      2       2 7430   63.00   M    2      2       3 7431   54.00   M    2      2       2 7432   60.00   M    4      2       3 7433   66.00   F    2      2       3 7434   56.00   M    0      2       2 7435   27.00   M    2      2       2 7436   60.00   M    0      2       3 7437   55.00   M    1      2       2 7438   73.00   M    1      2       3 7439   54.00   F    0      2       2 7440   60.00   M    1      2       3 7441   54.00   M    0      2       2 7442   40.00   M    0      2       2 7443   60.00   M    1      2       3 7444   62.00   M    1      2       3 7445   68.00   M    7      2       3 7446   97.00   M   29      2       3 7447   75.00   M   19      2       3 7448   62.00   M   15      2       3 7449   80.00   M   11      2       3 7450   40.00   M   15      2       2 7451   71.00   M    9      2       3 7452   59.00   M    3      2       2 7453   62.00   M   14      2       3 7454   75.00   F   15      2       3 7455   65.00   M   12      2       3 7456   86.00   M    4      2       3 7457   80.00   F    3      2       3 7458   62.00   M    7      2       3 7459   80.00   M    7      2       3 7460   83.00   M    0      2       3 7461   65.00   F    5      2       3 7462   53.00   M   11      2       2 7463   64.00   M    2      2       3 7464   52.00   F   11      2       2 7465   72.00   M    8      2       3 7466   66.00   F    6      2       3 7467   63.00   M   15      2       3 7468    0.00   M   10      2       1 7469   68.00   M    1      2       3 7470   82.00   F    2      2       3 7471   62.00   M    4      2       3 7472   70.00   F    8      2       3 7473   62.00   M    7      2       3 7474   89.00   M    8      2       3 7475   69.00   M    9      2       3 7476   80.00   M    8      2       3 7477   50.00   M    4      2       2 7478   80.00   M    9      2       3 7479   42.00   M   10      2       2 7480   56.00   F    6      2       2 7481   85.00   F    8      2       3 7482   85.00   M    3      2       3 7483   69.00   M    6      2       3 7484   61.00   F    9      2       3 7485   78.00   F    9      2       3 7486   81.00   M    2      2       3 7487   71.00   M    4      2       3 7488   58.00   M    0      2       2 7489   54.00   F    5      2       2 7490   62.00   M    7      2       3 7491   69.00   M    5      2       3 7492   93.00   M    0      2       3 7493   64.00   F    4      2       3 7494   50.00   M    3      2       2 7495   52.00   M    9      2       2 7496   65.00   M    2      2       3 7497   74.00   M    7      2       3 7498   68.00   M    2      2       3 7499   30.00   M    5      2       2 7500   77.00   M    7      2       3 7501   64.00   M    5      2       3 7502   66.00   M    6      2       3 7503   70.00   F    1      2       3 7504   65.00   M    6      2       3 7505   80.00   M    4      2       3 7506   70.00   F    1      2       3 7507   66.00   M    6      2       3 7508   68.00   M    3      2       3 7509   80.00   F    1      2       3 7510   45.00   F    0      2       2 7511   64.00   F   15      2       3 7512   86.00   M    0      2       3 7513   66.00   M    5      2       3 7514   65.00   F    3      2       3 7515   72.00   M    8      2       3 7516   70.00   M    1      2       3 7517   53.00   M    3      2       2 7518   73.00   F    5      2       3 7519   47.00   M    0      2       2 7520   48.00   M    4      2       2 7521   45.00   F   12      2       2 7522   62.00   M    2      2       3 7523   56.00   M    1      2       2 7524   55.00   M    2      2       2 7525   71.00   F    3      2       3 7526   70.00   F    2      2       3 7527   70.00   M    2      2       3 7528   60.00   M    1      2       3 7529   54.00   F    1      2       2 7530   73.00   F    0      2       3 7531   30.00   M    0      2       2 7532   52.00   F    0      2       2 7533   19.00   M    0      2       2 7534   65.00   M    0      2       3 7535   62.00   M    2      2       3 7536   36.00   F    1      2       2 7537   42.00   M    0      2       2 7538   61.00   M    0      2       3 7539   45.00   M    0      2       2 7540   72.00   M    0      2       3 7541   56.00   M    0      2       2 7542   54.00   M    0      2       2 7543   74.00   M   23      2       3 7544   68.00   M   25      2       3 7545   50.00   F    4      2       2 7546   60.00   F    6      2       3 7547   50.00   M    4      2       2 7548   68.00   F   19      2       3 7549   68.00   F    1      2       3 7550   58.00   F   19      2       2 7551   68.00   F   16      2       3 7552   51.00   M   13      2       2 7553   50.00   F   11      2       2 7554   66.00   F    2      2       3 7555   51.00   M   14      2       2 7556   75.00   M    9      2       3 7557   67.00   M    1      2       3 7558   68.00   M    7      2       3 7559   61.00   F    1      2       3 7560   74.00   F    1      2       3 7561   52.00   F    7      2       2 7562   92.00   M    1      2       3 7563   66.00   M    3      2       3 7564   67.00   F    6      2       3 7565   50.00   F    3      2       2 7566   67.00   M   10      2       3 7567   35.00   F    1      2       2 7568   65.00   M    2      2       3 7569   65.00   M    0      2       3 7570   72.00   M   12      2       3 7571   45.00   F    1      2       2 7572   62.00   M    4      2       3 7573   66.00   M    3      2       3 7574   52.00   F   12      2       2 7575   56.00   F    8      2       2 7576   53.00   M   12      2       2 7577   66.00   M    9      2       3 7578   43.00   M    0      2       2 7579   79.00   M    5      2       3 7580   60.00   M    6      2       3 7581   57.00   M   12      2       2 7582   88.00   M    5      2       3 7583   66.00   M   11      2       3 7584   70.00   M    2      2       3 7585   45.00   M    6      2       2 7586   65.00   M    5      2       3 7587   80.00   M    1      2       3 7588   65.00   M    5      2       3 7589   74.00   M   12      2       3 7590   76.00   M    0      2       3 7591   36.00   M    6      2       2 7592   65.00   F    0      2       3 7593   47.00   M    1      2       2 7594   85.00   M   38      2       3 7595   40.00   M    0      2       2 7596   73.00   M   10      2       3 7597   60.00   M    5      2       3 7598   54.00   M    5      2       2 7599   56.00   M    1      2       2 7600   74.00   M    1      2       3 7601   65.00   M    4      2       3 7602   53.00   M    1      2       2 7603   67.00   M    9      2       3 7604   77.00   F    1      2       3 7605   68.00   M    1      2       3 7606   78.00   M    4      2       3 7607   72.00   M    4      2       3 7608   85.00   F   10      2       3 7609   75.00   F    5      2       3 7610   56.00   M    2      2       2 7611   54.00   F    1      2       2 7612   60.00   F    5      2       3 7613   68.00   M   10      2       3 7614   47.00   F    3      2       2 7615   50.00   M    4      2       2 7616   83.00   M    9      2       3 7617   85.00   M    8      2       3 7618   75.00   F    9      2       3 7619   64.00   M   10      2       3 7620   65.00   M    6      2       3 7621   78.00   F    4      2       3 7622   67.00   F    4      2       3 7623   58.00   M    2      2       2 7624   58.00   F    2      2       2 7625   65.00   M    1      2       3 7626   70.00   M   31      2       3 7627   65.00   M    0      2       3 7628   70.00   F    4      2       3 7629   59.00   M    7      2       2 7630   64.00   F    5      2       3 7631   60.00   M    2      2       3 7632   70.00   M    4      2       3 7633   57.00   M    5      2       2 7634   72.00   M    6      2       3 7635   42.00   M    0      2       2 7636   74.00   M    3      2       3 7637   68.00   F    3      2       3 7638   45.00   M    3      2       2 7639   59.00   M    6      2       2 7640   55.00   M    5      2       2 7641   70.00   F    8      2       3 7642   52.00   M    5      2       2 7643   32.00   F    1      2       2 7644   43.00   F    1      2       2 7645   55.00   F    3      2       2 7646   42.00   M    5      2       2 7647   49.00   M    2      2       2 7648   68.00   F    0      2       3 7649   68.00   F    2      2       3 7650   87.00   M    6      2       3 7651   20.00   F    1      2       2 7652   49.00   M   14      2       2 7653   50.00   M    0      2       2 7654   55.00   F    3      2       2 7655   57.00   M    4      2       2 7656   45.00   M    5      2       2 7657   63.00   M    3      2       3 7658   34.00   F    1      2       2 7659   70.00   F    1      2       3 7660   62.00   M    0      2       3 7661   66.00   M    2      2       3 7662   54.00   M    3      2       2 7663   65.00   F    5      2       3 7664   84.00   F    1      2       3 7665   70.00   M    1      2       3 7666   69.00   M    3      2       3 7667   85.00   M    2      2       3 7668   62.00   M    1      2       3 7669   72.00   M    1      2       3 7670   50.00   M    0      2       2 7671   70.00   M    0      2       3 7672   60.00   F    0      2       3 7673   70.00   F    4      2       3 7674   76.00   M    9      2       3 7675   71.00   F    4      2       3 7676   60.00   M    0      2       3 7677   64.00   M    3      2       3 7678   68.00   M    1      2       3 7679   68.00   F    5      2       3 7680   67.00   M    5      2       3 7681   52.00   F    0      2       2 7682   85.00   F    7      2       3 7683   56.00   M    3      2       2 7684   70.00   M    0      2       3 7685   45.00   M    7      2       2 7686   63.00   F    3      2       3 7687   64.00   M    5      2       3 7688   87.00   M    2      2       3 7689   40.00   M    3      2       2 7690   86.00   F    1      2       3 7691   45.00   F    3      2       2 7692   67.00   M    1      2       3 7693   50.00   M    2      2       2 7694   58.00   M    3      2       2 7695   55.00   M    1      2       2 7696   75.00   M    3      2       3 7697   58.00   M    0      2       2 7698   21.00   M    0      2       2 7699   54.00   M    2      2       2 7700   56.00   M    3      2       2 7701   70.00   M    2      2       3 7702   73.00   M    0      2       3 7703   84.00   M    1      2       3 7704   75.00   M    1      2       3 7705   48.00   M    1      2       2 7706   50.00   F    1      2       2 7707   56.00   M    0      2       2 7708   80.00   M    3      2       3 7709   75.00   M    2      2       3 7710   46.00   F    2      2       2 7711   62.00   M    1      2       3 7712   65.00   F    2      2       3 7713   65.00   M    2      2       3 7714   69.00   M    1      2       3 7715   96.00   F    3      2       3 7716   79.00   F    6      2       3 7717   70.00   F   34      2       3 7718   73.00   M   23      2       3 7719   86.00   M   13      2       3 7720   59.00   M    8      2       2 7721   75.00   M   16      2       3 7722   70.00   M   17      2       3 7723   82.00   M   19      2       3 7724   34.00   M   17      2       2 7725   60.00   M    3      2       3 7726   65.00   M   14      2       3 7727   62.00   M   13      2       3 7728   59.00   M    6      2       2 7729   65.00   M    0      2       3 7730   78.00   F    0      2       3 7731   50.00   F    0      2       2 7732   63.00   F    5      2       3 7733   65.00   M    6      2       3 7734   21.00   F    3      2       2 7735   57.00   F    8      2       2 7736   67.00   M   18      2       3 7737   51.00   F    0      2       2 7738   71.00   F    0      2       3 7739   78.00   M    0      2       3 7740   69.00   M   17      2       3 7741   60.00   M    4      2       3 7742   84.00   F    0      2       3 7743   63.00   M    0      2       3 7744   70.00   M    1      2       3 7745   52.00   M    8      2       2 7746   36.00   M    2      2       2 7747   65.00   F    0      2       3 7748   72.00   M    2      2       3 7749   30.00   F    7      2       2 7750   74.00   M    0      2       3 7751   49.00   M    0      2       2 7752   75.00   M    1      2       3 7753   60.00   M   13      2       3 7754   49.00   M    2      2       2 7755   73.00   M   25      2       3 7756   57.00   M    1      2       2 7757   82.00   M    1      2       3 7758   75.00   M    6      2       3 7759   68.00   M    9      2       3 7760   50.00   M    4      2       2 7761   51.00   F    5      2       2 7762   84.00   M    2      2       3 7763   72.00   M    0      2       3 7764   66.00   M    5      2       3 7765   60.00   F    0      2       3 7766   72.00   M    7      2       3 7767   72.00   M    7      2       3 7768   59.00   M    6      2       2 7769   62.00   M   13      2       3 7770   68.00   M    3      2       3 7771   35.00   F    0      2       2 7772   85.00   F    2      2       3 7773   59.00   F    3      2       2 7774   85.00   M    2      2       3 7775   57.00   F    5      2       2 7776   39.00   M    4      2       2 7777   62.00   F    1      2       3 7778   55.00   M    5      2       2 7779   64.00   M    7      2       3 7780   61.00   F    2      2       3 7781   65.00   M    1      2       3 7782   51.00   M    6      2       2 7783   75.00   M    2      2       3 7784   72.00   M    4      2       3 7785   75.00   M    5      2       3 7786   62.00   M    0      2       3 7787   70.00   F    0      2       3 7788   65.00   F    2      2       3 7789   60.00   F    4      2       3 7790   70.00   F    5      2       3 7791   44.00   M    5      2       2 7792   58.00   M    0      2       2 7793   62.00   M    5      2       3 7794   50.00   M   14      2       2 7795   81.00   M   18      2       3 7796   64.00   M    2      2       3 7797   31.00   M    1      2       2 7798   73.00   M    0      2       3 7799   60.00   F    5      2       3 7800   64.00   M    1      2       3 7801   83.00   M    0      2       3 7802   48.00   M    0      2       2 7803   54.00   M    2      2       2 7804   65.00   M    2      2       3 7805   60.00   M    0      2       3 7806   68.00   M    0      2       3 7807   53.00   M    0      2       2 7808   38.00   M    1      2       2 7809   63.00   M    1      2       3 7810   61.00   M    1      2       3 7811   65.00   F    3      2       3 7812   73.00   M    0      2       3 7813   71.00   M    1      2       3 7814   62.00   F    1      2       3 7815   44.00   M    1      2       2 7816   53.00   M    1      2       2 7817   70.00   M    0      2       3 7818   63.00   M    1      2       3 7819   55.00   F    2      2       2 7820   42.00   M    1      2       2 7821   52.00   M   10      2       2 7822   73.00   M    0      2       3 7823   69.00   M    2      2       3 7824   88.00   M    0      2       3 7825   69.00   M    0      2       3 7826   65.00   F    1      2       3 7827   72.00   M    1      2       3 7828   55.00   F    1      2       2 7829   46.00   F    2      2       2 7830   56.00   M    0      2       2 7831   65.00   M   17      2       3 7832   67.00   M    0      2       3 7833   64.00   M    0      2       3 7834   60.00   F   13      2       3 7835   52.00   M    0      2       2 7836   63.00   M    5      2       3 7837   72.00   M   13      2       3 7838   52.00   M   13      2       2 7839   50.00   F   16      2       2 7840   68.00   M   10      2       3 7841   68.00   M    8      2       3 7842   57.00   M    2      2       2 7843   50.00   F    1      2       2 7844   60.00   M    6      2       3 7845   62.00   M   10      2       3 7846   59.00   F    0      2       2 7847   68.00   F    3      2       3 7848   96.00   M    5      2       3 7849   80.00   M   12      2       3 7850   80.00   M    7      2       3 7851   65.00   F    3      2       3 7852   64.00   F    2      2       3 7853   40.00   F    9      2       2 7854   72.00   M    6      2       3 7855   50.00   M    7      2       2 7856   51.00   F    5      2       2 7857   65.00   F    6      2       3 7858   75.00   M    7      2       3 7859   80.00   M    1      2       3 7860   49.00   M    0      2       2 7861   66.00   F    7      2       3 7862   12.00   M    0      2       1 7863   85.00   M    8      2       3 7864   76.00   M    4      2       3 7865   60.00   F    1      2       3 7866   80.00   F    4      2       3 7867   75.00   M    8      2       3 7868   33.00   M    9      2       2 7869   73.00   F    6      2       3 7870   80.00   F    2      2       3 7871   19.00   F    1      2       2 7872   54.00   M    8      2       2 7873   66.00   M    5      2       3 7874   57.00   M    4      2       2 7875   62.00   M    2      2       3 7876   83.00   M    1      2       3 7877   65.00   M    0      2       3 7878   50.00   F    4      2       2 7879   50.00   F    6      2       2 7880   32.00   M    2      2       2 7881   77.00   F    1      2       3 7882   55.00   F    3      2       2 7883   76.00   M    1      2       3 7884   61.00   M    0      2       3 7885   47.00   M    5      2       2 7886   50.00   M    5      2       2 7887   55.00   M    4      2       2 7888   73.00   F    6      2       3 7889   64.00   M    1      2       3 7890   85.00   F    6      2       3 7891   35.00   M    4      2       2 7892   55.00   F    2      2       2 7893   68.00   M    3      2       3 7894   50.00   F    2      2       2 7895   75.00   M    0      2       3 7896   76.00   F    1      2       3 7897   56.00   M    3      2       2 7898   48.00   F   13      2       2 7899   80.00   M    1      2       3 7900   72.00   M    3      2       3 7901   81.00   F    1      2       3 7902    1.00   F    0      2       1 7903   40.00   F    4      2       2 7904   69.00   M    4      2       3 7905   64.00   M    2      2       3 7906   72.00   M    7      2       3 7907   93.00   M    1      2       3 7908   97.00   M    5      2       3 7909   32.00   M    2      2       2 7910   75.00   F    2      2       3 7911   54.00   M    1      2       2 7912   38.00   F    1      2       2 7913   64.00   M    1      2       3 7914   60.00   F    1      2       3 7915   61.00   M   10      2       3 7916   60.00   M    0      2       3 7917   78.00   F    0      2       3 7918   65.00   F    2      2       3 7919   59.00   M    0      2       2 7920   58.00   M    2      2       2 7921   85.00   M    2      2       3 7922   55.00   M    0      2       2 7923   73.00   M    3      2       3 7924   48.00   F    4      2       2 7925   43.00   F    4      2       2 7926   84.00   M    1      2       3 7927   85.00   M    0      2       3 7928   38.00   M    0      2       2 7929   51.00   M    1      2       2 7930   60.00   M    0      2       3 7931   70.00   M    0      2       3 7932   85.00   M   15      2       3 7933   61.00   M   16      2       3 7934   75.00   M   21      2       3 7935   62.00   M   11      2       3 7936   75.00   M   17      2       3 7937   75.00   F   11      2       3 7938   70.00   M    1      2       3 7939   60.00   F   14      2       3 7940   50.00   M    1      2       2 7941   75.00   M   14      2       3 7942   63.00   F   13      2       3 7943   35.00   M   10      2       2 7944   56.00   M    7      2       2 7945   75.00   M    0      2       3 7946   69.00   M    6      2       3 7947   68.00   M    3      2       3 7948   70.00   M    5      2       3 7949   50.00   M   11      2       2 7950   65.00   F    4      2       3 7951   62.00   F    1      2       3 7952   72.00   M    0      2       3 7953   63.00   M    8      2       3 7954   75.00   F    6      2       3 7955   80.00   M    2      2       3 7956   72.00   F    5      2       3 7957   60.00   F    9      2       3 7958   75.00   M    0      2       3 7959   63.00   M   10      2       3 7960   65.00   F    2      2       3 7961   70.00   M    2      2       3 7962   84.00   M    9      2       3 7963   70.00   F    8      2       3 7964   65.00   M    8      2       3 7965   85.00   M   10      2       3 7966   58.00   M    2      2       2 7967   85.00   M    1      2       3 7968   61.00   M    4      2       3 7969   66.00   F    7      2       3 7970   80.00   F    8      2       3 7971   58.00   F    6      2       2 7972   66.00   M   10      2       3 7973   71.00   M    7      2       3 7974   59.00   M    8      2       2 7975   65.00   F    7      2       3 7976   78.00   M    4      2       3 7977   80.00   M    3      2       3 7978   82.00   M    3      2       3 7979   62.00   F    4      2       3 7980   82.00   M    2      2       3 7981   48.00   M    5      2       2 7982   62.00   M    3      2       3 7983   60.00   M    3      2       3 7984   77.00   M    5      2       3 7985   40.00   M    6      2       2 7986   50.00   M    5      2       2 7987   73.00   M    5      2       3 7988   65.00   F    7      2       3 7989   48.00   F    5      2       2 7990   64.00   M    3      2       3 7991   67.00   M    2      2       3 7992   65.00   F    4      2       3 7993   65.00   M    0      2       3 7994   53.00   M    3      2       2 7995   65.00   M    4      2       3 7996   66.00   M    5      2       3 7997   71.00   M    3      2       3 7998   52.00   M    0      2       2 7999   71.00   F    6      2       3 8000   75.00   M    6      2       3 8001   72.00   M    3      2       3 8002   66.00   M    2      2       3 8003   42.00   M    2      2       2 8004   67.00   M    3      2       3 8005   53.00   F    1      2       2 8006   49.00   M    2      2       2 8007   62.00   M   10      2       3 8008   63.00   M    2      2       3 8009   69.00   M    1      2       3 8010   65.00   F    1      2       3 8011   58.00   M    2      2       2 8012   72.00   M    2      2       3 8013   43.00   M    1      2       2 8014   70.00   M    2      2       3 8015   42.00   M    0      2       2 8016   65.00   M    4      2       3 8017   66.00   M    4      2       3 8018   70.00   F    0      2       3 8019   50.00   M    1      2       2 8020   70.00   M    1      2       3 8021   35.00   M    0      2       2 8022   65.00   M    3      2       3 8023   65.00   M    2      2       3 8024   59.00   M   15      2       2 8025   60.00   M    0      2       3 8026   65.00   M    1      2       3 8027   72.00   F    0      2       3 8028   60.00   M    1      2       3 8029   48.00   M    1      2       2 8030   58.00   F    2      2       2 8031   58.00   M    0      2       2 8032   49.00   M    2      2       2 8033   66.00   M    3      2       3 8034   70.00   M    2      2       3 8035   61.00   M    4      2       3 8036   60.00   F    3      2       3 8037   65.00   M    2      2       3 8038   80.00   M   23      2       3 8039   85.00   M    2      2       3 8040   70.00   M    1      2       3 8041   50.00   F    0      2       2 8042   25.00   M    1      2       2 8043   62.00   M    1      2       3 8044   52.00   M    1      2       2 8045   68.00   M    2      2       3 8046   59.00   F    2      2       2 8047   55.00   M    0      2       2 8048   65.00   M    0      2       3 8049   60.00   M    0      2       3 8050   65.00   F    0      2       3 8051   45.00   F    1      2       2 8052   42.00   M    2      2       2 8053   52.00   F    1      2       2 8054   65.00   F   19      2       3 8055   65.00   M   22      2       3 8056   26.00   M   24      2       2 8057   58.00   F    3      2       2 8058   56.00   M   17      2       2 8059   65.00   F   14      2       3 8060   76.00   M   22      2       3 8061   77.00   F   17      2       3 8062   40.00   M    1      2       2 8063   71.00   M    0      2       3 8064   68.00   M    6      2       3 8065   49.00   M    6      2       2 8066   74.00   M    7      2       3 8067   75.00   F   14      2       3 8068   71.00   F    0      2       3 8069   75.00   F    1      2       3 8070   60.00   M    0      2       3 8071   58.00   M    1      2       2 8072   72.00   M    5      2       3 8073   74.00   M    7      2       3 8074   93.00   M    2      2       3 8075   73.00   M    3      2       3 8076   45.00   M   11      2       2 8077   70.00   M    1      2       3 8078   79.00   M    7      2       3 8079   70.00   M    0      2       3 8080   65.00   M    7      2       3 8081   89.00   M    4      2       3 8082   68.00   M    6      2       3 8083   68.00   M    0      2       3 8084   73.00   M    3      2       3 8085   65.00   M    5      2       3 8086   57.00   F    1      2       2 8087   41.00   F   11      2       2 8088   72.00   M    0      2       3 8089   48.00   M    7      2       2 8090   40.00   M    0      2       2 8091   60.00   M    6      2       3 8092   38.00   M    6      2       2 8093   55.00   M    0      2       2 8094   71.00   F    6      2       3 8095   64.00   F    6      2       3 8096   65.00   M    8      2       3 8097   63.00   M    3      2       3 8098   65.00   F    3      2       3 8099   60.00   M    1      2       3 8100   65.00   M    4      2       3 8101   70.00   M    3      2       3 8102   64.00   M    4      2       3 8103   62.00   M    3      2       3 8104   58.00   M    4      2       2 8105   23.00   M    5      2       2 8106   95.00   M    5      2       3 8107   45.00   M    0      2       2 8108   57.00   M    4      2       2 8109   52.00   F    6      2       2 8110   43.00   M    7      2       2 8111   52.00   M    3      2       2 8112   82.00   M    3      2       3 8113   67.00   M    1      2       3 8114   58.00   M    2      2       2 8115   55.00   F    2      2       2 8116   56.00   F    3      2       2 8117   46.00   M    1      2       2 8118   95.00   M    2      2       3 8119   52.00   M    2      2       2 8120   71.00   M    5      2       3 8121   40.00   F    2      2       2 8122   83.00   M    0      2       3 8123   50.00   M    2      2       2 8124   74.00   M    3      2       3 8125   65.00   F    1      2       3 8126   52.00   M    4      2       2 8127   84.00   F    0      2       3 8128   72.00   M    3      2       3 8129   86.00   M    3      2       3 8130   67.00   F    0      2       3 8131    1.00   M    0      2       1 8132   60.00   M    4      2       3 8133   60.00   F    3      2       3 8134   58.00   M    2      2       2 8135   88.00   M    0      2       3 8136   74.00   M    0      2       3 8137   65.00   M   27      2       3 8138   76.00   M   15      2       3 8139   58.00   M    0      2       2 8140   67.00   M    8      2       3 8141   49.00   M    0      2       2 8142   70.00   M    2      2       3 8143   44.00   M   12      2       2 8144   64.00   M    2      2       3 8145   67.00   F    6      2       3 8146   58.00   F    7      2       2 8147   70.00   F    7      2       3 8148   50.00   M    6      2       2 8149   56.00   M    5      2       2 8150   83.00   M    1      2       3 8151   67.00   M    3      2       3 8152   77.00   M   17      2       3 8153   76.00   M    7      2       3 8154   75.00   F    1      2       3 8155   54.00   F    0      2       2 8156   70.00   M    2      2       3 8157   68.00   M    3      2       3 8158   46.00   M    1      2       2 8159   79.00   F    2      2       3 8160   82.00   M    6      2       3 8161   69.00   M    5      2       3 8162   83.00   F    8      2       3 8163   65.00   M    3      2       3 8164   78.00   M    2      2       3 8165   65.00   M    2      2       3 8166   48.00   F    4      2       2 8167   48.00   F    2      2       2 8168   89.00   F    2      2       3 8169   68.00   F    6      2       3 8170   75.00   M    3      2       3 8171   54.00   F    1      2       2 8172   41.00   M    0      2       2 8173   65.00   M    0      2       3 8174   60.00   F    1      2       3 8175   72.00   M   34      2       3 8176   64.00   M   23      2       3 8177   82.00   M   13      2       3 8178   52.00   M   17      2       2 8179   65.00   M    3      2       3 8180   85.00   M   12      2       3 8181   74.00   M   10      2       3 8182   67.00   F   12      2       3 8183   62.00   F    2      2       3 8184   78.00   M   13      2       3 8185   58.00   F    9      2       2 8186   50.00   M   14      2       2 8187   62.00   F    0      2       3 8188   85.00   M    2      2       3 8189   65.00   M    2      2       3 8190   69.00   M   10      2       3 8191   56.00   F    3      2       2 8192   67.00   M    3      2       3 8193   52.00   M    9      2       2 8194   81.00   M   11      2       3 8195   71.00   M    8      2       3 8196   74.00   M    3      2       3 8197   67.00   M    7      2       3 8198   45.00   F    7      2       2 8199   62.00   M   11      2       3 8200   54.00   M    0      2       2 8201   86.00   M    7      2       3 8202   32.00   M    0      2       2 8203   63.00   M    9      2       3 8204   65.00   M    7      2       3 8205   58.00   M    5      2       2 8206   50.00   M    0      2       2 8207   80.00   M    0      2       3 8208   59.00   F    4      2       2 8209   30.00   F    3      2       2 8210   72.00   F    3      2       3 8211   73.00   M    5      2       3 8212   95.00   M    3      2       3 8213   67.00   M    3      2       3 8214   53.00   M    4      2       2 8215   60.00   F    0      2       3 8216   80.00   F    4      2       3 8217   62.00   M    1      2       3 8218   54.00   M    0      2       2 8219   75.00   M    2      2       3 8220   85.00   F    3      2       3 8221   71.00   F    7      2       3 8222   71.00   F    2      2       3 8223   40.00   M    0      2       2 8224   72.00   M    1      2       3 8225   54.00   M    1      2       2 8226   97.00   M    1      2       3 8227   60.00   M    1      2       3 8228   52.00   M    0      2       2 8229   76.00   M    0      2       3 8230   88.00   M    0      2       3 8231   82.00   M    1      2       3 8232   70.00   M    2      2       3 8233   54.00   F    1      2       2 8234   73.00   M    0      2       3 8235   70.00   M    0      2       3 8236   67.00   M    4      2       3 8237   50.00   M    0      2       2 8238   70.00   F    1      2       3 8239   60.00   M    0      2       3 8240   33.00   F    2      2       2 8241   60.00   F    5      2       3 8242   79.00   M    4      2       3 8243   74.00   M   21      2       3 8244   59.00   M   14      2       2 8245   60.00   F   11      2       3 8246   81.00   M    8      2       3 8247   65.00   F    7      2       3 8248   65.00   M   12      2       3 8249   50.00   M   17      2       2 8250   65.00   M   12      2       3 8251   84.00   M   17      2       3 8252   60.00   M   13      2       3 8253   69.00   M   18      2       3 8254   75.00   M   17      2       3 8255   87.00   F   12      2       3 8256   68.00   M   11      2       3 8257   46.00   F    8      2       2 8258   67.00   M   17      2       3 8259   56.00   M    9      2       2 8260   60.00   M   10      2       3 8261   61.00   M    8      2       3 8262   68.00   F    8      2       3 8263   76.00   M    8      2       3 8264   63.00   M   12      2       3 8265   79.00   M    7      2       3 8266   45.00   M   10      2       2 8267   73.00   M   12      2       3 8268   62.00   M    2      2       3 8269   45.00   M    8      2       2 8270   61.00   M    5      2       3 8271   66.00   M   23      2       3 8272   85.00   M   10      2       3 8273   47.00   M    3      2       2 8274   74.00   M   10      2       3 8275   51.00   M    4      2       2 8276   60.00   M    4      2       3 8277   65.00   F    1      2       3 8278   75.00   M    0      2       3 8279   67.00   M    5      2       3 8280   50.00   M    1      2       2 8281   70.00   M    0      2       3 8282   60.00   M    1      2       3 8283   57.00   M    1      2       2 8284   29.00   M    1      2       2 8285   64.00   M    4      2       3 8286   55.00   M    3      2       2 8287   50.00   M    0      2       2 8288   66.00   F    0      2       3 8289   52.00   M    2      2       2 8290   73.00   M    9      2       3 8291   54.00   M    9      2       2 8292   80.00   F    0      2       3 8293   57.00   F    8      2       2 8294   58.00   M    1      2       2 8295   65.00   F    2      2       3 8296   56.00   M    6      2       2 8297   67.00   M    2      2       3 8298   64.00   M    1      2       3 8299   40.00   M   23      2       2 8300   52.00   F    2      2       2 8301   59.00   M    1      2       2 8302   76.00   M    2      2       3 8303   73.00   F    4      2       3 8304   60.00   M    0      2       3 8305   70.00   F    1      2       3 8306   31.00   M    0      2       2 8307   60.00   F    0      2       3 8308   53.00   M    0      2       2 8309   74.00   M    1      2       3 8310   85.00   M    0      2       3 8311   60.00   M    1      2       3 8312   80.00   M    0      2       3 8313   60.00   M    1      2       3 8314   67.00   M    2      2       3 8315   40.00   M    3      2       2 8316   64.00   M    4      2       3 8317   35.00   M    1      2       2 8318   85.00   M    1      2       3 8319   45.00   M    0      2       2 8320   65.00   F    4      2       3 8321   70.00   M    0      2       3 8322   70.00   M    0      2       3 8323   65.00   M    1      2       3 8324   70.00   M    0      2       3 8325   56.00   M    3      2       2 8326   71.00   M   28      2       3 8327   45.00   M   18      2       2 8328   75.00   M   16      2       3 8329   72.00   F   21      2       3 8330   74.00   M    9      2       3 8331   69.00   M   16      2       3 8332   65.00   M    9      2       3 8333   83.00   F    6      2       3 8334   65.00   M   14      2       3 8335   40.00   M   12      2       2 8336   58.00   M   11      2       2 8337   56.00   M   11      2       2 8338   60.00   M    8      2       3 8339   83.00   M    5      2       3 8340   75.00   M    1      2       3 8341   63.00   M    8      2       3 8342   45.00   M   12      2       2 8343   65.00   F   10      2       3 8344   60.00   M    9      2       3 8345   50.00   M   10      2       2 8346   85.00   M    2      2       3 8347   80.00   M    4      2       3 8348   70.00   F    9      2       3 8349   76.00   F   10      2       3 8350   63.00   M    7      2       3 8351   65.00   M   11      2       3 8352   62.00   F    3      2       3 8353   47.00   M    1      2       2 8354   66.00   M   10      2       3 8355   62.00   M    9      2       3 8356   57.00   M    3      2       2 8357   65.00   M    2      2       3 8358   75.00   M    9      2       3 8359   78.00   M    4      2       3 8360   85.00   M    3      2       3 8361   48.00   M    4      2       2 8362   42.00   M   10      2       2 8363   35.00   M    8      2       2 8364   82.00   F    5      2       3 8365   43.00   M    2      2       2 8366   72.00   F    2      2       3 8367   55.00   M    4      2       2 8368   60.00   M    2      2       3 8369   65.00   F    8      2       3 8370   72.00   F    3      2       3 8371   74.00   M    2      2       3 8372   56.00   M    3      2       2 8373   66.00   F    1      2       3 8374   62.00   F   10      2       3 8375   58.00   F    0      2       2 8376   65.00   M    6      2       3 8377   75.00   F    6      2       3 8378   60.00   M    2      2       3 8379   62.00   M    0      2       3 8380   65.00   M    3      2       3 8381   66.00   F    3      2       3 8382   74.00   M    2      2       3 8383   72.00   F    3      2       3 8384   66.00   M    2      2       3 8385   76.00   M    7      2       3 8386   24.00   M    1      2       2 8387   62.00   M    1      2       3 8388   59.00   F    4      2       2 8389   50.00   M    6      2       2 8390   75.00   M    5      2       3 8391   60.00   M    1      2       3 8392   55.00   F    1      2       2 8393   61.00   M   17      2       3 8394   34.00   M    0      2       2 8395   74.00   M    3      2       3 8396   67.00   F    0      2       3 8397   62.00   F    3      2       3 8398   79.00   M    5      2       3 8399   65.00   F    2      2       3 8400   68.00   M    1      2       3 8401   57.00   M    0      2       2 8402   40.00   F    2      2       2 8403   65.00   M    1      2       3 8404   70.00   F    0      2       3 8405   40.00   F    3      2       2 8406   45.00   F    1      2       2 8407   38.00   M    1      2       2 8408   70.00   F    2      2       3 8409   40.00   M    1      2       2 8410   58.00   M    0      2       2 8411   65.00   F    0      2       3 8412   50.00   M    0      2       2 8413   68.00   M   16      2       3 8414   56.00   M   12      2       2 8415   68.00   M    8      2       3 8416   79.00   M   12      2       3 8417   84.00   F   13      2       3 8418   43.00   M   15      2       2 8419   80.00   F    7      2       3 8420   60.00   M    5      2       3 8421   52.00   M   13      2       2 8422   68.00   M    2      2       3 8423   82.00   M    3      2       3 8424   68.00   F    8      2       3 8425   57.00   M    8      2       2 8426   70.00   M   10      2       3 8427   48.00   M   10      2       2 8428   65.00   M    8      2       3 8429   50.00   F    3      2       2 8430   60.00   F    5      2       3 8431   72.00   M    7      2       3 8432   78.00   M   13      2       3 8433   62.00   M    8      2       3 8434   61.00   M    7      2       3 8435   62.00   F    6      2       3 8436   75.00   M    3      2       3 8437   56.00   F    7      2       2 8438   60.00   F    2      2       3 8439   28.00   M   13      2       2 8440   62.00   M    4      2       3 8441   40.00   F    4      2       2 8442   74.00   M    9      2       3 8443   67.00   F    0      2       3 8444   56.00   M    5      2       2 8445   55.00   M    6      2       2 8446   78.00   M    1      2       3 8447   80.00   M    5      2       3 8448   46.00   M    5      2       2 8449   90.00   M    3      2       3 8450   46.00   M    4      2       2 8451   45.00   M    5      2       2 8452   48.00   F    2      2       2 8453   72.00   F    6      2       3 8454   65.00   M    1      2       3 8455   66.00   F    3      2       3 8456   70.00   M    0      2       3 8457   48.00   F   11      2       2 8458   84.00   F    5      2       3 8459   94.00   F    2      2       3 8460   65.00   M    1      2       3 8461   40.00   M    0      2       2 8462   76.00   M    7      2       3 8463   47.00   M    7      2       2 8464   76.00   F    4      2       3 8465   71.00   M    4      2       3 8466   34.00   M    0      2       2 8467   80.00   F    2      2       3 8468   52.00   F    2      2       2 8469   88.00   F    0      2       3 8470   65.00   M    1      2       3 8471   55.00   M    1      2       2 8472   30.00   F    3      2       2 8473   60.00   F    3      2       3 8474   61.00   M    1      2       3 8475   73.00   M    1      2       3 8476   75.00   M    0      2       3 8477   73.00   M    2      2       3 8478   62.00   M    1      2       3 8479   59.00   F    1      2       2 8480   76.00   M    1      2       3 8481   68.00   M    3      2       3 8482   70.00   M    0      2       3 8483   80.00   M   10      2       3 8484   45.00   F    0      2       2 8485   37.00   M    2      2       2 8486   80.00   M    2      2       3 8487   55.00   M    1      2       2 8488   72.00   F    1      2       3 8489   75.00   M    0      2       3 8490   46.00   F    1      2       2 8491   72.00   F    0      2       3 8492   80.00   M    9      2       3 8493   83.00   M   19      2       3 8494   36.00   M   13      2       2 8495   55.00   M    0      2       2 8496   62.00   M   18      2       3 8497   58.00   F    7      2       2 8498   68.00   M    8      2       3 8499   72.00   F   10      2       3 8500   65.00   M    2      2       3 8501   55.00   M    8      2       2 8502   72.00   M    9      2       3 8503   63.00   M   10      2       3 8504   56.00   M    1      2       2 8505   70.00   F   12      2       3 8506   40.00   M    0      2       2 8507   62.00   M   11      2       3 8508   70.00   M    0      2       3 8509   72.00   M    7      2       3 8510   85.00   F    9      2       3 8511   65.00   M    1      2       3 8512   60.00   F    9      2       3 8513   45.00   F    7      2       2 8514   56.00   F    9      2       2 8515   63.00   M    2      2       3 8516   72.00   M    4      2       3 8517   41.00   F    0      2       2 8518   57.00   M    4      2       2 8519   70.00   M    0      2       3 8520   57.00   F    0      2       2 8521   91.00   M    9      2       3 8522   75.00   M    0      2       3 8523   58.00   M    4      2       2 8524   70.00   F    0      2       3 8525   46.00   M    5      2       2 8526   63.00   M    4      2       3 8527   71.00   F    3      2       3 8528   35.00   F    3      2       2 8529   55.00   M    1      2       2 8530   46.00   M    3      2       2 8531   65.00   M    6      2       3 8532   67.00   M    5      2       3 8533   52.00   F    4      2       2 8534   62.00   M    2      2       3 8535   50.00   F    2      2       2 8536   58.00   F    0      2       2 8537   54.00   M    2      2       2 8538   65.00   M    1      2       3 8539   81.00   F    4      2       3 8540   80.00   M    1      2       3 8541   35.00   M    1      2       2 8542   50.00   M   19      2       2 8543   88.00   M    0      2       3 8544   52.00   F    1      2       2 8545   65.00   M    1      2       3 8546   46.00   M    1      2       2 8547   60.00   M    0      2       3 8548   56.00   M    0      2       2 8549   55.00   F    1      2       2 8550   84.00   F    1      2       3 8551   41.00   M    0      2       2 8552   51.00   M    0      2       2 8553   23.00   F    0      2       2 8554   71.00   F    1      2       3 8555   60.00   F    8      2       3 8556   52.00   M    4      2       2 8557   56.00   M    0      2       2 8558   61.00   M    1      2       3 8559   50.00   M    1      2       2 8560   52.00   M    0      2       2 8561   59.00   M    0      2       2 8562   71.00   M    3      2       3 8563   88.00   F    5      2       3 8564   52.00   F    0      2       2 8565   54.00   F    1      2       2 8566   48.00   M    0      2       2 8567   40.00   F    1      2       2 8568   58.00   F    0      2       2 8569   59.00   M    0      2       2 8570   37.00   M    1      2       2 8571   47.00   M    1      2       2 8572   63.00   M    3      2       3 8573   63.00   F    0      2       3 8574   46.00   M    0      2       2 8575   64.00   M    2      2       3 8576   54.00   M    0      2       2 8577   71.00   M    0      2       3 8578   64.00   M    4      2       3 8579   44.00   M    2      2       2 8580   74.00   F    2      2       3 8581   83.00   M    0      2       3 8582   65.00   F    2      2       3 8583   70.00   F    0      2       3 8584   58.00   F    2      2       2 8585   45.00   M    0      2       2 8586   62.00   M   10      2       3 8587   54.00   M    0      2       2 8588   40.00   M    0      2       2 8589   45.00   M   26      2       2 8590   52.00   M   23      2       2 8591   47.00   M    1      2       2 8592   58.00   M    1      2       2 8593   66.00   M    4      2       3 8594   79.00   F    7      2       3 8595   52.00   M    8      2       2 8596   59.00   M    4      2       2 8597   60.00   F    0      2       3 8598   45.00   F   20      2       2 8599   62.00   F    6      2       3 8600   84.00   M    9      2       3 8601   56.00   F    0      2       2 8602   77.00   F    2      2       3 8603   63.00   M    8      2       3 8604   68.00   F    4      2       3 8605   60.00   M   14      2       3 8606   69.00   F   16      2       3 8607   85.00   F   12      2       3 8608   45.00   M   14      2       2 8609   55.00   M    0      2       2 8610   66.00   M    9      2       3 8611   60.00   F    0      2       3 8612   55.00   M    0      2       2 8613   81.00   M   11      2       3 8614   62.00   M    8      2       3 8615   42.00   M   15      2       2 8616   69.00   M   19      2       3 8617   75.00   M    5      2       3 8618   68.00   F    4      2       3 8619   64.00   M    4      2       3 8620   60.00   M    0      2       3 8621   40.00   M   16      2       2 8622   40.00   F    5      2       2 8623   61.00   M    9      2       3 8624   23.00   M    1      2       2 8625   65.00   M    3      2       3 8626   69.00   M    9      2       3 8627   62.00   M   10      2       3 8628   60.00   M    1      2       3 8629   74.00   M    0      2       3 8630   85.00   M    3      2       3 8631   65.00   M    6      2       3 8632   61.00   M    8      2       3 8633   66.00   M    4      2       3 8634   55.00   M    8      2       2 8635   65.00   F    7      2       3 8636   69.00   M    9      2       3 8637   65.00   F   12      2       3 8638   62.00   M    7      2       3 8639   55.00   M    3      2       2 8640   75.00   M    9      2       3 8641   53.00   F   15      2       2 8642   78.00   M    6      2       3 8643   76.00   F    8      2       3 8644   42.00   M    7      2       2 8645   70.00   M    0      2       3 8646   50.00   M    2      2       2 8647   56.00   M    5      2       2 8648   70.00   M    4      2       3 8649   47.00   F    1      2       2 8650   65.00   M    1      2       3 8651   78.00   F    5      2       3 8652   74.00   M    0      2       3 8653    5.00   M    4      2       1 8654   92.00   M    1      2       3 8655   76.00   M    5      2       3 8656   69.00   F    4      2       3 8657   67.00   M    4      2       3 8658   78.00   M    1      2       3 8659   60.00   F    6      2       3 8660   55.00   M    4      2       2 8661   65.00   M    4      2       3 8662   47.00   M    3      2       2 8663   60.00   M    2      2       3 8664   82.00   M    0      2       3 8665   58.00   M    2      2       2 8666   71.00   M    3      2       3 8667   84.00   M    2      2       3 8668   59.00   M    0      2       2 8669   72.00   M    1      2       3 8670   42.00   M    1      2       2 8671   60.00   M    1      2       3 8672   82.00   F    1      2       3 8673   48.00   F    2      2       2 8674   68.00   M    3      2       3 8675   86.00   M    2      2       3 8676   75.00   F    1      2       3 8677   23.00   M    0      2       2 8678   68.00   F    2      2       3 8679   90.00   M    1      2       3 8680   70.00   F    1      2       3 8681   50.00   M    4      2       2 8682   45.00   F    4      2       2 8683   68.00   M    4      2       3 8684   73.00   M    1      2       3 8685   81.00   M   18      2       3 8686   84.00   F    7      2       3 8687   73.00   M   22      2       3 8688   67.00   M   10      2       3 8689   80.00   F   20      2       3 8690   52.00   F   16      2       2 8691   62.00   M    2      2       3 8692   73.00   F    7      2       3 8693   66.00   M    0      2       3 8694   70.00   F   11      2       3 8695   42.00   M    1      2       2 8696   66.00   M    6      2       3 8697   65.00   M   11      2       3 8698   67.00   M    7      2       3 8699   55.00   F    8      2       2 8700   65.00   M    0      2       3 8701   40.00   M    1      2       2 8702   55.00   M    9      2       2 8703   66.00   M    0      2       3 8704   62.00   M    2      2       3 8705   78.00   F    7      2       3 8706   78.00   M    0      2       3 8707   70.00   M    7      2       3 8708   75.00   M   10      2       3 8709   50.00   M    0      2       2 8710   45.00   F    2      2       2 8711   68.00   F    4      2       3 8712   53.00   M    7      2       2 8713   59.00   M    5      2       2 8714   68.00   M    0      2       3 8715   75.00   M    7      2       3 8716   70.00   M    7      2       3 8717   63.00   M    9      2       3 8718   90.00   M    6      2       3 8719   78.00   M    8      2       3 8720   82.00   F    6      2       3 8721   56.00   M    4      2       2 8722   40.00   M    1      2       2 8723   48.00   M    3      2       2 8724   73.00   M    3      2       3 8725   76.00   M    6      2       3 8726   59.00   M    5      2       2 8727   35.00   M    7      2       2 8728   59.00   M    4      2       2 8729   48.00   M    0      2       2 8730   52.00   M    3      2       2 8731   55.00   M    7      2       2 8732   65.00   F    3      2       3 8733   82.00   F    2      2       3 8734   72.00   M    2      2       3 8735   76.00   M    4      2       3 8736   60.00   F    9      2       3 8737   70.00   M    8      2       3 8738   53.00   F    0      2       2 8739   67.00   F    4      2       3 8740   74.00   M    5      2       3 8741   74.00   M    1      2       3 8742   75.00   M    3      2       3 8743   42.00   M    2      2       2 8744   82.00   M    0      2       3 8745   58.00   M    3      2       2 8746   63.00   M    0      2       3 8747   79.00   M   32      2       3 8748   85.00   F    2      2       3 8749   79.00   M    5      2       3 8750   69.00   M    2      2       3 8751   47.00   F    5      2       2 8752   65.00   M    1      2       3 8753   80.00   M    0      2       3 8754   72.00   M    2      2       3 8755   60.00   M    3      2       3 8756   36.00   M    2      2       2 8757   62.00   M    4      2       3 8758   50.00   M    0      2       2 8759   67.00   F    0      2       3 8760   62.00   M    3      2       3 8761   68.00   M    3      2       3 8762   70.00   F    2      2       3 8763   85.00   M    2      2       3 8764   74.00   F    1      2       3 8765   51.00   M    2      2       2 8766   54.00   M    1      2       2 8767   52.00   M    0      2       2 8768   86.00   M    2      2       3 8769   77.00   M    2      2       3 8770   45.00   M    0      2       2 8771   51.00   M    1      2       2 8772   70.00   F    3      2       3 8773   58.00   M   23      2       2 8774   28.00   M    9      2       2 8775   58.00   M    0      2       2 8776   48.00   M    1      2       2 8777   48.00   M    7      2       2 8778   59.00   M    5      2       2 8779   90.00   M    2      2       3 8780   60.00   F    8      2       3 8781   74.00   M    4      2       3 8782   72.00   F    1      2       3 8783   65.00   F    2      2       3 8784   70.00   M   14      2       3 8785   77.00   M    0      2       3 8786   52.00   M   10      2       2 8787   77.00   M    3      2       3 8788   66.00   M   18      2       3 8789   64.00   M   24      2       3 8790   79.00   M   12      2       3 8791   85.00   M    9      2       3 8792   81.00   M   12      2       3 8793   70.00   M   16      2       3 8794   51.00   M    4      2       2 8795   54.00   M   10      2       2 8796   52.00   M    0      2       2 8797   46.00   M   18      2       2 8798   80.00   F   16      2       3 8799   58.00   M    8      2       2 8800   65.00   M    5      2       3 8801   87.00   F    8      2       3 8802   74.00   M    2      2       3 8803    2.00   F    3      2       1 8804   88.00   F    4      2       3 8805   56.00   M    6      2       2 8806   42.00   M   10      2       2 8807   68.00   M   13      2       3 8808   45.00   M    0      2       2 8809   62.00   M   14      2       3 8810   59.00   F    5      2       2 8811   65.00   M    5      2       3 8812   70.00   M   10      2       3 8813   68.00   F    0      2       3 8814   70.00   M   13      2       3 8815   57.00   F    3      2       2 8816   64.00   M    9      2       3 8817   79.00   F    0      2       3 8818   63.00   F    9      2       3 8819   70.00   F    1      2       3 8820   55.00   M   12      2       2 8821   90.00   M   11      2       3 8822   52.00   F   10      2       2 8823   27.00   M    7      2       2 8824   84.00   F    6      2       3 8825   64.00   F    2      2       3 8826   75.00   M    8      2       3 8827   70.00   M    9      2       3 8828   45.00   M    8      2       2 8829   72.00   M    4      2       3 8830   55.00   M    6      2       2 8831   40.00   M    0      2       2 8832   78.00   M   10      2       3 8833   65.00   F    2      2       3 8834   78.00   M    1      2       3 8835   57.00   F    8      2       2 8836   68.00   F    5      2       3 8837   60.00   M    1      2       3 8838   36.00   M    4      2       2 8839   80.00   M    1      2       3 8840   60.00   F    8      2       3 8841   55.00   F    8      2       2 8842   50.00   M    0      2       2 8843   59.00   M    3      2       2 8844   68.00   M    8      2       3 8845   63.00   M    1      2       3 8846   52.00   M    6      2       2 8847   50.00   F    5      2       2 8848   58.00   F    2      2       2 8849   71.00   F    7      2       3 8850   48.00   M    1      2       2 8851   52.00   M    6      2       2 8852   70.00   M    1      2       3 8853   58.00   F    0      2       2 8854   65.00   M    5      2       3 8855   40.00   M    7      2       2 8856   75.00   M    4      2       3 8857   86.00   M    6      2       3 8858   58.00   M    6      2       2 8859   65.00   F    2      2       3 8860   56.00   M    0      2       2 8861   55.00   M    5      2       2 8862   72.00   M    6      2       3 8863   60.00   F    6      2       3 8864   64.00   M    5      2       3 8865   54.00   M    2      2       2 8866   96.00   F    3      2       3 8867   58.00   M    5      2       2 8868   46.00   M    1      2       2 8869   58.00   M    0      2       2 8870   60.00   F    5      2       3 8871   83.00   F    0      2       3 8872   53.00   M    6      2       2 8873   67.00   M    4      2       3 8874   55.00   M    4      2       2 8875   85.00   M    1      2       3 8876   25.00   F    1      2       2 8877   69.00   M    2      2       3 8878   48.00   M    4      2       2 8879   80.00   M    1      2       3 8880   75.00   M    4      2       3 8881   80.00   F    2      2       3 8882   70.00   M    5      2       3 8883   86.00   M    4      2       3 8884   67.00   F    4      2       3 8885   85.00   F   10      2       3 8886   65.00   F    0      2       3 8887   63.00   M    3      2       3 8888   52.00   M    0      2       2 8889   94.00   F    2      2       3 8890   75.00   M    3      2       3 8891   46.00   M    0      2       2 8892   58.00   M    2      2       2 8893   61.00   M    3      2       3 8894   55.00   M    3      2       2 8895   55.00   F    2      2       2 8896   63.00   F    1      2       3 8897   59.00   F    2      2       2 8898   56.00   M    0      2       2 8899   74.00   M    1      2       3 8900   33.00   M    0      2       2 8901   40.00   F    0      2       2 8902   78.00   M   26      2       3 8903   50.00   M   25      2       2 8904   58.00   F    0      2       2 8905   72.00   M   17      2       3 8906   82.00   M    9      2       3 8907   72.00   M   12      2       3 8908   50.00   M    4      2       2 8909   75.00   M   20      2       3 8910   71.00   M   16      2       3 8911   67.00   F    7      2       3 8912   76.00   M   16      2       3 8913   41.00   M   16      2       2 8914   67.00   M   16      2       3 8915   69.00   F   14      2       3 8916   56.00   M   17      2       2 8917   55.00   M    2      2       2 8918   36.00   M    1      2       2 8919   71.00   F    8      2       3 8920   36.00   M   13      2       2 8921   76.00   F    9      2       3 8922   56.00   M    9      2       2 8923   87.00   F   14      2       3 8924   72.00   M   11      2       3 8925   55.00   M    5      2       2 8926   57.00   F   13      2       2 8927   75.00   M    9      2       3 8928   52.00   M   11      2       2 8929   76.00   M   11      2       3 8930   68.00   M    6      2       3 8931   70.00   M   10      2       3 8932   62.00   M   10      2       3 8933   66.00   M   10      2       3 8934   53.00   F    5      2       2 8935   78.00   M    6      2       3 8936   54.00   M    3      2       2 8937   65.00   M    7      2       3 8938   39.00   M    7      2       2 8939   64.00   M    5      2       3 8940   44.00   M    8      2       2 8941   65.00   M    8      2       3 8942   64.00   M    1      2       3 8943   67.00   M    1      2       3 8944   54.00   F    6      2       2 8945   77.00   F    0      2       3 8946   65.00   F    6      2       3 8947   49.00   M    1      2       2 8948   48.00   M    6      2       2 8949   45.00   M    7      2       2 8950   39.00   M    2      2       2 8951   50.00   M    8      2       2 8952   62.00   F    6      2       3 8953   56.00   M    3      2       2 8954   59.00   M    5      2       2 8955   58.00   M    6      2       2 8956   83.00   M   10      2       3 8957   65.00   M    2      2       3 8958   72.00   M    1      2       3 8959   87.00   F    3      2       3 8960   70.00   M    8      2       3 8961   72.00   M    3      2       3 8962   70.00   M    3      2       3 8963   51.00   M    7      2       2 8964   72.00   M    3      2       3 8965   65.00   F    5      2       3 8966   37.00   M    6      2       2 8967   70.00   M    0      2       3 8968   35.00   M    5      2       2 8969   57.00   M    6      2       2 8970   82.00   F    6      2       3 8971   66.00   F    1      2       3 8972   24.00   M    2      2       2 8973   70.00   M    3      2       3 8974   63.00   M    3      2       3 8975   64.00   M    6      2       3 8976   68.00   M    4      2       3 8977   70.00   F    9      2       3 8978   78.00   M    0      2       3 8979   60.00   M    0      2       3 8980   40.00   M    0      2       2 8981   65.00   M    5      2       3 8982   74.00   M    3      2       3 8983   47.00   F    4      2       2 8984   75.00   M    6      2       3 8985   47.00   F    3      2       2 8986   45.00   M    4      2       2 8987   72.00   M    4      2       3 8988   58.00   M    4      2       2 8989   81.00   F    2      2       3 8990   20.00   M    2      2       2 8991   78.00   M    3      2       3 8992   73.00   M    2      2       3 8993   80.00   M    0      2       3 8994   54.00   M    2      2       2 8995   61.00   M    5      2       3 8996   55.00   M    3      2       2 8997   46.00   M    3      2       2 8998   47.00   M    1      2       2 8999   45.00   F    2      2       2 9000   73.00   M    7      2       3 9001   89.00   F    2      2       3 9002   53.00   M    7      2       2 9003   57.00   F    2      2       2 9004   65.00   M    0      2       3 9005   42.00   M    0      2       2 9006   55.00   M    1      2       2 9007   34.00   M    2      2       2 9008   50.00   M    2      2       2 9009   61.00   F    4      2       3 9010   77.00   F    4      2       3 9011   85.00   M    0      2       3 9012   80.00   F    0      2       3 9013   32.00   F    2      2       2 9014   75.00   F    1      2       3 9015   62.00   M    1      2       3 9016   37.00   F    2      2       2 9017   35.00   F    0      2       2 9018   33.00   M    1      2       2 9019   67.00   M    1      2       3 9020   57.00   M    0      2       2 9021   82.00   M    1      2       3 9022   70.00   M    0      2       3 9023   36.00   M    7      2       2 9024   42.00   F    3      2       2 9025   55.00   F    3      2       2 9026   82.00   F    1      2       3 9027   58.00   M    1      2       2 9028   38.00   F   23      2       2 9029   78.00   M   16      2       3 9030   75.00   M   18      2       3 9031   79.00   F   17      2       3 9032   66.00   F   20      2       3 9033   60.00   M    3      2       3 9034   65.00   M    8      2       3 9035   50.00   M    5      2       2 9036   62.00   M    5      2       3 9037   65.00   M    0      2       3 9038   61.00   F   12      2       3 9039   82.00   M    5      2       3 9040   72.00   M    7      2       3 9041   62.00   M    6      2       3 9042   81.00   M    8      2       3 9043   68.00   M    8      2       3 9044   57.00   M    3      2       2 9045   89.00   F   13      2       3 9046   75.00   M   20      2       3 9047   72.00   M   11      2       3 9048   50.00   F   10      2       2 9049   60.00   M    4      2       3 9050   40.00   M    1      2       2 9051   62.00   F    0      2       3 9052   64.00   F    9      2       3 9053   73.00   M   10      2       3 9054   83.00   M    2      2       3 9055   85.00   M    8      2       3 9056   70.00   M   10      2       3 9057   76.00   F    8      2       3 9058   68.00   M    8      2       3 9059   50.00   F    4      2       2 9060   72.00   M    2      2       3 9061   55.00   M   11      2       2 9062   75.00   M   10      2       3 9063   75.00   M    4      2       3 9064   55.00   M    0      2       2 9065   43.00   M    2      2       2 9066   66.00   F    7      2       3 9067   91.00   M    5      2       3 9068   40.00   M    3      2       2 9069   55.00   M    4      2       2 9070   44.00   M    8      2       2 9071   55.00   F    7      2       2 9072   65.00   M    4      2       3 9073   54.00   M    0      2       2 9074   61.00   M    2      2       3 9075   85.00   M    5      2       3 9076   66.00   M    5      2       3 9077   61.00   M    7      2       3 9078   78.00   M    3      2       3 9079   57.00   M    1      2       2 9080   77.00   F    5      2       3 9081   57.00   M    5      2       2 9082   76.00   F    5      2       3 9083   62.00   M    3      2       3 9084   66.00   M    5      2       3 9085   63.00   M    4      2       3 9086   55.00   M    3      2       2 9087   61.00   F    4      2       3 9088   56.00   M    4      2       2 9089   80.00   F    4      2       3 9090   65.00   M    3      2       3 9091   46.00   M    0      2       2 9092   62.00   F    7      2       3 9093   74.00   M    5      2       3 9094   60.00   F    5      2       3 9095   52.00   M    8      2       2 9096   65.00   M    3      2       3 9097   82.00   M    0      2       3 9098   55.00   F    1      2       2 9099   74.00   M    1      2       3 9100   36.00   F    1      2       2 9101   67.00   M    3      2       3 9102   87.00   M    2      2       3 9103   89.00   M    2      2       3 9104   65.00   F    4      2       3 9105   38.00   M    4      2       2 9106   52.00   F    1      2       2 9107   74.00   M    1      2       3 9108   45.00   M    2      2       2 9109   65.00   M    5      2       3 9110   85.00   M    3      2       3 9111   50.00   M    0      2       2 9112   38.00   M    1      2       2 9113   50.00   M    3      2       2 9114   70.00   M    5      2       3 9115   39.00   M    5      2       2 9116   60.00   M    0      2       3 9117   38.00   M    5      2       2 9118   70.00   M    0      2       3 9119   70.00   M    3      2       3 9120   64.00   M    4      2       3 9121   80.00   F    1      2       3 9122   75.00   M    2      2       3 9123   73.00   M    0      2       3 9124   67.00   M   42      2       3 9125   65.00   M    0      2       3 9126   80.00   M    0      2       3 9127   62.00   M   49      2       3 9128   65.00   M    7      2       3 9129   70.00   M   16      2       3 9130   73.00   M   13      2       3 9131   23.00   M    2      2       2 9132   42.00   F   10      2       2 9133   75.00   F   18      2       3 9134   17.00   M    0      2       1 9135   55.00   M    8      2       2 9136   49.00   M   18      2       2 9137   49.00   M    0      2       2 9138   60.00   M    3      2       3 9139   73.00   F    4      2       3 9140   62.00   M   10      2       3 9141   87.00   F    0      2       3 9142   33.00   M    5      2       2 9143   60.00   F    5      2       3 9144   57.00   M    0      2       2 9145   47.00   M   11      2       2 9146   58.00   M    7      2       2 9147   73.00   F    2      2       3 9148   65.00   F   12      2       3 9149   78.00   M    5      2       3 9150   59.00   F   12      2       2 9151   75.00   M   11      2       3 9152   62.00   M    8      2       3 9153   38.00   F    0      2       2 9154   93.00   M   11      2       3 9155   70.00   F    7      2       3 9156   24.00   M   11      2       2 9157   50.00   M    6      2       2 9158   45.00   M    7      2       2 9159   86.00   M    8      2       3 9160   42.00   M    7      2       2 9161   60.00   M    5      2       3 9162   36.00   M    6      2       2 9163   50.00   F    9      2       2 9164   61.00   F    5      2       3 9165   65.00   M    6      2       3 9166   59.00   M    5      2       2 9167   62.00   M    5      2       3 9168   86.00   F    5      2       3 9169   75.00   M    8      2       3 9170   70.00   F    1      2       3 9171   70.00   M    5      2       3 9172   60.00   M    3      2       3 9173   75.00   F    3      2       3 9174   53.00   M    3      2       2 9175   83.00   M    4      2       3 9176   65.00   M    2      2       3 9177   51.00   M    0      2       2 9178   77.00   F    5      2       3 9179   78.00   M    1      2       3 9180   48.00   M    2      2       2 9181   78.00   M    3      2       3 9182   62.00   M   13      2       3 9183   85.00   M    2      2       3 9184   75.00   M    2      2       3 9185   52.00   F    0      2       2 9186   75.00   F    4      2       3 9187   70.00   M    0      2       3 9188   83.00   M    0      2       3 9189   70.00   M    1      2       3 9190   55.00   M    2      2       2 9191   53.00   M    1      2       2 9192   75.00   M    0      2       3 9193   60.00   F    0      2       3 9194   41.00   M   21      2       2 9195   96.00   F   20      2       3 9196   54.00   F    4      2       2 9197   53.00   M   15      2       2 9198   43.00   M    7      2       2 9199   66.00   M   11      2       3 9200   45.00   F   13      2       2 9201   55.00   F   15      2       2 9202   47.00   M   11      2       2 9203   65.00   M    1      2       3 9204   77.00   M   12      2       3 9205   84.00   F   13      2       3 9206   53.00   M   13      2       2 9207   45.00   M    9      2       2 9208   76.00   M    6      2       3 9209   80.00   F    3      2       3 9210   48.00   F    8      2       2 9211   35.00   M   10      2       2 9212   71.00   M   11      2       3 9213   56.00   M   10      2       2 9214   58.00   M   10      2       2 9215   50.00   M    3      2       2 9216   55.00   F    8      2       2 9217   70.00   F    4      2       3 9218   80.00   M    5      2       3 9219   45.00   M    7      2       2 9220   63.00   M    6      2       3 9221   64.00   M    9      2       3 9222   32.00   M    9      2       2 9223   43.00   M    7      2       2 9224   65.00   M    4      2       3 9225   55.00   F    6      2       2 9226   58.00   F    9      2       2 9227   36.00   M    6      2       2 9228   69.00   F    1      2       3 9229   45.00   M    4      2       2 9230   48.00   F    0      2       2 9231   60.00   M    8      2       3 9232   24.00   M    4      2       2 9233   55.00   M    5      2       2 9234   70.00   M    8      2       3 9235   93.00   M    4      2       3 9236   60.00   M    5      2       3 9237   85.00   M    8      2       3 9238   65.00   F    6      2       3 9239   54.00   M    6      2       2 9240   66.00   M    3      2       3 9241   70.00   M    4      2       3 9242   37.00   M    1      2       2 9243   35.00   M    6      2       2 9244   70.00   F    2      2       3 9245   35.00   M    6      2       2 9246   60.00   M    5      2       3 9247   86.00   F    0      2       3 9248   89.00   M    2      2       3 9249   55.00   F    2      2       2 9250   55.00   M    5      2       2 9251   48.00   M    3      2       2 9252   57.00   F    0      2       2 9253   62.00   M    3      2       3 9254   75.00   F    3      2       3 9255   83.00   M    2      2       3 9256   70.00   M    5      2       3 9257   73.00   M    0      2       3 9258   40.00   M   19      2       2 9259   72.00   F    4      2       3 9260   76.00   M    0      2       3 9261   75.00   M    0      2       3 9262   43.00   M    1      2       2 9263   32.00   M    1      2       2 9264   76.00   F    2      2       3 9265   95.00   M    0      2       3 9266   45.00   F    1      2       2 9267   62.00   F    0      2       3 9268   89.00   M    1      2       3 9269   74.00   M    0      2       3 9270   60.00   F   13      2       3 9271   55.00   M    7      2       2 9272   56.00   F    1      2       2 9273   63.00   M    0      2       3 9274   48.00   M    1      2       2 9275   68.00   F    0      2       3 9276   75.00   M    0      2       3 9277   81.00   M    0      2       3 9278   65.00   M    2      2       3 9279   54.00   F    0      2       2 9280   76.00   M    1      2       3 9281   70.00   M    0      2       3 9282   75.00   M    6      2       3 9283   35.00   M   16      2       2 9284   72.00   M    9      2       3 9285   62.00   F    7      2       3 9286   46.00   M    0      2       2 9287   48.00   M   14      2       2 9288   65.00   M   14      2       3 9289   81.00   M    8      2       3 9290   43.00   M   14      2       2 9291   63.00   F    2      2       3 9292   62.00   F    1      2       3 9293   77.00   F   10      2       3 9294   75.00   M   12      2       3 9295   70.00   M    3      2       3 9296   45.00   M    3      2       2 9297   73.00   M    3      2       3 9298   58.00   M    4      2       2 9299   80.00   M    7      2       3 9300   64.00   F    6      2       3 9301   72.00   M   12      2       3 9302   88.00   M    2      2       3 9303   58.00   M    4      2       2 9304   39.00   F   11      2       2 9305   84.00   M    4      2       3 9306   56.00   M    5      2       2 9307   36.00   M    9      2       2 9308   68.00   F    2      2       3 9309   63.00   F   10      2       3 9310   47.00   M    5      2       2 9311   73.00   M    5      2       3 9312   70.00   F    8      2       3 9313   72.00   M    8      2       3 9314   80.00   F    6      2       3 9315   72.00   M    8      2       3 9316   68.00   F    8      2       3 9317   85.00   F    8      2       3 9318   51.00   M    3      2       2 9319   58.00   M    9      2       2 9320   70.00   M   10      2       3 9321   70.00   M    7      2       3 9322   53.00   M    4      2       2 9323   74.00   M    3      2       3 9324   98.00   M   14      2       3 9325   74.00   M    6      2       3 9326   55.00   F   10      2       2 9327   45.00   M   14      2       2 9328   72.00   M    9      2       3 9329   64.00   F   11      2       3 9330   72.00   M    4      2       3 9331   50.00   F    3      2       2 9332   40.00   M   26      2       2 9333   84.00   M   16      2       3 9334   55.00   F   17      2       2 9335   66.00   M   12      2       3 9336   52.00   M   19      2       2 9337   54.00   F   23      2       2 9338   70.00   M    3      2       3 9339   78.00   M   24      2       3 9340   55.00   M   43      2       2 9341   85.00   M    1      2       3 9342   62.00   F   51      2       3 9343   50.00   M    3      2       2 9344   72.00   M   20      2       3 9345   80.00   M    4      2       3 9346   78.00   M    1      2       3 9347   56.00   F    1      2       2 9348   40.00   M    0      2       2 9349   50.00   F    4      2       2 9350   47.00   M    2      2       2 9351   65.00   M    1      2       3 9352   63.00   M    7      2       3 9353   73.00   M    4      2       3 9354   67.00   M    4      2       3 9355   65.00   F    3      2       3 9356   70.00   M    4      2       3 9357   62.00   M    2      2       3 9358   50.00   M    3      2       2 9359   72.00   M    4      2       3 9360   72.00   M    0      2       3 9361   58.00   M    1      2       2 9362   66.00   M    6      2       3 9363   29.00   M    3      2       2 9364   77.00   M    1      2       3 9365   48.00   F    0      2       2 9366   77.00   M    1      2       3 9367   55.00   F   31      2       2 9368   56.00   M    1      2       2 9369   64.00   M    0      2       3 9370   26.00   M    0      2       2 9371   58.00   M    0      2       2 9372   56.00   M    1      2       2 9373   51.00   F    5      2       2 9374   75.00   M    0      2       3 9375   48.00   M    5      2       2 9376   82.00   M    4      2       3 9377   64.00   M    0      2       3 9378   60.00   F    4      2       3 9379   53.00   F    7      2       2 9380   76.00   M   13      2       3 9381   28.00   M   10      2       2 9382   70.00   M   20      2       3 9383   68.00   M   20      2       3 9384   70.00   F    1      2       3 9385   72.00   M    5      2       3 9386   65.00   M    4      2       3 9387   60.00   F    4      2       3 9388   88.00   M    4      2       3 9389   56.00   M    3      2       2 9390   55.00   F   13      2       2 9391   73.00   M    3      2       3 9392   66.00   M   10      2       3 9393   72.00   M   11      2       3 9394   64.00   M    4      2       3 9395   52.00   M    0      2       2 9396   46.00   M    6      2       2 9397   72.00   M    0      2       3 9398   65.00   F   11      2       3 9399   68.00   M   11      2       3 9400   44.00   F    3      2       2 9401   73.00   M    3      2       3 9402   75.00   M    2      2       3 9403   66.00   M    7      2       3 9404   64.00   F   13      2       3 9405   72.00   M   11      2       3 9406   68.00   F    5      2       3 9407   87.00   M    2      2       3 9408   71.00   M    8      2       3 9409   63.00   M   11      2       3 9410   56.00   M   12      2       2 9411   55.00   F   13      2       2 9412   42.00   M   13      2       2 9413   72.00   M    9      2       3 9414   69.00   M    7      2       3 9415   71.00   F    1      2       3 9416   58.00   F    0      2       2 9417   57.00   M    9      2       2 9418   75.00   M    8      2       3 9419   85.00   M    7      2       3 9420   71.00   M    7      2       3 9421   62.00   M    9      2       3 9422   68.00   M    4      2       3 9423   67.00   M   15      2       3 9424   69.00   M    5      2       3 9425   75.00   M    8      2       3 9426   79.00   M    5      2       3 9427   57.00   M    7      2       2 9428   40.00   M    5      2       2 9429   86.00   M    0      2       3 9430   75.00   M    6      2       3 9431   64.00   M    6      2       3 9432   76.00   F    1      2       3 9433   82.00   M    6      2       3 9434   86.00   M    6      2       3 9435   59.00   M   10      2       2 9436   50.00   M    4      2       2 9437   40.00   M    7      2       2 9438   47.00   M    0      2       2 9439   89.00   M    5      2       3 9440   58.00   M    4      2       2 9441   52.00   M    7      2       2 9442   67.00   M    4      2       3 9443   48.00   M    5      2       2 9444   60.00   F    0      2       3 9445   58.00   M    7      2       2 9446   48.00   F    4      2       2 9447   75.00   M    1      2       3 9448   73.00   M    3      2       3 9449   57.00   F    4      2       2 9450   83.00   M    3      2       3 9451   42.00   M    2      2       2 9452   70.00   M    4      2       3 9453   80.00   M    3      2       3 9454   62.00   M    1      2       3 9455   85.00   M    4      2       3 9456   58.00   M    3      2       2 9457   70.00   F    3      2       3 9458   72.00   F    4      2       3 9459   51.00   M    3      2       2 9460   68.00   M    3      2       3 9461   30.00   F    1      2       2 9462   70.00   M    1      2       3 9463   60.00   M    3      2       3 9464   70.00   M    5      2       3 9465   72.00   M    1      2       3 9466   68.00   F    9      2       3 9467   51.00   F    0      2       2 9468   80.00   M    2      2       3 9469   60.00   M    0      2       3 9470   38.00   M    0      2       2 9471   84.00   F    4      2       3 9472   76.00   M    4      2       3 9473   50.00   F    3      2       2 9474   50.00   M    0      2       2 9475   30.00   M    1      2       2 9476   58.00   M    2      2       2 9477   65.00   F    1      2       3 9478   65.00   F    4      2       3 9479   40.00   M    1      2       2 9480   55.00   M    1      2       2 9481   72.00   M    8      2       3 9482   52.00   F    0      2       2 9483   42.00   M    0      2       2 9484   63.00   F    0      2       3 9485   58.00   M   23      2       2 9486   45.00   M    0      2       2 9487   40.00   F    4      2       2 9488   39.00   M    1      2       2 9489   57.00   M   12      2       2 9490   70.00   F    4      2       3 9491   68.00   M    3      2       3 9492   72.00   F    4      2       3 9493   71.00   M   14      2       3 9494   75.00   M    0      2       3 9495   64.00   M   15      2       3 9496   80.00   M    2      2       3 9497   65.00   M    0      2       3 9498   59.00   M    0      2       2 9499   47.00   M    0      2       2 9500   75.00   M    0      2       3 9501   60.00   M    0      2       3 9502   43.00   M    0      2       2 9503   71.00   M    1      2       3 9504   62.00   M    6      2       3 9505   70.00   M    1      2       3 9506   60.00   M   20      2       3 9507   65.00   M    2      2       3 9508   87.00   F    6      2       3 9509   49.00   M    4      2       2 9510   65.00   F   29      2       3 9511   34.00   M   12      2       2 9512   84.00   M    0      2       3 9513   59.00   M    9      2       2 9514   76.00   M   12      2       3 9515   65.00   M    9      2       3 9516   44.00   M   12      2       2 9517   52.00   M    6      2       2 9518   68.00   F   12      2       3 9519   72.00   M    0      2       3 9520   74.00   M   12      2       3 9521   68.00   M   12      2       3 9522   64.00   M    7      2       3 9523   60.00   M    6      2       3 9524   73.00   M   11      2       3 9525   71.00   M    9      2       3 9526   65.00   M   11      2       3 9527   73.00   M   10      2       3 9528   61.00   M    5      2       3 9529   76.00   M   12      2       3 9530   75.00   M    7      2       3 9531   52.00   M    8      2       2 9532   58.00   M    9      2       2 9533   75.00   M    3      2       3 9534   54.00   F    8      2       2 9535   68.00   F    8      2       3 9536   82.00   M    8      2       3 9537   46.00   M    3      2       2 9538   75.00   F    0      2       3 9539   50.00   M   11      2       2 9540   60.00   M   11      2       3 9541   62.00   M    0      2       3 9542   63.00   F    9      2       3 9543   71.00   F    8      2       3 9544   42.00   M    4      2       2 9545   84.00   F    3      2       3 9546   70.00   F    7      2       3 9547   73.00   M    0      2       3 9548   68.00   F    6      2       3 9549   52.00   M    1      2       2 9550   45.00   M    6      2       2 9551   59.00   M    7      2       2 9552   90.00   M    4      2       3 9553   69.00   F    4      2       3 9554   54.00   M    3      2       2 9555   77.00   M    9      2       3 9556   61.00   M    8      2       3 9557   70.00   F    5      2       3 9558   46.00   M    6      2       2 9559   82.00   M    6      2       3 9560   65.00   F    2      2       3 9561   55.00   M    4      2       2 9562   80.00   M    2      2       3 9563   40.00   M    3      2       2 9564   66.00   M    0      2       3 9565   54.00   M    0      2       2 9566   78.00   M   34      2       3 9567   66.00   M    0      2       3 9568   62.00   M    2      2       3 9569   40.00   F    3      2       2 9570   62.00   M    3      2       3 9571   54.00   M    1      2       2 9572   55.00   M    0      2       2 9573   60.00   F    3      2       3 9574   45.00   F    0      2       2 9575   60.00   F    1      2       3 9576   45.00   M    1      2       2 9577   65.00   F    4      2       3 9578   60.00   F    1      2       3 9579   47.00   M    1      2       2 9580   70.00   M    0      2       3 9581   55.00   F    0      2       2 9582   74.00   M    0      2       3 9583   50.00   M   17      2       2 9584   60.00   F    0      2       3 9585   70.00   M   13      2       3 9586   86.00   M   23      2       3 9587   60.00   M    1      2       3 9588   87.00   M    8      2       3 9589   70.00   M    9      2       3 9590   56.00   M    1      2       2 9591   65.00   M    8      2       3 9592   80.00   M    6      2       3 9593   75.00   F    5      2       3 9594   80.00   M    0      2       3 9595   60.00   M    7      2       3 9596   58.00   F    0      2       2 9597   40.00   M    0      2       2 9598   90.00   M    1      2       3 9599   72.00   M   10      2       3 9600   71.00   M   15      2       3 9601   55.00   M   11      2       2 9602   57.00   M    3      2       2 9603   70.00   M    3      2       3 9604   61.00   M    3      2       3 9605   60.00   M   12      2       3 9606   67.00   M    7      2       3 9607   58.00   F   11      2       2 9608   57.00   M    2      2       2 9609   67.00   M    0      2       3 9610   75.00   M   12      2       3 9611   35.00   F   10      2       2 9612   74.00   F    3      2       3 9613   65.00   M    8      2       3 9614   63.00   M   11      2       3 9615   80.00   F    1      2       3 9616   78.00   M    3      2       3 9617   57.00   M    6      2       2 9618   41.00   M   11      2       2 9619   48.00   M   11      2       2 9620   94.00   M    9      2       3 9621   29.00   F    6      2       2 9622   55.00   M   12      2       2 9623   86.00   M   10      2       3 9624   70.00   F   12      2       3 9625   24.00   M   10      2       2 9626   72.00   M   10      2       3 9627   64.00   M   12      2       3 9628   50.00   F    7      2       2 9629   82.00   F   10      2       3 9630   74.00   M    7      2       3 9631   55.00   M    0      2       2 9632   53.00   M    8      2       2 9633   60.00   F    2      2       3 9634   62.00   M    8      2       3 9635   68.00   M    9      2       3 9636   80.00   M    8      2       3 9637   75.00   M   10      2       3 9638   55.00   M    4      2       2 9639   53.00   M   11      2       2 9640   50.00   M    1      2       2 9641   52.00   M    6      2       2 9642   49.00   M    8      2       2 9643   70.00   M    2      2       3 9644   80.00   F    7      2       3 9645   68.00   M    8      2       3 9646   57.00   M    3      2       2 9647   60.00   M    9      2       3 9648   72.00   M    2      2       3 9649   76.00   M    9      2       3 9650   63.00   M   11      2       3 9651   60.00   M    5      2       3 9652   62.00   M    7      2       3 9653   47.00   M    4      2       2 9654   57.00   F    7      2       2 9655   86.00   M    0      2       3 9656   35.00   F    4      2       2 9657   67.00   F    4      2       3 9658   44.00   M    2      2       2 9659   44.00   M    4      2       2 9660   45.00   F    5      2       2 9661   73.00   M    3      2       3 9662   60.00   F    4      2       3 9663   68.00   M    3      2       3 9664   71.00   M    7      2       3 9665   80.00   F    4      2       3 9666   85.00   M    2      2       3 9667   56.00   F    2      2       2 9668   66.00   M    3      2       3 9669   69.00   M    4      2       3 9670   40.00   F    1      2       2 9671   76.00   F    3      2       3 9672   77.00   M    2      2       3 9673   30.00   M    1      2       2 9674   57.00   M    3      2       2 9675   65.00   M    3      2       3 9676   54.00   M    1      2       2 9677   70.00   M    1      2       3 9678   71.00   M    0      2       3 9679   73.00   M    3      2       3 9680   61.00   M    0      2       3 9681   65.00   F    1      2       3 9682   80.00   M    1      2       3 9683   67.00   M    0      2       3 9684   62.00   M    2      2       3 9685   34.00   M    1      2       2 9686   58.00   M    0      2       2 9687   60.00   F    1      2       3 9688   66.00   M    1      2       3 9689   71.00   M    1      2       3 9690   62.00   M    0      2       3 9691   64.00   M    1      2       3 9692   70.00   M    1      2       3 9693   58.00   M    1      2       2 9694   50.00   F    0      2       2 9695   63.00   M    0      2       3 9696   62.00   M    0      2       3 9697   74.00   M    0      2       3 9698   27.00   M    1      2       2 9699   43.00   M   20      2       2 9700   43.00   F   22      2       2 9701   65.00   F   19      2       3 9702   53.00   M   19      2       2 9703   50.00   F    9      2       2 9704   64.00   M    4      2       3 9705   50.00   M    7      2       2 9706   64.00   M    6      2       3 9707   85.00   M    0      2       3 9708   58.00   M    2      2       2 9709   67.00   M    3      2       3 9710   71.00   F    1      2       3 9711   74.00   M    5      2       3 9712   75.00   M    2      2       3 9713   24.00   F    3      2       2 9714   55.00   F   16      2       2 9715   76.00   F   11      2       3 9716   58.00   F    0      2       2 9717   74.00   M    3      2       3 9718   60.00   M    1      2       3 9719   67.00   M   12      2       3 9720   65.00   M   13      2       3 9721   80.00   M    6      2       3 9722   70.00   M    8      2       3 9723   65.00   M    2      2       3 9724   47.00   M   11      2       2 9725   45.00   F    1      2       2 9726   65.00   M   11      2       3 9727   50.00   F    2      2       2 9728   48.00   F    0      2       2 9729   70.00   F   10      2       3 9730   82.00   M    3      2       3 9731   75.00   F   12      2       3 9732   64.00   M   11      2       3 9733   55.00   M   12      2       2 9734   71.00   M    9      2       3 9735   55.00   M    4      2       2 9736   65.00   M   25      2       3 9737   57.00   M   11      2       2 9738   80.00   M   10      2       3 9739   40.00   M    1      2       2 9740   56.00   M    4      2       2 9741   56.00   M    1      2       2 9742   77.00   M   16      2       3 9743   70.00   F    2      2       3 9744   35.00   M    8      2       2 9745   64.00   M    8      2       3 9746   90.00   M    0      2       3 9747   52.00   M   10      2       2 9748   93.00   M    4      2       3 9749   55.00   M    5      2       2 9750   51.00   M    1      2       2 9751   57.00   M    7      2       2 9752   52.00   M    9      2       2 9753   75.00   F    6      2       3 9754   64.00   M    1      2       3 9755   66.00   M    2      2       3 9756   84.00   F    8      2       3 9757   71.00   F    7      2       3 9758   70.00   F    5      2       3 9759   55.00   F    4      2       2 9760   48.00   F    3      2       2 9761   46.00   F    5      2       2 9762   50.00   F    6      2       2 9763   56.00   M    6      2       2 9764   60.00   M    5      2       3 9765   86.00   F    4      2       3 9766   83.00   M    6      2       3 9767   82.00   F    4      2       3 9768   70.00   M    0      2       3 9769   85.00   M    3      2       3 9770   84.00   M    2      2       3 9771   75.00   M    4      2       3 9772   58.00   M    2      2       2 9773   51.00   M    2      2       2 9774   58.00   F    1      2       2 9775   60.00   M    2      2       3 9776   62.00   F    2      2       3 9777   73.00   M    3      2       3 9778   84.00   M    3      2       3 9779   65.00   F    3      2       3 9780   60.00   M    2      2       3 9781   84.00   M    3      2       3 9782   42.00   M    2      2       2 9783   70.00   M    6      2       3 9784   61.00   M    1      2       3 9785   65.00   F    1      2       3 9786   78.00   F    4      2       3 9787   36.00   M    9      2       2 9788   64.00   M    1      2       3 9789   48.00   M    1      2       2 9790   34.00   M    1      2       2 9791   55.00   F    1      2       2 9792   65.00   F    4      2       3 9793   46.00   M    0      2       2 9794   86.00   F    1      2       3 9795   75.00   F    2      2       3 9796   80.00   M    1      2       3 9797   25.00   F   30      2       2 9798   85.00   M    3      2       3 9799   60.00   M   20      2       3 9800   76.00   F    6      2       3 9801   70.00   M    4      2       3 9802   59.00   M   18      2       2 9803   72.00   M   15      2       3 9804   60.00   F    4      2       3 9805   85.00   M   13      2       3 9806   55.00   M   10      2       2 9807   72.00   M   12      2       3 9808   53.00   M   15      2       2 9809   70.00   M   13      2       3 9810   53.00   M    3      2       2 9811   60.00   M    7      2       3 9812   53.00   F   11      2       2 9813   76.00   M   13      2       3 9814   97.00   F    4      2       3 9815   60.00   F    0      2       3 9816   68.00   F   10      2       3 9817   70.00   F    5      2       3 9818   59.00   M    1      2       2 9819   60.00   M    0      2       3 9820   73.00   M    6      2       3 9821   71.00   F    9      2       3 9822   75.00   M    7      2       3 9823   83.00   M    9      2       3 9824   75.00   M    9      2       3 9825   52.00   F    4      2       2 9826   63.00   F   23      2       3 9827   63.00   M   26      2       3 9828   95.00   M    9      2       3 9829   70.00   F    8      2       3 9830   73.00   M    8      2       3 9831   52.00   M    8      2       2 9832   50.00   M    1      2       2 9833   55.00   F    0      2       2 9834   84.00   M    6      2       3 9835   80.00   F    9      2       3 9836   92.00   M    4      2       3 9837   74.00   M    7      2       3 9838   36.00   M   26      2       2 9839   60.00   M    6      2       3 9840   50.00   M    3      2       2 9841   74.00   F    4      2       3 9842   65.00   F    1      2       3 9843   78.00   M    2      2       3 9844   60.00   M    6      2       3 9845   65.00   M    3      2       3 9846   60.00   M    6      2       3 9847   68.00   M    5      2       3 9848   70.00   M    4      2       3 9849   40.00   M    4      2       2 9850   70.00   F    0      2       3 9851   75.00   M    1      2       3 9852   65.00   F    9      2       3 9853   55.00   M    7      2       2 9854   62.00   F    3      2       3 9855   63.00   M    3      2       3 9856   39.00   F    4      2       2 9857   55.00   F    3      2       2 9858   70.00   M    1      2       3 9859   64.00   M    1      2       3 9860   72.00   F    0      2       3 9861   60.00   F    1      2       3 9862   65.00   F    1      2       3 9863   56.00   M    3      2       2 9864   40.00   F    2      2       2 9865   82.00   M   10      2       3 9866   84.00   M    3      2       3 9867   53.00   F    2      2       2 9868   59.00   M    1      2       2 9869   76.00   M    3      2       3 9870   40.00   M    0      2       2 9871   67.00   M   32      2       3 9872   56.00   M    1      2       2 9873   47.00   M    0      2       2 9874   61.00   M    5      2       3 9875   65.00   M    5      2       3 9876   70.00   M    1      2       3 9877   53.00   M   11      2       2 9878   46.00   M    0      2       2 9879   55.00   M    4      2       2 9880   56.00   M    4      2       2 9881   79.00   M   15      2       3 9882   51.00   F    5      2       2 9883   56.00   F    6      2       2 9884   66.00   M   12      2       3 9885   51.00   M    4      2       2 9886   67.00   F   10      2       3 9887   65.00   M    9      2       3 9888   85.00   M    3      2       3 9889   72.00   M    8      2       3 9890   50.00   M   11      2       2 9891   65.00   M    1      2       3 9892   82.00   M   12      2       3 9893   57.00   M   10      2       2 9894   53.00   F    1      2       2 9895   80.00   F   12      2       3 9896   71.00   M    8      2       3 9897   53.00   M    8      2       2 9898   70.00   F    6      2       3 9899   46.00   F    7      2       2 9900   74.00   M    7      2       3 9901   55.00   F    8      2       2 9902   64.00   M    9      2       3 9903   47.00   M   10      2       2 9904   65.00   M   10      2       3 9905   76.00   F    3      2       3 9906   87.00   M   39      2       3 9907   70.00   F    5      2       3 9908   61.00   F    2      2       3 9909   80.00   M    3      2       3 9910   65.00   M    2      2       3 9911   48.00   M    2      2       2 9912   84.00   M    6      2       3 9913   82.00   M    1      2       3 9914   69.00   F    6      2       3 9915   61.00   F   12      2       3 9916   60.00   M    2      2       3 9917   64.00   F    1      2       3 9918   80.00   F    3      2       3 9919   86.00   F    4      2       3 9920   75.00   F    4      2       3 9921   65.00   F    6      2       3 9922   70.00   M    3      2       3 9923   75.00   F    6      2       3 9924   72.00   M    3      2       3 9925   78.00   F   16      2       3 9926   58.00   M    0      2       2 9927   73.00   M    5      2       3 9928   70.00   M    2      2       3 9929   65.00   F   15      2       3 9930   71.00   F    3      2       3 9931   76.00   M    2      2       3 9932   80.00   M    0      2       3 9933   80.00   M    0      2       3 9934   32.00   M   10      2       2 9935   58.00   M    5      2       2 9936   55.00   F    1      2       2 9937   50.00   M    0      2       2 9938   60.00   M    2      2       3 9939   70.00   M    1      2       3 9940   65.00   M    0      2       3 9941   50.00   F    2      2       2 9942   54.00   M    0      2       2 9943   57.00   M    0      2       2 9944   48.00   F   31      2       2 9945   58.00   F    5      2       2 9946   44.00   M   28      2       2 9947   71.00   M   10      2       3 9948   75.00   M   25      2       3 9949   62.00   F    2      2       3 9950   59.00   M   37      2       2 9951   60.00   M    1      2       3 9952   60.00   M    4      2       3 9953   65.00   M    7      2       3 9954   69.00   M   17      2       3 9955   70.00   M    3      2       3 9956   60.00   F    6      2       3 9957   74.00   M    0      2       3 9958   52.00   M   13      2       2 9959   54.00   F    0      2       2 9960   65.00   M    8      2       3 9961   71.00   M   10      2       3 9962   72.00   F    7      2       3 9963   65.00   F    8      2       3 9964   51.00   M    9      2       2 9965   71.00   M    4      2       3 9966   45.00   F   15      2       2 9967   75.00   M    2      2       3 9968   67.00   M   10      2       3 9969   77.00   M    8      2       3 9970   74.00   F    8      2       3 9971   51.00   M   10      2       2 9972   73.00   M    3      2       3 9973   45.00   M    7      2       2 9974   61.00   M    5      2       3 9975   49.00   M    0      2       2 9976   58.00   M    2      2       2 9977   60.00   M    7      2       3 9978   47.00   F    7      2       2 9979   45.00   F    4      2       2 9980   53.00   M    7      2       2 9981   83.00   M    7      2       3 9982   59.00   M    6      2       2 9983   36.00   F    6      2       2 9984   31.00   F    4      2       2 9985   65.00   M    7      2       3 9986   81.00   M    3      2       3 9987   46.00   F    5      2       2 9988   80.00   F    7      2       3 9989   70.00   M    1      2       3 9990   66.00   M    8      2       3 9991   53.00   F    0      2       2 9992   65.00   M    0      2       3 9993   83.00   M   11      2       3 9994   60.00   F    2      2       3 9995   76.00   F    5      2       3 9996   65.00   F    2      2       3 9997   70.00   F    5      2       3 9998   68.00   M    3      2       3 9999   75.00   M    5      2       3 10000  67.00   M    7      2       3 10001  67.00   M    6      2       3 10002  70.00   M    1      2       3 10003  59.00   M    5      2       2 10004  81.00   M    2      2       3 10005  53.00   M    4      2       2 10006  65.00   F    2      2       3 10007  55.00   F    0      2       2 10008  52.00   M    4      2       2 10009  40.00   M    1      2       2 10010  82.00   M    3      2       3 10011  67.00   M    3      2       3 10012  58.00   M    2      2       2 10013  52.00   F    2      2       2 10014  51.00   M    2      2       2 10015  62.00   M    4      2       3 10016  70.00   M    2      2       3 10017  54.00   M    2      2       2 10018  59.00   F   53      2       2 10019  70.00   M    2      2       3 10020  56.00   M    2      2       2 10021  71.00   M    2      2       3 10022  74.00   F    2      2       3 10023  70.00   M    2      2       3 10024  60.00   F    1      2       3 10025  42.00   F    1      2       2 10026  65.00   M    1      2       3 10027  64.00   M    1      2       3 10028  60.00   M    0      2       3 10029  65.00   F    6      2       3 10030  24.00   M    4      2       2 10031  63.00   M   11      2       3 10032  54.00   M   10      2       2 10033  71.00   M    6      2       3 10034  70.00   M    9      2       3 10035  61.00   M   23      2       3 10036  65.00   M   16      2       3 10037  82.00   M   16      2       3 10038  65.00   M    7      2       3 10039  52.00   F   18      2       2 10040  66.00   M   17      2       3 10041  60.00   F    1      2       3 10042  59.00   M    7      2       2 10043  53.00   M   11      2       2 10044  69.00   M    8      2       3 10045  58.00   M   10      2       2 10046  81.00   M    9      2       3 10047  62.00   M   11      2       3 10048  85.00   F   11      2       3 10049  65.00   M    8      2       3 10050  77.00   M    9      2       3 10051  72.00   F    9      2       3 10052  85.00   M    8      2       3 10053  76.00   M    6      2       3 10054  67.00   M    8      2       3 10055  58.00   M    0      2       2 10056  53.00   M    3      2       2 10057  84.00   M    8      2       3 10058  60.00   F    7      2       3 10059  70.00   M    7      2       3 10060  75.00   M    9      2       3 10061  53.00   M    3      2       2 10062  60.00   M    8      2       3 10063  54.00   M    7      2       2 10064  77.00   M    2      2       3 10065  74.00   F    8      2       3 10066  59.00   M    7      2       2 10067  80.00   M    7      2       3 10068  54.00   M    9      2       2 10069  50.00   M    5      2       2 10070  50.00   M    7      2       2 10071  74.00   M    8      2       3 10072  75.00   F    5      2       3 10073  65.00   M    3      2       3 10074  80.00   M    3      2       3 10075  65.00   M    6      2       3 10076  77.00   M    5      2       3 10077  70.00   M    3      2       3 10078  74.00   M    5      2       3 10079  62.00   M    6      2       3 10080  65.00   M    1      2       3 10081  67.00   M    3      2       3 10082  38.00   F    4      2       2 10083  72.00   M    5      2       3 10084  61.00   F    6      2       3 10085  60.00   M    4      2       3 10086  75.00   M    4      2       3 10087  58.00   M    1      2       2 10088  45.00   M    1      2       2 10089  42.00   M    1      2       2 10090  63.00   M    2      2       3 10091  68.00   M    0      2       3 10092  32.00   F    3      2       2 10093  61.00   F    4      2       3 10094  67.00   F    1      2       3 10095  68.00   M    1      2       3 10096  55.00   F    2      2       2 10097  20.00   F    2      2       2 10098  72.00   F    0      2       3 10099  68.00   M    1      2       3 10100  43.00   M    0      2       2 10101  67.00   M    2      2       3 10102  32.00   M    1      2       2 10103  70.00   F    2      2       3 10104  65.00   F    2      2       3 10105  35.00   F    1      2       2 10106  76.00   M    1      2       3 10107  64.00   M   20      2       3 10108  55.00   F   17      2       2 10109  80.00   M   15      2       3 10110  65.00   M   11      2       3 10111  60.00   F    1      2       3 10112  54.00   M   17      2       2 10113  58.00   M    1      2       2 10114  51.00   M   13      2       2 10115  76.00   M   13      2       3 10116  58.00   M   12      2       2 10117  62.00   F   18      2       3 10118  65.00   M    6      2       3 10119  80.00   F    6      2       3 10120  68.00   M   11      2       3 10121  82.00   M    8      2       3 10122  63.00   M    8      2       3 10123  67.00   M    7      2       3 10124  44.00   M    9      2       2 10125  46.00   M    8      2       2 10126  79.00   M    9      2       3 10127  52.00   F    6      2       2 10128  60.00   F    3      2       3 10129  60.00   M    7      2       3 10130  39.00   M    8      2       2 10131  46.00   F    4      2       2 10132  65.00   M    2      2       3 10133  55.00   F    7      2       2 10134  77.00   M   10      2       3 10135  73.00   M    8      2       3 10136  48.00   M    6      2       2 10137  65.00   M   36      2       3 10138  79.00   F    7      2       3 10139  76.00   M    6      2       3 10140  81.00   M    6      2       3 10141  61.00   F    8      2       3 10142  68.00   M    3      2       3 10143  75.00   M    7      2       3 10144  80.00   M    6      2       3 10145  65.00   F    6      2       3 10146  30.00   M    8      2       2 10147  32.00   F    8      2       2 10148  82.00   M    9      2       3 10149  92.00   F    8      2       3 10150  60.00   M    3      2       3 10151  58.00   F    5      2       2 10152  59.00   M    5      2       2 10153  56.00   M    6      2       2 10154  61.00   M    6      2       3 10155  33.00   M   24      2       2 10156  57.00   F    1      2       2 10157  62.00   M    7      2       3 10158  70.00   F    0      2       3 10159  90.00   M   12      2       3 10160  32.00   F    3      2       2 10161  82.00   M   16      2       3 10162  85.00   M    5      2       3 10163  39.00   M    2      2       2 10164  77.00   F    4      2       3 10165  58.00   M    4      2       2 10166  70.00   M    2      2       3 10167  75.00   F    1      2       3 10168  88.00   F    3      2       3 10169  76.00   F    3      2       3 10170  80.00   M    6      2       3 10171  90.00   M   16      2       3 10172  66.00   M    6      2       3 10173  72.00   M    1      2       3 10174  48.00   F    0      2       2 10175  68.00   F   23      2       3 10176  18.00   F    2      2       2 10177  52.00   M    6      2       2 10178  62.00   F    3      2       3 10179  48.00   M    1      2       2 10180  49.00   M    1      2       2 10181  62.00   F    0      2       3 10182  45.00   F    1      2       2 10183  60.00   F    0      2       3 10184  52.00   F    3      2       2 10185  56.00   F    2      2       2 10186  45.00   F    2      2       2 10187  53.00   M    0      2       2 10188  70.00   M    1      2       3 10189  78.00   F    0      2       3 10190  75.00   M    0      2       3 10191  68.00   M    8      2       3 10192  41.00   F   22      2       2 10193  86.00   M   15      2       3 10194  76.00   M   23      2       3 10195  60.00   M   11      2       3 10196  72.00   M    9      2       3 10197  61.00   M   14      2       3 10198  68.00   M    0      2       3 10199  84.00   M   12      2       3 10200  45.00   M    2      2       2 10201  52.00   M    4      2       2 10202  87.00   F   15      2       3 10203  70.00   F   12      2       3 10204  64.00   M    2      2       3 10205  80.00   M   11      2       3 10206  53.00   F   13      2       2 10207  62.00   M   11      2       3 10208  63.00   M    5      2       3 10209  58.00   M   16      2       2 10210  52.00   M   12      2       2 10211  75.00   M    0      2       3 10212  92.00   M    5      2       3 10213  66.00   M    9      2       3 10214  58.00   M    5      2       2 10215  80.00   M   11      2       3 10216  70.00   M   10      2       3 10217  65.00   M   11      2       3 10218  60.00   M   22      2       3 10219  75.00   M   10      2       3 10220  74.00   M    6      2       3 10221  81.00   M    7      2       3 10222  72.00   F    6      2       3 10223  78.00   M    7      2       3 10224  40.00   F    8      2       2 10225  71.00   M    5      2       3 10226  70.00   M    7      2       3 10227  43.00   M    8      2       2 10228  47.00   M    6      2       2 10229  78.00   M    1      2       3 10230  57.00   M    7      2       2 10231  82.00   F    7      2       3 10232  76.00   M    1      2       3 10233  71.00   M    7      2       3 10234  60.00   M    4      2       3 10235  60.00   M    2      2       3 10236  55.00   F    2      2       2 10237  75.00   F    3      2       3 10238  70.00   M    2      2       3 10239  65.00   F    2      2       3 10240  57.00   M    4      2       2 10241  89.00   M    1      2       3 10242  75.00   M    0      2       3 10243  48.00   M   33      2       2 10244  65.00   M   26      2       3 10245  62.00   F    5      2       3 10246  79.00   F    4      2       3 10247  45.00   F    8      2       2 10248  37.00   F    1      2       2 10249  75.00   F    1      2       3 10250  45.00   F    0      2       2 10251  46.00   M    2      2       2 10252  68.00   M    5      2       3 10253  75.00   M    4      2       3 10254  75.00   M    2      2       3 10255  83.00   M    2      2       3 10256  60.00   F    4      2       3 10257  60.00   M    4      2       3 10258  63.00   F    2      2       3 10259  64.00   M    2      2       3 10260  72.00   M    2      2       3 10261  72.00   M    3      2       3 10262  78.00   M    0      2       3 10263  78.00   M    2      2       3 10264  68.00   M    2      2       3 10265  77.00   M    8      2       3 10266  70.00   M    2      2       3 10267  56.00   M   15      2       2 10268  63.00   M    3      2       3 10269  64.00   M    4      2       3 10270  64.00   F   13      2       3 10271  60.00   M   16      2       3 10272  62.00   M   15      2       3 10273  72.00   M   11      2       3 10274  61.00   M    1      2       3 10275  60.00   M   13      2       3 10276  38.00   M   11      2       2 10277  70.00   M    9      2       3 10278  84.00   M    9      2       3 10279  74.00   F   11      2       3 10280  39.00   M   12      2       2 10281  70.00   F    8      2       3 10282  81.00   M   11      2       3 10283  61.00   F   10      2       3 10284  70.00   M    6      2       3 10285  64.00   M   11      2       3 10286  40.00   F    2      2       2 10287  62.00   F   12      2       3 10288  75.00   M   10      2       3 10289  61.00   M    2      2       3 10290  62.00   M    5      2       3 10291  55.00   F    7      2       2 10292  65.00   M    3      2       3 10293  26.00   M    9      2       2 10294  72.00   M    4      2       3 10295  56.00   M    7      2       2 10296  76.00   M    6      2       3 10297  57.00   F    8      2       2 10298  78.00   F    9      2       3 10299  64.00   M    7      2       3 10300  52.00   M    5      2       2 10301  72.00   M    4      2       3 10302  70.00   F    7      2       3 10303  70.00   M    8      2       3 10304  74.00   M    8      2       3 10305  58.00   M    5      2       2 10306  55.00   M    6      2       2 10307  94.00   M    6      2       3 10308  86.00   M    7      2       3 10309  75.00   M    5      2       3 10310  62.00   F    6      2       3 10311  75.00   M    6      2       3 10312  65.00   M    1      2       3 10313  46.00   M    0      2       2 10314  49.00   M    8      2       2 10315  72.00   M    1      2       3 10316  45.00   F    1      2       2 10317  67.00   F    1      2       3 10318  54.00   M    4      2       2 10319  68.00   M    5      2       3 10320  52.00   F    3      2       2 10321  55.00   F    5      2       2 10322  71.00   M   32      2       3 10323  52.00   F    3      2       2 10324  48.00   M    2      2       2 10325  25.00   F    1      2       2 10326  80.00   M    2      2       3 10327  87.00   F    2      2       3 10328  74.00   M    5      2       3 10329  60.00   M    1      2       3 10330  65.00   F    3      2       3 10331  80.00   M    1      2       3 10332  68.00   M    1      2       3 10333  65.00   F    0      2       3 10334  38.00   M    1      2       2 10335  55.00   F    2      2       2 10336  24.00   F    0      2       2 10337  57.00   M    2      2       2 10338  70.00   F    4      2       3 10339  51.00   F   18      2       2 10340  50.00   M   17      2       2 10341  39.00   F   16      2       2 10342  54.00   M   15      2       2 10343  65.00   F    6      2       3 10344  69.00   M   14      2       3 10345  84.00   M    9      2       3 10346  70.00   M   11      2       3 10347  49.00   F    2      2       2 10348  69.00   M   10      2       3 10349  60.00   M    9      2       3 10350  75.00   M    8      2       3 10351  72.00   M    8      2       3 10352  59.00   F    7      2       2 10353  52.00   M    6      2       2 10354  73.00   M    7      2       3 10355  74.00   M    4      2       3 10356  60.00   M    1      2       3 10357  75.00   M   13      2       3 10358  37.00   M    6      2       2 10359  65.00   M   13      2       3 10360  67.00   F    5      2       3 10361  71.00   M    3      2       3 10362  70.00   F    4      2       3 10363  57.00   F    2      2       2 10364  70.00   M    4      2       3 10365  75.00   F    3      2       3 10366  44.00   M    4      2       2 10367  53.00   M    2      2       2 10368  43.00   F    4      2       2 10369  75.00   M    5      2       3 10370  60.00   M    4      2       3 10371  73.00   M    2      2       3 10372  79.00   M    2      2       3 10373  72.00   M    1      2       3 10374  68.00   M    3      2       3 10375  60.00   F    1      2       3 10376  61.00   M    2      2       3 10377  36.00   M    1      2       2 10378  86.00   M    1      2       3 10379  78.00   M    0      2       3 10380  64.00   M    2      2       3 10381  66.00   M    1      2       3 10382  52.00   F    1      2       2 10383  48.00   M   11      2       2 10384  50.00   M    7      2       2 10385  60.00   M    1      2       3 10386  56.00   F    1      2       2 10387  65.00   M    5      2       3 10388  67.00   M    4      2       3 10389  52.00   M    4      2       2 10390  75.00   F   11      2       3 10391  46.00   F   16      2       2 10392  71.00   F   14      2       3 10393  62.00   M   13      2       3 10394  75.00   M   12      2       3 10395  52.00   F   15      2       2 10396  70.00   M   12      2       3 10397  52.00   M    0      2       2 10398  82.00   M    6      2       3 10399  80.00   F   14      2       3 10400  75.00   F    6      2       3 10401  60.00   F   12      2       3 10402  57.00   M    4      2       2 10403  90.00   M    0      2       3 10404  47.00   M    9      2       2 10405  75.00   M    8      2       3 10406  86.00   M    7      2       3 10407  71.00   M    0      2       3 10408  65.00   M    9      2       3 10409  35.00   M    4      2       2 10410  68.00   M   11      2       3 10411  69.00   F    5      2       3 10412  60.00   F    3      2       3 10413  62.00   F    8      2       3 10414  68.00   F   25      2       3 10415  62.00   M    0      2       3 10416  85.00   M    2      2       3 10417  46.00   M   11      2       2 10418  65.00   M    3      2       3 10419  65.00   M    6      2       3 10420  64.00   F    6      2       3 10421  83.00   M    0      2       3 10422  73.00   M    8      2       3 10423  52.00   F    6      2       2 10424  55.00   F    3      2       2 10425  49.00   M    1      2       2 10426  80.00   M    2      2       3 10427  58.00   F    3      2       2 10428  63.00   M    2      2       3 10429  52.00   F    5      2       2 10430  73.00   M    2      2       3 10431  35.00   F    3      2       2 10432  58.00   M    0      2       2 10433  61.00   M    4      2       3 10434  53.00   F    3      2       2 10435  85.00   M    3      2       3 10436  59.00   M    3      2       2 10437  35.00   M    0      2       2 10438  60.00   F    1      2       3 10439  50.00   M    1      2       2 10440  75.00   M    1      2       3 10441  75.00   F    3      2       3 10442  84.00   F    2      2       3 10443  26.00   M    0      2       2 10444  80.00   M    8      2       3 10445  45.00   M    4      2       2 10446  79.00   M   11      2       3 10447  56.00   M   15      2       2 10448  75.00   M    0      2       3 10449  70.00   M    5      2       3 10450  52.00   M    8      2       2 10451  73.00   M    0      2       3 10452  60.00   M    4      2       3 10453  71.00   M   16      2       3 10454  28.00   M   13      2       2 10455  38.00   M   10      2       2 10456  85.00   M    7      2       3 10457  60.00   F   12      2       3 10458  65.00   F    8      2       3 10459  61.00   F    4      2       3 10460  62.00   M   11      2       3 10461  70.00   F    8      2       3 10462  70.00   M    5      2       3 10463  67.00   M    8      2       3 10464  46.00   F    9      2       2 10465  76.00   M    9      2       3 10466  54.00   M   10      2       2 10467  85.00   M    7      2       3 10468  65.00   F    8      2       3 10469  62.00   F   10      2       3 10470  65.00   M    3      2       3 10471  80.00   F   10      2       3 10472  80.00   M    9      2       3 10473  75.00   F    8      2       3 10474  90.00   F    8      2       3 10475  84.00   M    7      2       3 10476  63.00   F    3      2       3 10477  70.00   F    7      2       3 10478  80.00   F    9      2       3 10479  77.00   M    6      2       3 10480  79.00   M    6      2       3 10481  60.00   M    6      2       3 10482  62.00   F    9      2       3 10483  70.00   M    6      2       3 10484  40.00   M    5      2       2 10485  68.00   M    0      2       3 10486  58.00   F    2      2       2 10487  65.00   F    5      2       3 10488  67.00   M    4      2       3 10489  72.00   M    7      2       3 10490  68.00   F    7      2       3 10491  25.00   M    0      2       2 10492  66.00   F    3      2       3 10493  30.00   M    4      2       2 10494  60.00   M    1      2       3 10495  85.00   M    4      2       3 10496  85.00   M    1      2       3 10497  64.00   M    5      2       3 10498  63.00   M    3      2       3 10499  56.00   M    1      2       2 10500  60.00   M    6      2       3 10501  52.00   F    0      2       2 10502  64.00   M    1      2       3 10503  45.00   F   11      2       2 10504  49.00   M    1      2       2 10505  50.00   F    0      2       2 10506  71.00   F    2      2       3 10507  53.00   F    1      2       2 10508  65.00   M    1      2       3 10509  46.00   M    0      2       2 10510  55.00   M    2      2       2 10511  70.00   M    0      2       3 10512  43.00   M    3      2       2 10513  75.00   F    1      2       3 10514  75.00   F    0      2       3 10515  70.00   F    2      2       3 10516  42.00   M    0      2       2 10517  65.00   F    5      2       3 10518  75.00   M   19      2       3 10519  37.00   M   18      2       2 10520  51.00   F    5      2       2 10521  60.00   M    1      2       3 10522  70.00   M   12      2       3 10523  65.00   M   14      2       3 10524  67.00   F    3      2       3 10525  56.00   F   13      2       2 10526  70.00   M   13      2       3 10527  85.00   M   15      2       3 10528  65.00   F    6      2       3 10529  64.00   M    4      2       3 10530  71.00   F   11      2       3 10531  38.00   M    5      2       2 10532  61.00   F   12      2       3 10533  69.00   M   18      2       3 10534  70.00   M   10      2       3 10535  89.00   M    7      2       3 10536  62.00   M    1      2       3 10537  55.00   F    1      2       2 10538  46.00   M    5      2       2 10539  65.00   M   10      2       3 10540  50.00   M   10      2       2 10541  79.00   M    8      2       3 10542  82.00   M    2      2       3 10543  60.00   M    4      2       3 10544  70.00   F    5      2       3 10545  59.00   F    9      2       2 10546  71.00   M    7      2       3 10547  48.00   F    3      2       2 10548  85.00   F    7      2       3 10549  64.00   M    3      2       3 10550  87.00   F    7      2       3 10551  62.00   M    7      2       3 10552  65.00   M    4      2       3 10553  76.00   M    3      2       3 10554  69.00   M    4      2       3 10555  55.00   F    8      2       2 10556  75.00   F    7      2       3 10557  65.00   M    5      2       3 10558  59.00   F    8      2       2 10559  70.00   M    5      2       3 10560  38.00   M    7      2       2 10561  66.00   M    7      2       3 10562  66.00   M    6      2       3 10563  41.00   M    7      2       2 10564  65.00   M    8      2       3 10565  63.00   M    6      2       3 10566  40.00   F    2      2       2 10567  50.00   F    1      2       2 10568  72.00   M    5      2       3 10569  64.00   M    8      2       3 10570  22.00   M    2      2       2 10571  65.00   M    2      2       3 10572  70.00   M    7      2       3 10573  58.00   F    0      2       2 10574  35.00   M    8      2       2 10575  42.00   F    4      2       2 10576  75.00   M    8      2       3 10577  60.00   F    5      2       3 10578  65.00   M    4      2       3 10579  70.00   F    3      2       3 10580  66.00   M    4      2       3 10581  60.00   F    4      2       3 10582  56.00   M    1      2       2 10583  55.00   F    2      2       2 10584  49.00   F    4      2       2 10585  61.00   M    4      2       3 10586  72.00   M    6      2       3 10587  51.00   M    3      2       2 10588  61.00   M    3      2       3 10589  62.00   F    5      2       3 10590  60.00   M    0      2       3 10591  74.00   F    1      2       3 10592  59.00   M    1      2       2 10593  75.00   F    5      2       3 10594  77.00   M    2      2       3 10595  67.00   F    2      2       3 10596  70.00   M    1      2       3 10597  70.00   M    2      2       3 10598  49.00   F    2      2       2 10599  55.00   M    4      2       2 10600  56.00   F    2      2       2 10601  60.00   F    4      2       3 10602  60.00   F    2      2       3 10603  72.00   M    8      2       3 10604  54.00   F    8      2       2 10605  59.00   M   33      2       2 10606  64.00   F   36      2       3 10607  53.00   F    3      2       2 10608  63.00   M    2      2       3 10609  72.00   F    6      2       3 10610  78.00   M    2      2       3 10611  73.00   M   14      2       3 10612  85.00   M    1      2       3 10613  42.00   M    7      2       2 10614  77.00   M   13      2       3 10615  89.00   M   14      2       3 10616  72.00   M    7      2       3 10617  37.00   M    7      2       2 10618  78.00   M   12      2       3 10619  69.00   M   14      2       3 10620  65.00   F    0      2       3 10621  51.00   M    3      2       2 10622  74.00   M    3      2       3 10623  32.00   F    0      2       2 10624  51.00   M   11      2       2 10625  74.00   M    9      2       3 10626  52.00   M    4      2       2 10627  68.00   F   11      2       3 10628  66.00   M   10      2       3 10629  71.00   M   11      2       3 10630  65.00   M   10      2       3 10631  43.00   M    7      2       2 10632  80.00   M   10      2       3 10633  65.00   M    4      2       3 10634  76.00   M    8      2       3 10635  56.00   M    3      2       2 10636  60.00   F    7      2       3 10637  51.00   M   14      2       2 10638  64.00   M   11      2       3 10639  75.00   M    1      2       3 10640  57.00   F    4      2       2 10641  80.00   M    2      2       3 10642  81.00   M   12      2       3 10643  75.00   F   11      2       3 10644  88.00   M    8      2       3 10645  75.00   M    8      2       3 10646  65.00   M    7      2       3 10647  86.00   M    7      2       3 10648  76.00   M    5      2       3 10649  67.00   M    6      2       3 10650  20.00   M    2      2       2 10651  70.00   F    5      2       3 10652  50.00   M    3      2       2 10653  70.00   M    6      2       3 10654  80.00   M    4      2       3 10655  72.00   M    3      2       3 10656  55.00   F    3      2       2 10657  60.00   F    2      2       3 10658  66.00   M    5      2       3 10659  50.00   M    4      2       2 10660  55.00   M    3      2       2 10661  75.00   M    4      2       3 10662  68.00   M    0      2       3 10663  69.00   F    1      2       3 10664  59.00   M    2      2       2 10665  75.00   F    4      2       3 10666  82.00   M    2      2       3 10667  68.00   M    2      2       3 10668  52.00   M    0      2       2 10669  58.00   F    3      2       2 10670  62.00   M    2      2       3 10671  55.00   F    0      2       2 10672  70.00   F    4      2       3 10673  53.00   M    3      2       2 10674  50.00   M    2      2       2 10675  54.00   M    3      2       2 10676  45.00   M    0      2       2 10677  87.00   M    0      2       3 10678  81.00   M   18      2       3 10679  85.00   M    4      2       3 10680  54.00   M    1      2       2 10681  55.00   F   15      2       2 10682  64.00   M    4      2       3 10683  58.00   F   18      2       2 10684  65.00   F   13      2       3 10685  60.00   F   14      2       3 10686  60.00   M    5      2       3 10687  60.00   M    0      2       3 10688  76.00   M    1      2       3 10689  63.00   F   11      2       3 10690  70.00   M   11      2       3 10691  55.00   M    4      2       2 10692  81.00   M    1      2       3 10693  73.00   F    2      2       3 10694  49.00   F   12      2       2 10695  80.00   F    7      2       3 10696  65.00   M    4      2       3 10697  71.00   M   11      2       3 10698  60.00   M    1      2       3 10699  55.00   M    8      2       2 10700  65.00   M    9      2       3 10701  56.00   M    7      2       2 10702  76.00   M    8      2       3 10703  68.00   F    7      2       3 10704  85.00   M    6      2       3 10705  62.00   M   17      2       3 10706  70.00   F    6      2       3 10707  66.00   M    6      2       3 10708  60.00   M   10      2       3 10709  50.00   M    4      2       2 10710  95.00   M    5      2       3 10711  65.00   M    5      2       3 10712  60.00   M    5      2       3 10713  51.00   M    6      2       2 10714  63.00   M    7      2       3 10715  34.00   M    4      2       2 10716  64.00   M    5      2       3 10717  52.00   M    0      2       2 10718  65.00   M   12      2       3 10719  85.00   M    2      2       3 10720  67.00   M    1      2       3 10721  57.00   F    1      2       2 10722  63.00   M    1      2       3 10723  82.00   F    5      2       3 10724  50.00   M    4      2       2 10725  63.00   M    3      2       3 10726  52.00   M    1      2       2 10727  58.00   M    0      2       2 10728  81.00   M    2      2       3 10729  55.00   M   16      2       2 10730  68.00   M    9      2       3 10731  77.00   F   16      2       3 10732  75.00   F   18      2       3 10733  89.00   M   14      2       3 10734  68.00   M   15      2       3 10735  65.00   M    8      2       3 10736  66.00   M   15      2       3 10737  28.00   M    4      2       2 10738  74.00   M    5      2       3 10739  59.00   F   13      2       2 10740  77.00   M    4      2       3 10741  79.00   F   13      2       3 10742  75.00   M    9      2       3 10743  82.00   M    8      2       3 10744  41.00   F    1      2       2 10745  56.00   M    7      2       2 10746  63.00   M    3      2       3 10747  70.00   M   10      2       3 10748  83.00   F   11      2       3 10749  68.00   M   13      2       3 10750  88.00   M    7      2       3 10751  60.00   M    0      2       3 10752  70.00   M   10      2       3 10753  51.00   M    2      2       2 10754  47.00   M   10      2       2 10755  60.00   M    1      2       3 10756  65.00   M    7      2       3 10757  57.00   M    7      2       2 10758  84.00   F    7      2       3 10759  60.00   F    6      2       3 10760  54.00   F    4      2       2 10761  73.00   M    5      2       3 10762  80.00   F    6      2       3 10763  68.00   F    2      2       3 10764  60.00   M   16      2       3 10765  54.00   F    2      2       2 10766  58.00   M    1      2       2 10767  83.00   M    4      2       3 10768  75.00   M    3      2       3 10769  62.00   F    3      2       3 10770  59.00   M    0      2       2 10771  78.00   F    0      2       3 10772  63.00   F    1      2       3 10773  55.00   F    3      2       2 10774  74.00   F    0      2       3 10775  84.00   M   10      2       3 10776  84.00   M    4      2       3 10777  81.00   M    1      2       3 10778  38.00   F    0      2       2 10779  60.00   M    6      2       3 10780  57.00   F    7      2       2 10781  72.00   M   21      2       3 10782  42.00   M   15      2       2 10783  72.00   M   17      2       3 10784  72.00   M   13      2       3 10785  58.00   F   13      2       2 10786  70.00   M   11      2       3 10787  68.00   M    0      2       3 10788  47.00   M   11      2       2 10789  81.00   F   41      2       3 10790  75.00   F    9      2       3 10791  87.00   M   10      2       3 10792  90.00   F    8      2       3 10793  78.00   M    9      2       3 10794  51.00   M    5      2       2 10795  70.00   M    8      2       3 10796  63.00   F    7      2       3 10797  70.00   F    8      2       3 10798  57.00   F    9      2       2 10799  70.00   M    7      2       3 10800  62.00   M    6      2       3 10801  82.00   F    1      2       3 10802  80.00   F    5      2       3 10803  68.00   M    5      2       3 10804  75.00   M    7      2       3 10805  80.00   F    0      2       3 10806  62.00   M    4      2       3 10807  67.00   F    5      2       3 10808  64.00   F    0      2       3 10809  56.00   M    3      2       2 10810  59.00   M    1      2       2 10811  63.00   M    0      2       3 10812  53.00   M    7      2       2 10813  59.00   M    4      2       2 10814  56.00   M    5      2       2 10815  65.00   M    5      2       3 10816  65.00   F    9      2       3 10817  32.00   M    0      2       2 10818  65.00   F   17      2       3 10819  54.00   M    7      2       2 10820  80.00   M   14      2       3 10821  64.00   M   12      2       3 10822  54.00   M   11      2       2 10823  82.00   M    9      2       3 10824  70.00   M    0      2       3 10825  66.00   F    3      2       3 10826  84.00   M   10      2       3 10827  71.00   M   15      2       3 10828  92.00   M    8      2       3 10829  60.00   M   11      2       3 10830  58.00   F    8      2       2 10831  80.00   M    8      2       3 10832  74.00   M    8      2       3 10833  83.00   F   10      2       3 10834  70.00   F    7      2       3 10835  86.00   M   10      2       3 10836  38.00   M    7      2       2 10837  70.00   F    8      2       3 10838  80.00   M    7      2       3 10839  70.00   F    5      2       3 10840  47.00   F    9      2       2 10841  75.00   M    4      2       3 10842  59.00   M    7      2       2 10843  68.00   M    3      2       3 10844  82.00   F    5      2       3 10845  73.00   M    2      2       3 10846  87.00   M    3      2       3 10847  70.00   M   14      2       3 10848  48.00   F    3      2       2 10849  78.00   M   11      2       3 10850  52.00   F    0      2       2 10851  47.00   M    4      2       2 10852  70.00   M    2      2       3 10853  60.00   F    4      2       3 10854  78.00   M    0      2       3 10855  70.00   F    2      2       3 10856  64.00   F    2      2       3 10857  57.00   M    0      2       2 10858  56.00   M   13      2       2 10859  55.00   M   14      2       2 10860  67.00   M    4      2       3 10861  58.00   M   14      2       2 10862   3.00   F   12      2       1 10863  63.00   F   14      2       3 10864  48.00   F   12      2       2 10865  44.00   M    6      2       2 10866  77.00   M   11      2       3 10867  71.00   F    7      2       3 10868  55.00   M    9      2       2 10869  65.00   M    4      2       3 10870  68.00   M    9      2       3 10871  80.00   F   11      2       3 10872  78.00   M    9      2       3 10873  84.00   M    8      2       3 10874  75.00   M    6      2       3 10875  55.00   M    7      2       2 10876  70.00   F    2      2       3 10877  80.00   F    9      2       3 10878  70.00   M    8      2       3 10879  80.00   F    6      2       3 10880  56.00   M    5      2       2 10881  58.00   M    3      2       2 10882  60.00   F    1      2       3 10883  65.00   M    4      2       3 10884  88.00   M    6      2       3 10885  70.00   F    5      2       3 10886  72.00   M    3      2       3 10887  66.00   M    5      2       3 10888  90.00   M    6      2       3 10889  54.00   M    2      2       2 10890  76.00   F    2      2       3 10891  63.00   F    3      2       3 10892  57.00   M    2      2       2 10893  58.00   F    7      2       2 10894  84.00   M    1      2       3 10895  35.00   F    2      2       2 10896  31.00   M    1      2       2 10897  19.00   M    9      2       2 10898  58.00   M    0      2       2 10899  72.00   M   12      2       3 10900  76.00   M    9      2       3 10901  57.00   F   12      2       2 10902  57.00   M   22      2       2 10903  66.00   M   18      2       3 10904  62.00   M    6      2       3 10905  72.00   M   21      2       3 10906  60.00   F    8      2       3 10907  55.00   M    2      2       2 10908  80.00   M   10      2       3 10909  65.00   M   12      2       3 10910  65.00   M   16      2       3 10911  60.00   F    4      2       3 10912  72.00   M   10      2       3 10913  75.00   M   12      2       3 10914  75.00   M   11      2       3 10915  80.00   M    8      2       3 10916  65.00   M   12      2       3 10917  63.00   M   10      2       3 10918  72.00   F    9      2       3 10919  69.00   M   11      2       3 10920  93.00   F    9      2       3 10921  40.00   M   11      2       2 10922  75.00   M   17      2       3 10923  57.00   M    3      2       2 10924  79.00   M   11      2       3 10925  58.00   M   11      2       2 10926  54.00   M    6      2       2 10927  75.00   M    2      2       3 10928  55.00   M    5      2       2 10929  68.00   M    7      2       3 10930  54.00   M   26      2       2 10931  65.00   F    0      2       3 10932  74.00   M    4      2       3 10933  52.00   M    5      2       2 10934  63.00   M    6      2       3 10935  80.00   F    0      2       3 10936  65.00   F    6      2       3 10937  76.00   M    6      2       3 10938  57.00   F    3      2       2 10939  64.00   M    2      2       3 10940  66.00   M    1      2       3 10941  60.00   M    2      2       3 10942  80.00   M    3      2       3 10943  76.00   M    5      2       3 10944  61.00   M    5      2       3 10945  82.00   M    3      2       3 10946  34.00   M    0      2       2 10947  89.00   M    2      2       3 10948  80.00   M    2      2       3 10949  80.00   F    2      2       3 10950  74.00   M    3      2       3 10951  53.00   F    2      2       2 10952  66.00   M    0      2       3 10953  78.00   M    3      2       3 10954  55.00   M    1      2       2 10955  52.00   M    3      2       2 10956  33.00   M   14      2       2 10957  65.00   F    0      2       3 10958  46.00   M    2      2       2 10959  46.00   F   12      2       2 10960  68.00   F   19      2       3 10961  29.00   M   12      2       2 10962  63.00   M   10      2       3 10963  69.00   M   11      2       3 10964  42.00   M    4      2       2 10965  70.00   M    1      2       3 10966  86.00   M   11      2       3 10967  77.00   F    4      2       3 10968  67.00   M    8      2       3 10969  68.00   M    7      2       3 10970  73.00   M   10      2       3 10971  41.00   M   11      2       2 10972  83.00   M    8      2       3 10973  68.00   M    6      2       3 10974  79.00   M    7      2       3 10975  65.00   M    3      2       3 10976  55.00   M    2      2       2 10977  60.00   M    5      2       3 10978  45.00   M    2      2       2 10979  25.00   F    0      2       2 10980  89.00   M    6      2       3 10981  57.00   F    6      2       2 10982  51.00   F    6      2       2 10983  85.00   F    2      2       3 10984  84.00   M    5      2       3 10985  78.00   M    7      2       3 10986  58.00   M    3      2       2 10987  78.00   F    3      2       3 10988  65.00   M    2      2       3 10989  68.00   M    9      2       3 10990  78.00   F    4      2       3 10991  75.00   F    0      2       3 10992  78.00   M    5      2       3 10993  67.00   F    2      2       3 10994  63.00   M    3      2       3 10995  83.00   M    2      2       3 10996  11.00   M    3      2       1 10997  68.00   F    0      2       3 10998  77.00   M    2      2       3 10999  24.00   M    8      2       2 11000  75.00   F   19      2       3 11001  56.00   F   25      2       2 11002  56.00   M    8      2       2 11003  24.00   F   16      2       2 11004  65.00   M   15      2       3 11005  70.00   M   15      2       3 11006  31.00   F    1      2       2 11007  76.00   M    7      2       3 11008  55.00   F    9      2       2 11009  65.00   M   14      2       3 11010  61.00   M   13      2       3 11011  65.00   M    0      2       3 11012  62.00   M    4      2       3 11013  74.00   M    1      2       3 11014  78.00   M   10      2       3 11015  58.00   M    3      2       2 11016  67.00   M   11      2       3 11017  62.00   M    9      2       3 11018  70.00   F   10      2       3 11019  65.00   F    2      2       3 11020  75.00   M    5      2       3 11021  55.00   M    9      2       2 11022  92.00   M    8      2       3 11023  74.00   M    1      2       3 11024  91.00   F    6      2       3 11025  29.00   M    4      2       2 11026  81.00   M    7      2       3 11027  82.00   M    7      2       3 11028  46.00   M    2      2       2 11029  76.00   M    6      2       3 11030  74.00   M    5      2       3 11031  60.00   F   36      2       3 11032  30.00   F    2      2       2 11033  48.00   F    1      2       2 11034  64.00   M    2      2       3 11035  75.00   M    1      2       3 11036  73.00   M    1      2       3 11037  71.00   M    1      2       3 11038  86.00   F    1      2       3 11039  59.00   M    2      2       2 11040  79.00   M    2      2       3 11041  70.00   M    2      2       3 11042  87.00   F    0      2       3 11043  56.00   F    1      2       2 11044  25.00   M    0      2       2 11045  66.00   M   11      2       3 11046  73.00   M    1      2       3 11047  58.00   F    1      2       2 11048  60.00   F    6      2       3 11049  75.00   M    7      2       3 11050  60.00   M   11      2       3 11051  75.00   M   11      2       3 11052  70.00   F    8      2       3 11053  93.00   M    7      2       3 11054  66.00   M   16      2       3 11055  48.00   M   10      2       2 11056  68.00   M   11      2       3 11057  37.00   M   13      2       2 11058  74.00   F   12      2       3 11059  65.00   M    9      2       3 11060  61.00   M    9      2       3 11061  62.00   F    8      2       3 11062  34.00   M    7      2       2 11063  70.00   F    4      2       3 11064  72.00   M    4      2       3 11065  82.00   M    6      2       3 11066  70.00   M    7      2       3 11067  60.00   F    6      2       3 11068  69.00   F    0      2       3 11069  79.00   M    8      2       3 11070  34.00   M    1      2       2 11071  90.00   M    1      2       3 11072  52.00   M    0      2       2 11073  25.00   M    0      2       2 11074  44.00   M    0      2       2 11075  73.00   M    0      2       3 11076  60.00   M   43      2       3 11077  56.00   M    4      2       2 11078  59.00   M    2      2       2 11079  60.00   F   13      2       3 11080  85.00   M   11      2       3 11081  65.00   M    7      2       3 11082  78.00   M   10      2       3 11083  92.00   M    8      2       3 11084  68.00   M    8      2       3 11085  63.00   M    5      2       3 11086  58.00   F    8      2       2 11087  65.00   M    9      2       3 11088  80.00   F    7      2       3 11089 100.00   F    3      2       3 11090  48.00   M    2      2       2 11091  45.00   M    5      2       2 11092  62.00   M    2      2       3 11093  69.00   M    1      2       3 11094  60.00   F    4      2       3 11095  80.00   F    3      2       3 11096  29.00   M    2      2       2 11097  78.00   M   14      2       3 11098  64.00   M    1      2       3 11099  52.00   M    0      2       2 11100  68.00   M    1      2       3 11101  74.00   F   16      2       3 11102  64.00   M   12      2       3 11103  92.00   F    6      2       3 11104  65.00   F   12      2       3 11105  56.00   M   21      2       2 11106  68.00   F    8      2       3 11107  73.00   M    9      2       3 11108  58.00   M    7      2       2 11109  85.00   M    9      2       3 11110  33.00   M    9      2       2 11111  63.00   M    8      2       3 11112  46.00   M    4      2       2 11113  74.00   M    6      2       3 11114  62.00   M    6      2       3 11115  55.00   F    6      2       2 11116  50.00   F    0      2       2 11117  65.00   M    5      2       3 11118  63.00   F    3      2       3 11119  84.00   M    1      2       3 11120  67.00   M    2      2       3 11121  48.00   F    1      2       2 11122  65.00   F    1      2       3 11123  76.00   F    2      2       3 11124  65.00   M    1      2       3 11125  65.00   F   21      2       3 11126  41.00   M    0      2       2 11127  30.00   M    3      2       2 11128  76.00   F    2      2       3 11129  65.00   F    2      2       3 11130  76.00   F    7      2       3 11131  30.00   M    0      2       2 11132  92.00   M   14      2       3 11133  65.00   F   11      2       3 11134  55.00   F    5      2       2 11135  75.00   F    9      2       3 11136  60.00   M    2      2       3 11137  93.00   F    9      2       3 11138  72.00   M    3      2       3 11139  84.00   M    0      2       3 11140  82.00   M    5      2       3 11141  64.00   F    2      2       3 11142  65.00   M   21      2       3 11143  78.00   M    4      2       3 11144  60.00   M    3      2       3 11145  58.00   M    4      2       2 11146  35.00   F    2      2       2 11147  67.00   M    4      2       3 11148  79.00   M    1      2       3 11149  74.00   M    8      2       3 11150  56.00   F    0      2       2 11151  55.00   M    0      2       2 11152  80.00   M    2      2       3 11153  67.00   M    3      2       3 11154  68.00   M    1      2       3 11155  67.00   M    7      2       3 11156  82.00   M   13      2       3 11157  77.00   M    4      2       3 11158  60.00   F    0      2       3 11159  70.00   M    1      2       3 11160  84.00   F    0      2       3 11161  57.00   M    2      2       2 11162  29.00   F    1      2       2 11163  84.00   M   11      2       3 11164  82.00   F    9      2       3 11165  57.00   F   11      2       2 11166  48.00   F   12      2       2 11167  61.00   M   11      2       3 11168  59.00   M    9      2       2 11169  62.00   F    8      2       3 11170  81.00   M   11      2       3 11171  72.00   F    7      2       3 11172  45.00   M    8      2       2 11173  63.00   M    0      2       3 11174  74.00   M    9      2       3 11175  32.00   F    8      2       2 11176  55.00   F   10      2       2 11177  58.00   F    2      2       2 11178  84.00   F    5      2       3 11179  63.00   M    5      2       3 11180  61.00   M    4      2       3 11181  68.00   M    5      2       3 11182  90.00   F    5      2       3 11183  71.00   M    2      2       3 11184  82.00   M    3      2       3 11185  60.00   F   14      2       3 11186  76.00   M    3      2       3 11187  74.00   M    2      2       3 11188  86.00   M    1      2       3 11189  56.00   F    6      2       2 11190  74.00   F    4      2       3 11191   7.00   M   23      2       1 11192  65.00   M    0      2       3 11193  56.00   M    3      2       2 11194  60.00   M   12      2       3 11195  40.00   M   14      2       2 11196  58.00   F    2      2       2 11197  52.00   M   11      2       2 11198  66.00   F   13      2       3 11199  56.00   M    5      2       2 11200  60.00   M    0      2       3 11201  67.00   M    2      2       3 11202  54.00   F    2      2       2 11203  66.00   F    6      2       3 11204  47.00   M    7      2       2 11205  67.00   F    2      2       3 11206  45.00   F    3      2       2 11207  35.00   F    2      2       2 11208  70.00   M    1      2       3 11209  79.00   M    3      2       3 11210  53.00   M    3      2       2 11211  50.00   F    3      2       2 11212  48.00   M    1      2       2 11213  65.00   F    4      2       3 11214  72.00   M    2      2       3 11215  70.00   F    1      2       3 11216  50.00   M    2      2       2 11217  57.00   M    1      2       2 11218  70.00   F    1      2       3 11219  38.00   M    1      2       2 11220  70.00   M    4      2       3 11221  56.00   M   17      2       2 11222  84.00   F    0      2       3 11223  88.00   M   17      2       3 11224  49.00   M    1      2       2 11225  58.00   M    6      2       2 11226  78.00   F    5      2       3 11227  70.00   M   12      2       3 11228  51.00   M    3      2       2 11229  75.00   M    9      2       3 11230  70.00   F    0      2       3 11231  54.00   M    9      2       2 11232  55.00   M    1      2       2 11233  78.00   M    8      2       3 11234  73.00   M    9      2       3 11235  72.00   M   12      2       3 11236  41.00   M    8      2       2 11237  75.00   F    5      2       3 11238  58.00   F    5      2       2 11239  78.00   M    6      2       3 11240  66.00   M    3      2       3 11241  68.00   M    5      2       3 11242  62.00   F    4      2       3 11243  73.00   M    0      2       3 11244  71.00   M    1      2       3 11245  75.00   M    3      2       3 11246  60.00   F    2      2       3 11247  34.00   F    3      2       2 11248  70.00   M    3      2       3 11249  54.00   M    1      2       2 11250  41.00   F    1      2       2 11251  53.00   M    0      2       2 11252  60.00   F    1      2       3 11253  68.00   M    0      2       3 11254  70.00   M    1      2       3 11255  54.00   M   19      2       2 11256  50.00   M    1      2       2 11257  65.00   F    0      2       3 11258  67.00   F   17      2       3 11259  59.00   M   17      2       2 11260  78.00   M    3      2       3 11261  62.00   F    0      2       3 11262  20.00   F   14      2       2 11263  77.00   M    4      2       3 11264  32.00   M    6      2       2 11265  75.00   M    0      2       3 11266  75.00   M    8      2       3 11267  61.00   M    6      2       3 11268  75.00   M    0      2       3 11269  65.00   M    2      2       3 11270  70.00   M    2      2       3 11271  44.00   M    4      2       2 11272  63.00   F    2      2       3 11273  81.00   M    2      2       3 11274  72.00   F    2      2       3 11275  66.00   M    2      2       3 11276  55.00   M    0      2       2 11277  62.00   M    0      2       3 11278  71.00   M   25      2       3 11279  50.00   F   10      2       2 11280  80.00   F    2      2       3 11281  65.00   M    0      2       3 11282  74.00   M    9      2       3 11283  48.00   M   12      2       2 11284  79.00   F    0      2       3 11285  79.00   M    9      2       3 11286  40.00   M    4      2       2 11287  83.00   M    6      2       3 11288  67.00   M    5      2       3 11289  65.00   F    3      2       3 11290  73.00   M    2      2       3 11291  60.00   M    1      2       3 11292  57.00   M    5      2       2 11293  65.00   F    0      2       3 11294  65.00   M    1      2       3 11295  70.00   F    2      2       3 11296  50.00   M    0      2       2 11297  89.00   M    5      2       3 11298  49.00   F    3      2       2 11299  88.00   M   10      2       3 11300  68.00   F    6      2       3 11301  67.00   M   23      2       3 11302  41.00   M    0      2       2 11303  65.00   M    8      2       3 11304  70.00   M    0      2       3 11305  51.00   M    9      2       2 11306  63.00   M    6      2       3 11307  70.00   M   10      2       3 11308  82.00   M    7      2       3 11309  30.00   F    8      2       2 11310  48.00   F    0      2       2 11311  73.00   F    8      2       3 11312  79.00   M    5      2       3 11313  68.00   F    1      2       3 11314  84.00   M    5      2       3 11315  58.00   M    3      2       2 11316  55.00   F    8      2       2 11317  58.00   M    1      2       2 11318  72.00   F    3      2       3 11319  32.00   M    7      2       2 11320  80.00   F    0      2       3 11321  68.00   M    0      2       3 11322  75.00   F    8      2       3 11323  71.00   M   12      2       3 11324  50.00   M   12      2       2 11325  58.00   M    7      2       2 11326  73.00   F   10      2       3 11327  75.00   F    8      2       3 11328  47.00   M    7      2       2 11329  68.00   M    0      2       3 11330  50.00   M    8      2       2 11331  48.00   F    8      2       2 11332  45.00   F    4      2       2 11333  49.00   M    3      2       2 11334  52.00   M    3      2       2 11335  51.00   M    3      2       2 11336  84.00   M   12      2       3 11337  29.00   M    1      2       2 11338  72.00   M    0      2       3 11339  41.00   F    5      2       2 11340  96.00   M    2      2       3 11341  58.00   M    8      2       2 11342  77.00   M   17      2       3 11343  50.00   M    0      2       2 11344  51.00   M    8      2       2 11345  90.00   F    8      2       3 11346  72.00   M    4      2       3 11347  44.00   M    5      2       2 11348  74.00   M    1      2       3 11349  60.00   M    7      2       3 11350  60.00   M    4      2       3 11351  66.00   M    5      2       3 11352  35.00   M    6      2       2 11353  59.00   M    4      2       2 11354  72.00   M    5      2       3 11355  65.00   F    4      2       3 11356  50.00   F    0      2       2 11357  72.00   M    2      2       3 11358  68.00   M    2      2       3 11359  65.00   F    0      2       3 11360  40.00   M    0      2       2 11361  54.00   M    2      2       2 11362  62.00   M    0      2       3 11363  78.00   F    4      2       3 11364  80.00   M    2      2       3 11365  48.00   M    0      2       2 11366  75.00   M    0      2       3 11367  58.00   M    1      2       2 11368  89.00   M    5      2       3 11369  27.00   M   18      2       2 11370  78.00   M    1      2       3 11371  60.00   F    9      2       3 11372  80.00   F    7      2       3 11373  40.00   M    1      2       2 11374  60.00   F    5      2       3 11375  52.00   M    4      2       2 11376  72.00   M    5      2       3 11377  61.00   M    5      2       3 11378  40.00   F    1      2       2 11379  41.00   M    4      2       2 11380  41.00   M    1      2       2 11381  65.00   M    0      2       3 11382  69.00   M   13      2       3 11383  40.00   M    5      2       2 11384  60.00   M    0      2       3 11385  53.00   F    0      2       2 11386  66.00   M    3      2       3 11387  66.00   M   12      2       3 11388  80.00   F   12      2       3 11389  65.00   M   11      2       3 11390  80.00   M    2      2       3 11391  60.00   F    3      2       3 11392  77.00   M    4      2       3 11393  48.00   M    4      2       2 11394  66.00   F    3      2       3 11395  45.00   M    3      2       2 11396  70.00   M    4      2       3 11397  45.00   F    1      2       2 11398  58.00   M    0      2       2 11399  78.00   F   19      2       3 11400  59.00   M    6      2       2 11401  70.00   M    4      2       3 11402  53.00   F   12      2       2 11403  56.00   M    4      2       2 11404  88.00   F    0      2       3 11405  49.00   F    0      2       2 11406  79.00   M    0      2       3 11407  70.00   M    1      2       3 11408  62.00   M    0      2       3 11409  85.00   M    9      2       3 11410  56.00   M    9      2       2 11411  64.00   M    2      2       3 11412  54.00   M    7      2       2 11413  17.00   F    6      2       1 11414  80.00   F    4      2       3 11415  65.00   M    2      2       3 11416  60.00   M   23      2       3 11417  73.00   M    6      2       3 11418  30.00   F    8      2       2 11419  52.00   M    5      2       2 11420  68.00   M    0      2       3 11421  74.00   M    7      2       3 11422  80.00   M   10      2       3 11423  65.00   M    8      2       3 11424  82.00   F    1      2       3 11425  65.00   F    6      2       3 11426  70.00   M    6      2       3 11427  36.00   M    2      2       2 11428  56.00   F    5      2       2 11429  17.00   M    1      2       1 11430  38.00   M    1      2       2 11431  68.00   M    3      2       3 11432  68.00   M    3      2       3 11433  76.00   M    3      2       3 11434  68.00   M    2      2       3 11435  38.00   F    0      2       2 11436  60.00   M    0      2       3 11437  65.00   F    0      2       3 11438  74.00   F   11      2       3 11439  75.00   M    7      2       3 11440  79.00   F   17      2       3 11441  65.00   M   11      2       3 11442  48.00   F   10      2       2 11443  81.00   M    1      2       3 11444  77.00   M    3      2       3 11445  70.00   M    0      2       3 11446  65.00   F    1      2       3 11447  89.00   M    1      2       3 11448  55.00   M    0      2       2 11449  68.00   M    4      2       3 11450  65.00   M    4      2       3 11451  70.00   M   10      2       3 11452  70.00   M    0      2       3 11453  82.00   M   11      2       3 11454  69.00   M   13      2       3 11455  60.00   M    2      2       3 11456  74.00   M    9      2       3 11457  48.00   F    3      2       2 11458  45.00   M    3      2       2 11459  67.00   M    6      2       3 11460  53.00   F    3      2       2 11461  73.00   M    4      2       3 11462  54.00   M    0      2       2 11463  60.00   M    2      2       3 11464  55.00   M    0      2       2 11465  48.00   M    3      2       2 11466  65.00   M   13      2       3 11467  65.00   F   11      2       3 11468  32.00   M    2      2       2 11469  60.00   M    5      2       3 11470  64.00   M   10      2       3 11471  87.00   F    9      2       3 11472  72.00   M   10      2       3 11473  62.00   M   10      2       3 11474  40.00   M    2      2       2 11475  71.00   M    4      2       3 11476  63.00   M    5      2       3 11477  82.00   M    6      2       3 11478  76.00   F    5      2       3 11479  78.00   M    5      2       3 11480  65.00   M    3      2       3 11481  75.00   M    3      2       3 11482  70.00   M    3      2       3 11483  52.00   F    5      2       2 11484  27.00   F    0      2       2 11485  75.00   M   20      2       3 11486  43.00   M    0      2       2 11487  60.00   M   20      2       3 11488  65.00   F    9      2       3 11489  70.00   F    4      2       3 11490  50.00   M   13      2       2 11491  48.00   M    3      2       2 11492  62.00   M   14      2       3 11493  70.00   M    5      2       3 11494  53.00   M    6      2       2 11495  49.00   M   12      2       2 11496  68.00   F    2      2       3 11497  45.00   M    8      2       2 11498  43.00   M    9      2       2 11499  85.00   M    9      2       3 11500  46.00   M    7      2       2 11501  35.00   M    9      2       2 11502  65.00   F    7      2       3 11503  91.00   F    6      2       3 11504  86.00   M    7      2       3 11505  94.00   M    1      2       3 11506  67.00   M    2      2       3 11507  55.00   F    2      2       2 11508  75.00   M    1      2       3 11509  36.00   M    1      2       2 11510  55.00   F   30      2       2 11511  41.00   M   18      1       2 11512   0.00   F   14      1       1 11513  13.00   F   17      1       1 11514   0.00   M   15      1       1 11515  26.00   M    7      1       2 11516   0.00   M   22      1       1 11517  32.00   M   14      1       2 11518  63.00   M   15      1       3 11519  20.00   F   15      1       2 11520  67.00   F   13      1       3 11521  25.00   M   13      1       2 11522  56.00   F   19      1       2 11523  35.00   M   18      1       2 11524  35.00   M   20      1       2 11525  53.00   F   22      1       2 11526  39.00   M   25      1       2 11527  21.00   M   24      1       2 11528  31.00   M   24      1       2 11529  35.00   M   17      1       2 11530  35.00   M   15      1       2 11531  64.00   F   16      1       3 11532  36.00   F   16      1       2 11533  27.00   M   15      1       2 11534  51.00   M   17      1       2 11535  22.00   M   14      1       2 11536  46.00   M   20      1       2 11537  38.00   M   15      1       2 11538  41.00   M   14      1       2 11539  30.00   F   22      1       2 11540  24.00   M   14      1       2 11541  60.00   M   27      1       3 11542  22.00   M   20      1       2 11543  32.00   M   14      1       2 11544  40.00   M   15      1       2 11545  65.00   M   15      1       3 11546  56.00   F   16      1       2 11547  56.00   F   24      1       2 11548  47.00   M   14      1       2 11549  70.00   F   20      1       3 11550  23.00   M   14      1       2 11551  37.00   F   12      1       2 11552  63.00   M   19      1       3 11553  59.00   F   19      1       2 11554  26.00   M   21      1       2 11555  26.00   M   19      1       2 11556  63.00   F   17      1       3 11557  69.00   M   17      1       3 11558   9.00   F   20      1       1 11559   7.00   F   20      1       1 11560  34.00   M   18      1       2 11561  35.00   M   16      1       2 11562  70.00   F   37      1       3 11563  64.00   M   23      1       3 11564  45.00   M   24      1       2 11565   0.80   M   16      1       1 11566  20.00   M   21      1       2 11567  25.00   F   21      1       2 11568  35.00   F   21      1       2 11569  33.00   F   18      1       2 11570  22.00   M   19      1       2 11571  18.00   M   13      1       2 11572  21.00   M   14      1       2 11573  54.00   F   18      1       2 11574  28.00   F   18      1       2 11575  23.00   F   18      1       2 11576  21.00   M   14      1       2 11577  23.00   M   15      1       2 11578  70.00   M   13      1       3 11579  32.00   F   15      1       2 11580  38.00   M   15      1       2 11581  18.00   M   15      1       2 11582  63.00   F   35      1       3 11583  20.00   M    9      1       2 11584  24.00   M   18      1       2 11585  39.00   M   20      1       2 11586  38.00   M   14      1       2 11587  21.00   M   14      1       2 11588  31.00   M   20      1       2 11589  42.00   M   14      1       2 11590  35.00   M   16      1       2 11591  29.00   M   21      1       2 11592  13.00   M   17      1       1 11593  32.00   M   13      1       2 11594  34.00   M   13      1       2 11595  21.00   M   15      1       2 11596  24.00   M   13      1       2 11597  52.00   M   23      1       2 11598  48.00   F   26      1       2 11599  26.00   F   23      1       2 11600  40.00   M   14      1       2 11601  19.00   M   17      1       2 11602  40.00   F   21      1       2 11603  35.00   M   13      1       2 11604  41.00   M   18      1       2 11605  34.00   M   14      1       2 11606  26.00   M   18      1       2 11607  60.00   F   16      1       3 11608  40.00   M   15      1       2 11609  62.00   F   50      1       3 11610  24.00   M   17      1       2 11611  37.00   M   25      1       2 11612  27.00   M   18      1       2 11613  33.00   M   13      1       2 11614  49.00   M   14      1       2 11615  26.00   M   12      1       2 11616  63.00   M   27      1       3 11617  63.00   M   17      1       3 11618  27.00   F   17      1       2 11619  24.00   M   23      1       2 11620  22.00   M   16      1       2 11621  14.00   M   41      1       1 11622  48.00   M   21      1       2 11623  30.00   M   21      1       2 11624  41.00   M   21      1       2 11625  66.00   M   21      1       3 11626  59.00   M   21      1       2 11627  39.00   M   21      1       2 11628  60.00   M   21      1       3 11629  63.00   M   30      1       3 11630  73.00   M   21      1       3 11631  45.00   M   21      1       2 11632  60.00   F   22      1       3 11633  70.00   M   18      1       3 11634  26.00   M   26      1       2 11635  20.00   M   18      1       2 11636  21.00   M   16      1       2 11637  57.00   M   24      1       2 11638  43.00   M   22      1       2 11639  78.00   M   13      1       3 11640  60.00   M   26      1       3 11641  38.00   M   17      1       2 11642  19.00   M   14      1       2 11643  39.00   M   17      1       2 11644  39.00   M   17      1       2 11645  54.00   M   14      1       2 11646  40.00   M   15      1       2 11647  34.00   M   24      1       2 11648  47.00   F   19      1       2 11649  63.00   F   14      1       3 11650  43.00   M   14      1       2 11651  52.00   M   14      1       2 11652  68.00   M   14      1       3 11653  62.00   F   14      1       3 11654  40.00   M   16      1       2 11655  67.00   F   27      1       3 11656  41.00   F   20      1       2 11657  41.00   M   21      1       2 11658  32.00   M   15      1       2 11659  62.00   F   22      1       3 11660  20.00   M   15      1       2 11661  37.00   M   12      1       2 11662  57.00   M   21      1       2 11663  22.00   M   16      1       2 11664  26.00   M   12      1       2 11665  32.00   F   22      1       2 11666  35.00   M   15      1       2 11667  56.00   F   15      1       2 11668  58.00   M   15      1       2 11669  43.00   M   15      1       2 11670  33.00   M   19      1       2 11671  41.00   F   19      1       2 11672  29.00   M   14      1       2 11673  50.00   M   14      1       2 11674  35.00   M   17      1       2 11675  68.00   M   17      1       3 11676  32.00   M   19      1       2 11677  36.00   M   19      1       2 11678  65.00   M   19      1       3 11679  28.00   F   17      1       2 11680  57.00   M   18      1       2 11681  26.00   F   16      1       2 11682  72.00   F   16      1       3 11683  35.00   M   26      1       2 11684  23.00   M   17      1       2 11685  27.00   F   18      1       2 11686  50.00   M   17      1       2 11687  55.00   M   21      1       2 11688  68.00   M   12      1       3 11689  32.00   M   17      1       2 11690   4.00   M   17      1       1 11691  13.00   M   17      1       1 11692   9.00   F   24      1       1 11693  19.00   F   17      1       2 11694  27.00   M   19      1       2 11695  48.00   F   14      1       2 11696  40.00   F   17      1       2 11697  22.00   M   25      1       2 11698  27.00   M   15      1       2 11699  42.00   M   17      1       2 11700  27.00   M   17      1       2 11701  48.00   M   16      1       2 11702  57.00   M   16      1       2 11703   8.00   M   11      1       1 11704  48.00   F   18      1       2 11705  33.00   M   12      1       2 11706  28.00   F   23      1       2 11707  48.00   F   34      1       2 11708  35.00   M   15      1       2 11709  11.00   F   15      1       1 11710  32.00   M   15      1       2 11711  46.00   M   15      1       2 11712  43.00   M   15      1       2 11713  50.00   F   21      1       2 11714  27.00   M   23      1       2 11715  31.00   M   17      1       2 11716  26.00   M   15      1       2 11717  32.00   M   16      1       2 11718  75.00   F   16      1       3 11719  58.00   M   16      1       2 11720  24.00   F   19      1       2 11721  60.00   F   15      1       3 11722  38.00   F   19      1       2 11723  19.00   M   37      1       2 11724  38.00   M   24      1       2 11725  55.00   M   24      1       2 11726  25.00   M   20      1       2 11727   2.00   M   19      1       1 11728  13.00   M   15      1       1 11729  12.00   F   15      1       1 11730  10.00   M   15      1       1 11731  49.00   M   15      1       2 11732  20.00   F   15      1       2 11733   5.00   M   21      1       1 11734  36.00   M   19      1       2 11735   7.00   F   19      1       1 11736  37.00   M   21      1       2 11737  60.00   F   23      1       3 11738   8.00   F   21      1       1 11739  18.00   M   21      1       2 11740  27.00   M   24      1       2 11741  16.00   F   24      1       1 11742  35.00   F   24      1       2 11743  20.00   M   23      1       2 11744  14.00   F   23      1       1 11745  45.00   F   23      1       2 11746  39.00   M   15      1       2 11747  62.00   M   20      1       3 11748  43.00   M   19      1       2 11749  32.00   F   19      1       2 11750  39.00   F   19      1       2 11751  26.00   M   18      1       2 11752  10.00   F   18      1       1 11753  51.00   M   18      1       2 11754  35.00   F   18      1       2 11755  33.00   M   26      1       2 11756  36.00   M   17      1       2 11757  59.00   M   15      1       2 11758  52.00   M   21      1       2 11759  39.00   M   21      1       2 11760  41.00   M   12      1       2 11761  30.00   M   18      1       2 11762  27.00   M   12      1       2 11763  35.00   M   18      1       2 11764  26.00   M   22      1       2 11765  23.00   M   12      1       2 11766  35.00   M   15      1       2 11767  28.00   M   12      1       2 11768  32.00   M   20      1       2 11769  72.00   M   14      1       3 11770   1.00   M   16      1       1 11771  38.00   F   37      1       2 11772  25.00   M   18      1       2 11773  32.00   F   15      1       2 11774  28.00   F   31      1       2 11775  13.00   M   14      1       1 11776  51.00   F   16      1       2 11777  42.00   M   16      1       2 11778  33.00   M   24      1       2 11779  16.00   F   16      1       1 11780  65.00   F   16      1       3 11781  30.00   F   24      1       2 11782  54.00   F   16      1       2 11783  58.00   F   20      1       2 11784  54.00   F    7      1       2 11785  43.00   M   14      1       2 11786  47.00   M   18      1       2 11787  25.00   M   20      1       2 11788  45.00   F   18      1       2 11789  30.00   M   18      1       2 11790  43.00   M   18      1       2 11791  50.00   M   20      1       2 11792  35.00   M   18      1       2 11793  25.00   M   24      1       2 11794  64.00   M   18      1       3 11795  23.00   F   14      1       2 11796  52.00   M   14      1       2 11797  59.00   F   15      1       2 11798  12.00   M   17      1       1 11799  65.00   M   15      1       3 11800  66.00   M   15      1       3 11801  37.00   M   15      1       2 11802  70.00   F   19      1       3 11803   1.50   F   15      1       1 11804  38.00   M   12      1       2 11805  26.00   F   12      1       2 11806  55.00   F   17      1       2 11807  32.00   M   16      1       2 11808   5.00   M   16      1       1 11809  55.00   M   14      1       2 11810  11.00   F   14      1       1 11811  50.00   F   12      1       2 11812  33.00   M   22      1       2 11813  33.00   M   16      1       2 11814  41.00   F   13      1       2 11815  25.00   M   19      1       2 11816  29.00   M   19      1       2 11817  45.00   M   22      1       2 11818  39.00   M   19      1       2 11819   6.00   M   14      1       1 11820  25.00   F   14      1       2 11821  18.00   M   20      1       2 11822   6.00   M   18      1       1 11823  28.00   F   18      1       2 11824  68.00   F   20      1       3 11825  21.00   M   14      1       2 11826  48.00   F   21      1       2 11827  10.00   F   21      1       1 11828  50.00   M   16      1       2 11829  24.00   M   14      1       2 11830  36.00   M   23      1       2 11831  20.00   M   23      1       2 11832  19.00   M   16      1       2 11833  38.00   M   12      1       2 11834  38.00   M   12      1       2 11835  26.00   M   16      1       2 11836  28.00   M   12      1       2 11837  22.00   M   12      1       2 11838  29.00   M   12      1       2 11839  26.00   M   12      1       2 11840  32.00   M   14      1       2 11841  23.00   M   14      1       2 11842  28.00   M   14      1       2 11843  21.00   M   14      1       2 11844  65.00   F   17      1       3 11845  34.00   M   17      1       2 11846  17.00   M   17      1       1 11847  37.00   M   17      1       2 11848  38.00   M   17      1       2 11849  34.00   M   16      1       2 11850  16.00   M   16      1       1 11851  60.00   M   13      1       3 11852  63.00   M   20      1       3 11853  30.00   M   12      1       2 11854  50.00   M   12      1       2 11855  65.00   M   17      1       3 11856  48.00   F   17      1       2 11857  65.00   M   12      1       3 11858  42.00   M   22      1       2 11859  39.00   M   18      1       2 11860  32.00   M   22      1       2 11861  32.00   M   18      1       2 11862  26.00   F   15      1       2 11863  55.00   F   16      1       2 11864  21.00   F   16      1       2 11865  43.00   M   18      1       2 11866  43.00   M   22      1       2 11867  47.00   M   17      1       2 11868  30.00   M   16      1       2 11869  36.00   M   13      1       2 11870  28.00   M   17      1       2 11871  46.00   M   25      1       2 11872  20.00   M   21      1       2 11873  39.00   M   16      1       2 11874  23.00   F   14      1       2 11875  30.00   F   25      1       2 11876  17.00   M   15      1       1 11877  13.00   F   12      1       1 11878  30.00   F   14      1       2 11879  50.00   M   16      1       2 11880  19.00   M   16      1       2 11881  24.00   M   20      1       2 11882   7.00   F   20      1       1 11883  36.00   M   38      1       2 11884  27.00   F   20      1       2 11885  25.00   F   14      1       2 11886  21.00   F   23      1       2 11887  28.00   M   20      1       2 11888  47.00   F   14      1       2 11889  10.00   M   14      1       1 11890  34.00   F   14      1       2 11891  38.00   F   17      1       2 11892  14.00   M   20      1       1 11893  27.00   M   23      1       2 11894  18.00   F   19      1       2 11895  30.00   F   37      1       2 11896  29.00   M   16      1       2 11897  61.00   M   16      1       3 11898  18.00   F   12      1       2 11899  26.00   M   12      1       2 11900  26.00   M   12      1       2 11901  25.00   F   27      1       2 11902  54.00   M   16      1       2 11903  28.00   F   15      1       2 11904  46.00   F   14      1       2 11905  35.00   F   17      1       2 11906   0.30   M   14      1       1 11907  26.00   F   14      1       2 11908  56.00   M   11      1       2 11909  32.00   M   13      1       2 11910  25.00   F   14      1       2 11911  30.00   F   27      1       2 11912  13.00   F   27      1       1 11913  30.00   M   29      1       2 11914  30.00   M   23      1       2 11915  22.00   M   23      1       2 11916  40.00   M   21      1       2 11917  30.00   M   15      1       2 11918  25.00   M   15      1       2 11919  37.00   M   15      1       2 11920  43.00   M   24      1       2 11921  24.00   M   15      1       2 11922  47.00   M   16      1       2 11923  28.00   F   16      1       2 11924  32.00   M   16      1       2 11925  49.00   M   13      1       2 11926  32.00   M   15      1       2 11927  10.00   F   24      1       1 11928  30.00   M   31      1       2 11929  22.00   M   23      1       2 11930  39.00   M   16      1       2 11931  35.00   M   22      1       2 11932  32.00   M   53      1       2 11933  23.00   M   22      1       2 11934  28.00   M   24      1       2 11935  46.00   M   18      1       2 11936  17.00   M   16      1       1 11937  26.00   M   31      1       2 11938  35.00   M   22      1       2 11939  31.00   M   22      1       2 11940  24.00   M   28      1       2 11941  60.00   M   27      1       3 11942  27.00   M   16      1       2 11943  14.00   M   16      1       1 11944  26.00   M   22      1       2 11945  20.00   M   22      1       2 11946  22.00   M   15      1       2 11947  58.00   M   19      1       2 11948  38.00   M   22      1       2 11949  44.00   M   22      1       2 11950  37.00   M   14      1       2 11951  21.00   M    7      1       2 11952  17.00   M   21      1       1 11953  19.00   F   31      1       2 11954  28.00   M   21      1       2 11955  37.00   M   25      1       2 11956  45.00   M   18      1       2 11957  38.00   M   27      1       2 11958  80.00   F   23      1       3 11959  55.00   F   18      1       2 11960  42.00   F   18      1       2 11961  39.00   F   18      1       2 11962  18.00   M   15      1       2 11963  33.00   F   18      1       2 11964  52.00   F   21      1       2 11965   6.00   M   18      1       1 11966  27.00   F   30      1       2 11967  20.00   M   18      1       2 11968   8.00   M   23      1       1 11969  30.00   F   23      1       2 11970  50.00   F   14      1       2 11971  27.00   M   30      1       2 11972  27.00   M   35      1       2 11973  66.00   F   30      1       3 11974  47.00   F   18      1       2 11975  65.00   F   14      1       3 11976   7.00   M   14      1       1 11977  13.00   M   16      1       1 11978  50.00   M   16      1       2 11979  45.00   M   25      1       2 11980  32.00   F   15      1       2 11981  21.00   F   15      1       2 11982  45.00   M   25      1       2 11983  27.00   M   13      1       2 11984  22.00   F   16      1       2 11985  48.00   M   27      1       2 11986  75.00   M   12      1       3 11987  55.00   M   20      1       2 11988  40.00   F   12      1       2 11989  43.00   M   12      1       2 11990  28.00   F   12      1       2 11991  45.00   F   12      1       2 11992  22.00   F   12      1       2 11993  20.00   F   14      1       2 11994  11.00   M   14      1       1 11995  22.00   M   14      1       2 11996  46.00   F   15      1       2 11997  28.00   M   11      1       2 11998  14.00   F   11      1       1 11999  22.00   M   11      1       2 12000  40.00   F   17      1       2 12001  20.00   M   11      1       2 12002  17.00   F   11      1       1 12003  12.00   F   11      1       1 12004  35.00   F   23      1       2 12005  62.00   M   12      1       3 12006  33.00   F   22      1       2 12007   9.00   M   18      1       1 12008  75.00   F   18      1       3 12009  48.00   F   25      1       2 12010   8.00   F   18      1       1 12011  65.00   F   17      1       3 12012  20.00   M   25      1       2 12013  28.00   M   13      1       2 12014  35.00   F   14      1       2 12015  15.00   M   17      1       1 12016  60.00   F   25      1       3 12017  16.00   F   17      1       1 12018  13.00   F   17      1       1 12019  35.00   M   47      1       2 12020  64.00   F   31      1       3 12021  25.00   M   32      1       2 12022  24.00   F   19      1       2 12023  27.00   M   32      1       2 12024  30.00   F   17      1       2 12025  19.00   M   12      1       2 12026  32.00   F   17      1       2 12027  13.00   M   12      1       1 12028  12.00   M   12      1       1 12029  55.00   M   31      1       2 12030  54.00   M   27      1       2 12031  69.00   M   17      1       3 12032  62.00   M   20      1       3 12033  31.00   F   19      1       2 12034  26.00   F   21      1       2 12035  34.00   M   33      1       2 12036   1.00   M   19      1       1 12037  18.00   F   19      1       2 12038  16.00   M   19      1       1 12039  40.00   F   17      1       2 12040  20.00   F   16      1       2 12041  57.00   M   22      1       2 12042  40.00   M   16      1       2 12043  29.00   F   16      1       2 12044  54.00   M   15      1       2 12045  22.00   M   23      1       2 12046  45.00   M   12      1       2 12047  23.00   M   17      1       2 12048  45.00   M   16      1       2 12049  38.00   F   15      1       2 12050  26.00   F   23      1       2 12051  30.00   M   15      1       2 12052  13.00   F   14      1       1 12053  54.00   M   14      1       2 12054  41.00   M   19      1       2 12055  68.00   M   23      1       3 12056  60.00   F   23      1       3 12057  23.00   F   21      1       2 12058  24.00   M   22      1       2 12059  35.00   F   16      1       2 12060  78.00   M   15      1       3 12061  22.00   M   14      1       2 12062  45.00   F   27      1       2 12063  24.00   F   28      1       2 12064  45.00   F   28      1       2 12065  30.00   M   24      1       2 12066  52.00   F   16      1       2 12067  38.00   M   16      1       2 12068  32.00   F   17      1       2 12069  35.00   M   26      1       2 12070  32.00   F   17      1       2 12071  12.00   F   20      1       1 12072   7.00   M   19      1       1 12073  38.00   M   17      1       2 12074  49.00   F   20      1       2 12075  27.00   M   27      1       2 12076  25.00   M   24      1       2 12077  33.00   M   20      1       2 12078  62.00   F   17      1       3 12079  34.00   F   31      1       2 12080  20.00   F   21      1       2 12081  22.00   F   21      1       2 12082   6.00   M   20      1       1 12083  17.00   M   24      1       1 12084  42.00   M   24      1       2 12085  11.00   M   16      1       1 12086  30.00   M   22      1       2 12087  20.00   F   25      1       2 12088  19.00   F   16      1       2 12089  32.00   M   21      1       2 12090  36.00   F   15      1       2 12091  37.00   M   19      1       2 12092  35.00   F   15      1       2 12093  46.00   F   15      1       2 12094  50.00   M   15      1       2 12095  16.00   M   15      1       1 12096  72.00   F   15      1       3 12097  22.00   M   15      1       2 12098  60.00   M   15      1       3 12099  40.00   M    4      1       2 12100  30.00   F    3      1       2 12101  34.00   M   26      1       2 12102  45.00   M   17      1       2 12103  29.00   F   13      1       2 12104  29.00   M   13      1       2 12105  51.00   M   45      1       2 12106  18.00   F   20      1       2 12107  30.00   M   17      1       2 12108  25.00   M   17      1       2 12109  51.00   M   19      1       2 12110  48.00   M   16      1       2 12111  52.00   F   17      1       2 12112  36.00   F   17      1       2 12113  15.00   F   19      1       1 12114  19.00   F   20      1       2 12115  27.00   M   19      1       2 12116  42.00   F   19      1       2 12117   5.00   M   19      1       1 12118  40.00   M   19      1       2 12119  26.00   M   31      1       2 12120  11.00   F   15      1       1 12121  35.00   F   44      1       2 12122  16.00   F   15      1       1 12123  40.00   F   25      1       2 12124  25.00   M   19      1       2 12125  52.00   M   16      1       2 12126  18.00   F   15      1       2 12127  45.00   M   15      1       2 12128  55.00   F   15      1       2 12129  26.00   M   16      1       2 12130  47.00   M   19      1       2 12131  30.00   F   15      1       2 12132  15.00   M   28      1       1 12133  40.00   F   15      1       2 12134  23.00   M   18      1       2 12135   0.80   M   15      1       1 12136  32.00   M   18      1       2 12137  30.00   M   22      1       2 12138  16.00   F   19      1       1 12139  35.00   F   19      1       2 12140  53.00   F   17      1       2 12141  40.00   F   15      1       2 12142  35.00   M   15      1       2 12143  36.00   F   15      1       2 12144  41.00   M   15      1       2 12145  13.00   F   18      1       1 12146  49.00   F   12      1       2 12147  55.00   F   15      1       2 12148  80.00   F   23      1       3 12149  19.00   F   19      1       2 12150  35.00   M   20      1       2 12151  23.00   M   14      1       2 12152  35.00   F   23      1       2 12153  22.00   M   14      1       2 12154  19.00   M   17      1       2 12155  40.00   M   23      1       2 12156  25.00   M   14      1       2 12157  58.00   M   17      1       2 12158  30.00   F   17      1       2 12159  20.00   M   17      1       2 12160  16.00   F   20      1       1 12161  21.00   F   24      1       2 12162  45.00   M   17      1       2 12163  38.00   F   17      1       2 12164  20.00   F   24      1       2 12165  23.00   M   17      1       2 12166  20.00   M   24      1       2 12167  10.00   M   16      1       1 12168  20.00   M   16      1       2 12169  18.00   F   16      1       2 12170  27.00   F   20      1       2 12171  18.00   F   16      1       2 12172   6.00   M   19      1       1 12173   9.00   M   16      1       1 12174  36.00   M   16      1       2 12175  32.00   F   15      1       2 12176   3.00   F   20      1       1 12177  13.00   F   15      1       1 12178   8.00   M   15      1       1 12179  38.00   F   20      1       2 12180  34.00   F   17      1       2 12181  25.00   F   16      1       2 12182  18.00   F   16      1       2 12183  11.00   F   16      1       1 12184  39.00   F   16      1       2 12185  33.00   M   16      1       2 12186  75.00   F   21      1       3 12187  12.00   F   16      1       1 12188  83.00   M   16      1       3 12189   0.40   F   21      1       1 12190   3.00   F   16      1       1 12191  60.00   M   16      1       3 12192  22.00   F   18      1       2 12193  64.00   M   16      1       3 12194  64.00   M   16      1       3 12195  64.00   M   23      1       3 12196  20.00   F   21      1       2 12197  45.00   M   16      1       2 12198  24.00   M   19      1       2 12199  24.00   M   14      1       2 12200  26.00   M   14      1       2 12201  36.00   M   16      1       2 12202  60.00   M   21      1       3 12203  55.00   F   16      1       2 12204  20.00   F   25      1       2 12205  40.00   F   16      1       2 12206  45.00   M   24      1       2 12207  20.00   F   25      1       2 12208  40.00   M   22      1       2 12209  24.00   M   21      1       2 12210  37.00   F   22      1       2 12211  11.00   F   13      1       1 12212  30.00   M   18      1       2 12213  12.00   F   18      1       1 12214  30.00   F   18      1       2 12215  46.00   M   16      1       2 12216  19.00   F   16      1       2 12217  31.00   F   22      1       2 12218  48.00   M   19      1       2 12219  30.00   M   41      1       2 12220  60.00   F   41      1       3 12221  70.00   F   41      1       3 12222   2.50   F   15      1       1 12223  65.00   M   15      1       3 12224  50.00   M   15      1       2 12225  68.00   M   10      1       3 12226   1.50   M   15      1       1 12227  17.00   F   17      1       1 12228  23.00   F   15      1       2 12229  34.00   M   15      1       2 12230  26.00   M   15      1       2 12231  17.00   M   22      1       1 12232  71.00   M   23      1       3 12233  32.00   F   15      1       2 12234  40.00   M   20      1       2 12235  27.00   M   24      1       2 12236  38.00   M   24      1       2 12237  29.00   M   37      1       2 12238  50.00   F   14      1       2 12239  15.00   M   14      1       1 12240  16.00   M   14      1       1 12241  42.00   M   14      1       2 12242  21.00   F   16      1       2 12243  31.00   M   16      1       2 12244  60.00   M   14      1       3 12245  22.00   M   39      1       2 12246  35.00   M   19      1       2 12247  30.00   M   19      1       2 12248  72.00   M   13      1       3 12249  65.00   M   17      1       3 12250  65.00   M   17      1       3 12251  18.00   M   22      1       2 12252  56.00   M   19      1       2 12253  43.00   M   19      1       2 12254  25.00   M   17      1       2 12255  27.00   M   19      1       2 12256  20.00   M   17      1       2 12257  33.00   M   16      1       2 12258  50.00   F   28      1       2 12259  29.00   F   40      1       2 12260   2.00   F   16      1       1 12261  38.00   M   22      1       2 12262  17.00   F   22      1       1 12263  60.00   F   16      1       3 12264  10.00   M   28      1       1 12265  14.00   F   22      1       1 12266  40.00   F   16      1       2 12267  63.00   M   16      1       3 12268  20.00   M   16      1       2 12269  56.00   F   33      1       2 12270  29.00   M   16      1       2 12271  60.00   F   22      1       3 12272  25.00   F   16      1       2 12273   3.00   F   22      1       1 12274  46.00   F   22      1       2 12275  50.00   F   22      1       2 12276  30.00   F   33      1       2 12277   8.00   F   22      1       1 12278  12.00   F   16      1       1 12279  22.00   M   36      1       2 12280  75.00   F   16      1       3 12281  12.00   M   16      1       1 12282  14.00   M   16      1       1 12283  27.00   F   22      1       2 12284   8.00   F   16      1       1 12285   3.00   M   22      1       1 12286   6.00   M   16      1       1 12287  22.00   M   14      1       2 12288  35.00   M   15      1       2 12289  38.00   M   14      1       2 12290  33.00   M   13      1       2 12291  30.00   F   17      1       2 12292  56.00   F   12      1       2 12293  26.00   M   16      1       2 12294  20.00   M   23      1       2 12295  28.00   M   14      1       2 12296  20.00   F   14      1       2 12297  50.00   M   19      1       2 12298  27.00   M   26      1       2 12299  55.00   F   16      1       2 12300  26.00   M   34      1       2 12301  30.00   F   18      1       2 12302  30.00   M   15      1       2 12303  46.00   M   18      1       2 12304  55.00   M   14      1       2 12305  14.00   M   17      1       1 12306  33.00   F   20      1       2 12307  38.00   M   20      1       2 12308  28.00   M   11      1       2 12309  16.00   M   14      1       1 12310  14.00   M   14      1       1 12311  33.00   M   14      1       2 12312  21.00   M   14      1       2 12313  19.00   M   14      1       2 12314  19.00   M   14      1       2 12315  34.00   M   14      1       2 12316  26.00   M   16      1       2 12317  50.00   F   16      1       2 12318  55.00   M   21      1       2 12319  31.00   M   21      1       2 12320  25.00   M   21      1       2 12321  70.00   M   21      1       3 12322  26.00   M   21      1       2 12323  18.00   M   21      1       2 12324  19.00   M   21      1       2 12325  20.00   M   21      1       2 12326  27.00   M   21      1       2 12327  33.00   F   18      1       2 12328  38.00   F   18      1       2 12329  38.00   F   19      1       2 12330  23.00   M   18      1       2 12331  30.00   M   18      1       2 12332  30.00   M   14      1       2 12333  17.00   M   14      1       1 12334  32.00   M   14      1       2 12335  20.00   M   14      1       2 12336  18.00   M   14      1       2 12337  29.00   M   18      1       2 12338  20.00   M   14      1       2 12339  36.00   M   22      1       2 12340  27.00   F   22      1       2 12341   7.00   F   28      1       1 12342  45.00   M   17      1       2 12343   4.00   F   28      1       1 12344  36.00   M   22      1       2 12345  22.00   F   18      1       2 12346  70.00   F   18      1       3 12347  22.00   M   18      1       2 12348  27.00   M   18      1       2 12349  21.00   M   18      1       2 12350  35.00   M   18      1       2 12351  61.00   M   14      1       3 12352  65.00   M   14      1       3 12353  17.00   F   18      1       1 12354  40.00   M   18      1       2 12355  20.00   M   50      1       2 12356  23.00   M   15      1       2 12357  45.00   M   13      1       2 12358  37.00   F   14      1       2 12359   2.00   F   13      1       1 12360  18.00   F   20      1       2 12361  16.00   F   24      1       1 12362  30.00   M   15      1       2 12363  18.00   M   15      1       2 12364  26.00   M   13      1       2 12365  38.00   F   15      1       2 12366  38.00   F   21      1       2 12367  17.00   M   23      1       1 12368  14.00   F   24      1       1 12369  43.00   M   27      1       2 12370  42.00   F   16      1       2 12371  30.00   F   12      1       2 12372  19.00   M   16      1       2 12373  28.00   F   16      1       2 12374  15.00   F   16      1       1 12375  35.00   F   16      1       2 12376  24.00   M   18      1       2 12377  40.00   M   15      1       2 12378  48.00   F   15      1       2 12379  38.00   M   17      1       2 12380   6.00   M   17      1       1 12381  26.00   M   15      1       2 12382   8.00   M   16      1       1 12383  10.00   M   17      1       1 12384  45.00   M   17      1       2 12385  40.00   F   16      1       2 12386  10.00   M   16      1       1 12387  47.00   M   20      1       2 12388  44.00   M   24      1       2 12389  28.00   M   14      1       2 12390  27.00   F   42      1       2 12391  23.00   M   20      1       2 12392  25.00   M   23      1       2 12393  37.00   M   11      1       2 12394  80.00   F   13      1       3 12395  28.00   M   15      1       2 12396  18.00   M   23      1       2 12397  32.00   F   15      1       2 12398  24.00   M   22      1       2 12399   3.00   F   16      1       1 12400  39.00   M   16      1       2 12401  42.00   M   17      1       2 12402  24.00   F   21      1       2 12403   7.00   M   19      1       1 12404  33.00   M   19      1       2 12405  21.00   F   25      1       2 12406  69.00   F   17      1       3 12407   6.00   M   13      1       1 12408  45.00   M   13      1       2 12409  33.00   F   13      1       2 12410  27.00   F   13      1       2 12411  81.00   M   39      1       3 12412  27.00   M   13      1       2 12413  76.00   M   35      1       3 12414  68.00   F   15      1       3 12415  37.00   M   13      1       2 12416  38.00   M   13      1       2 12417  65.00   M   13      1       3 12418  39.00   M   17      1       2 12419  40.00   M   13      1       2 12420  71.00   M   17      1       3 12421  40.00   M   17      1       2 12422  21.00   M   15      1       2 12423  21.00   M   15      1       2 12424  22.00   M   16      1       2 12425  28.00   M   15      1       2 12426  15.00   M   16      1       1 12427  27.00   M   15      1       2 12428  27.00   M   15      1       2 12429  33.00   M   16      1       2 12430  28.00   M   32      1       2 12431  30.00   M   16      1       2 12432  21.00   M   15      1       2 12433  52.00   M   16      1       2 12434  31.00   M   16      1       2 12435  33.00   F   16      1       2 12436  38.00   F   16      1       2 12437  37.00   M   16      1       2 12438  30.00   M   21      1       2 12439  18.00   M   19      1       2 12440   8.00   M   19      1       1 12441  30.00   F   14      1       2 12442  30.00   F   14      1       2 12443  80.00   M   12      1       3 12444  24.00   M   15      1       2 12445  36.00   M   17      1       2 12446  34.00   M   32      1       2 12447  33.00   M   16      1       2 12448  40.00   F   16      1       2 12449  50.00   M   16      1       2 12450  22.00   M   21      1       2 12451  28.00   M   21      1       2 12452  39.00   F   22      1       2 12453   9.00   F   24      1       1 12454  42.00   M   16      1       2 12455  15.00   M   16      1       1 12456  46.00   M   16      1       2 12457   6.00   F   18      1       1 12458  28.00   M   16      1       2 12459  40.00   M   23      1       2 12460  50.00   M   20      1       2 12461   1.00   F   15      1       1 12462  34.00   M   23      1       2 12463  65.00   F   14      1       3 12464  46.00   M   17      1       2 12465  33.00   M   14      1       2 12466  27.00   M   15      1       2 12467  25.00   M   14      1       2 12468  20.00   M    9      1       2 12469  50.00   M   15      1       2 12470  20.00   M   15      1       2 12471  27.00   M   15      1       2 12472  24.00   M   18      1       2 12473  23.00   M   15      1       2 12474  24.00   M   15      1       2 12475  26.00   M   15      1       2 12476  33.00   M   16      1       2 12477  17.00   M   15      1       1 12478  18.00   M   31      1       2 12479  19.00   M   15      1       2 12480  63.00   M   20      1       3 12481  21.00   F   18      1       2 12482  33.00   F    9      1       2 12483  15.00   F    9      1       1 12484  14.00   F    9      1       1 12485  55.00   M    9      1       2 12486  30.00   F   14      1       2 12487  50.00   F    9      1       2 12488  60.00   M    9      1       3 12489  10.00   F    9      1       1 12490  38.00   F   15      1       2 12491  42.00   M   15      1       2 12492   4.00   F   15      1       1 12493   4.00   F   12      1       1 12494  18.00   M   20      1       2 12495  31.00   M   13      1       2 12496  35.00   F   17      1       2 12497  27.00   M   20      1       2 12498  48.00   M   14      1       2 12499  33.00   F   14      1       2 12500   9.00   F   14      1       1 12501  11.00   F   14      1       1 12502   8.00   F   14      1       1 12503  28.00   F   14      1       2 12504   8.00   M   14      1       1 12505  58.00   M   14      1       2 12506  48.00   F   16      1       2 12507  32.00   M   14      1       2 12508  75.00   F   16      1       3 12509  44.00   M   14      1       2 12510  52.00   M   14      1       2 12511  35.00   F   14      1       2 12512  65.00   F   14      1       3 12513  60.00   F   14      1       3 12514  28.00   F   14      1       2 12515  32.00   F   14      1       2 12516  39.00   M   14      1       2 12517   1.00   M   14      1       1 12518  37.00   F   12      1       2 12519  33.00   M   19      1       2 12520  22.00   F   19      1       2 12521   3.00   F   19      1       1 12522  35.00   F   13      1       2 12523  39.00   M   16      1       2 12524  16.00   M   16      1       1 12525  53.00   M   14      1       2 12526  24.00   M   14      1       2 12527  51.00   M   14      1       2 12528  49.00   M   23      1       2 12529  35.00   F   22      1       2 12530  55.00   M   13      1       2 12531  10.00   M   13      1       1 12532  55.00   M   13      1       2 12533  36.00   M   13      1       2 12534  50.00   F   13      1       2 12535  13.00   M   13      1       1 12536   7.00   M   25      1       1 12537  40.00   M   13      1       2 12538  55.00   M   13      1       2 12539  30.00   M   11      1       2 12540  22.00   M   11      1       2 12541  34.00   M   11      1       2 12542  28.00   F   16      1       2 12543  25.00   M   16      1       2 12544  28.00   M   19      1       2 12545  24.00   M   12      1       2 12546  43.00   M   16      1       2 12547  68.00   M    9      1       3 12548  17.00   M   18      1       1 12549  40.00   F   11      1       2 12550  34.00   M   11      1       2 12551  29.00   F   11      1       2 12552  36.00   M   14      1       2 12553  37.00   F   14      1       2 12554  25.00   F   14      1       2 12555  32.00   F   14      1       2 12556  20.00   M   14      1       2 12557  44.00   M   14      1       2 12558  26.00   M   13      1       2 12559  26.00   M   16      1       2 12560  23.00   M   13      1       2 12561  28.00   M   13      1       2 12562  24.00   M   16      1       2 12563  25.00   M   13      1       2 12564  23.00   M   13      1       2 12565  27.00   M   13      1       2 12566  19.00   M   13      1       2 12567  25.00   M   13      1       2 12568  21.00   M   13      1       2 12569  32.00   M   13      1       2 12570  30.00   M   13      1       2 12571  19.00   M   13      1       2 12572  21.00   M   13      1       2 12573  24.00   M   14      1       2 12574  20.00   F   12      1       2 12575  25.00   M   12      1       2 12576  19.00   F   15      1       2 12577  45.00   M   24      1       2 12578  30.00   M   10      1       2 12579  33.00   M   20      1       2 12580  58.00   M   10      1       2 12581  32.00   M   10      1       2 12582  12.00   M   10      1       1 12583  10.00   M   19      1       1 12584  20.00   F   30      1       2 12585  24.00   M   10      1       2 12586  24.00   F   19      1       2 12587  25.00   F   10      1       2 12588  25.00   M   10      1       2 12589  30.00   M   10      1       2 12590  15.00   M   10      1       1 12591  30.00   M   10      1       2 12592  22.00   F   12      1       2 12593  24.00   F   12      1       2 12594  29.00   M   12      1       2 12595  27.00   F   12      1       2 12596   0.50   F   12      1       1 12597  27.00   M   15      1       2 12598  35.00   F   15      1       2 12599  28.00   F   15      1       2 12600  40.00   M   15      1       2 12601   8.00   M   19      1       1 12602  24.00   M   19      1       2 12603  10.00   F   19      1       1 12604   2.00   F   11      1       1 12605  20.00   M   13      1       2 12606  43.00   M   20      1       2 12607  14.00   M   13      1       1 12608  16.00   M   15      1       1 12609  22.00   M   29      1       2 12610  25.00   F   15      1       2 12611  28.00   M   15      1       2 12612  50.00   F   15      1       2 12613  10.00   M   15      1       1 12614  42.00   M   11      1       2 12615  31.00   M   15      1       2 12616  24.00   F   15      1       2 12617  35.00   M   15      1       2 12618  26.00   M   15      1       2 12619  33.00   M   15      1       2 12620   4.00   M   15      1       1 12621  40.00   F   23      1       2 12622  45.00   F   37      1       2 12623  46.00   M   13      1       2 12624  23.00   M   15      1       2 12625  23.00   F   15      1       2 12626   8.00   M   21      1       1 12627  30.00   M   15      1       2 12628   8.00   F   15      1       1 12629   3.00   F   15      1       1 12630  37.00   M   15      1       2 12631  30.00   M   32      1       2 12632  55.00   F   32      1       2 12633  28.00   F   13      1       2 12634  39.00   M   13      1       2 12635  32.00   M   13      1       2 12636  11.00   M   13      1       1 12637  30.00   F   13      1       2 12638   8.00   F   13      1       1 12639   4.00   F   12      1       1 12640   6.00   F   30      1       1 12641   4.00   M   12      1       1 12642   5.00   F   12      1       1 12643  25.00   F   12      1       2 12644  35.00   F   10      1       2 12645  27.00   M    9      1       2 12646  58.00   F    9      1       2 12647  22.00   F   10      1       2 12648  48.00   M   11      1       2 12649  14.00   M   11      1       1 12650   5.00   M   11      1       1 12651  30.00   M   11      1       2 12652  31.00   M   11      1       2 12653   2.00   M    9      1       1 12654   7.00   M   29      1       1 12655  42.00   M   11      1       2 12656  18.00   F   11      1       2 12657   8.00   F   11      1       1 12658  35.00   F   11      1       2 12659  32.00   M   11      1       2 12660  21.00   F   11      1       2 12661  40.00   M   11      1       2 12662  30.00   F   11      1       2 12663  30.00   F   11      1       2 12664  32.00   M   11      1       2 12665  54.00   F   11      1       2 12666  78.00   M   12      1       3 12667   1.00   M   16      1       1 12668  16.00   F   16      1       1 12669  24.00   F   16      1       2 12670  60.00   F   16      1       3 12671  40.00   F   16      1       2 12672  17.00   M   16      1       1 12673  20.00   F   16      1       2 12674  50.00   M   16      1       2 12675  52.00   F   16      1       2 12676   9.00   M   16      1       1 12677  33.00   F   16      1       2 12678  18.00   M   16      1       2 12679  65.00   M   19      1       3 12680  34.00   M   16      1       2 12681  25.00   M   16      1       2 12682  24.00   M   12      1       2 12683  24.00   F   12      1       2 12684   8.00   M   12      1       1 12685  38.00   M   12      1       2 12686  23.00   F   10      1       2 12687  36.00   F   15      1       2 12688  25.00   F   15      1       2 12689  43.00   M    8      1       2 12690  27.00   F   10      1       2 12691  34.00   F   12      1       2 12692  13.00   M   12      1       1 12693  20.00   M   12      1       2 12694  29.00   M   12      1       2 12695  39.00   F   12      1       2 12696  12.00   M   15      1       1 12697  15.00   F   12      1       1 12698  63.00   M   12      1       3 12699  20.00   F   13      1       2 12700  49.00   F   19      1       2 12701  28.00   M   12      1       2 12702  40.00   F   11      1       2 12703  17.00   F   17      1       1 12704  13.00   M   18      1       1 12705  38.00   F   17      1       2 12706  24.00   M   18      1       2 12707  16.00   F   18      1       1 12708  38.00   M   16      1       2 12709  30.00   F   16      1       2 12710  20.00   M   16      1       2 12711  45.00   M   16      1       2 12712  12.00   M   16      1       1 12713  39.00   F   19      1       2 12714  18.00   F   16      1       2 12715  19.00   F   16      1       2 12716   1.00   F   16      1       1 12717  50.00   F   16      1       2 12718  52.00   M   19      1       2 12719  45.00   F   16      1       2 12720  52.00   M   16      1       2 12721  44.00   M   16      1       2 12722  12.00   F   16      1       1 12723  32.00   F   19      1       2 12724  12.00   F   19      1       1 12725  34.00   M   16      1       2 12726  10.00   M   16      1       1 12727  28.00   F   16      1       2 12728  41.00   M   19      1       2 12729  10.00   M   16      1       1 12730  35.00   F   16      1       2 12731  47.00   F   19      1       2 12732  40.00   M   19      1       2 12733  16.00   F   16      1       1 12734  34.00   F   19      1       2 12735  37.00   M   28      1       2 12736  32.00   M   16      1       2 12737   8.00   F   19      1       1 12738  40.00   M   19      1       2 12739  38.00   M   16      1       2 12740  31.00   F   16      1       2 12741  37.00   M   16      1       2 12742  40.00   F   19      1       2 12743  33.00   F   16      1       2 12744  15.00   F   43      1       1 12745  47.00   M   16      1       2 12746  35.00   M   16      1       2 12747  54.00   M   31      1       2 12748  46.00   M   19      1       2 12749  33.00   F   16      1       2 12750   5.00   M   20      1       1 12751  26.00   M   16      1       2 12752  15.00   M   16      1       1 12753  27.00   F   16      1       2 12754  35.00   M   16      1       2 12755  31.00   F   18      1       2 12756  34.00   F   18      1       2 12757  19.00   M   11      1       2 12758  18.00   M   11      1       2 12759  25.00   M   11      1       2 12760  30.00   F   11      1       2 12761  20.00   M   11      1       2 12762  11.00   M   11      1       1 12763  13.00   F   11      1       1 12764  35.00   F   11      1       2 12765  70.00   M   11      1       3 12766  29.00   F   13      1       2 12767   6.00   F   11      1       1 12768  35.00   F   11      1       2 12769  69.00   M   10      1       3 12770  36.00   F   16      1       2 12771  30.00   M   23      1       2 12772  26.00   M   16      1       2 12773  13.00   F   16      1       1 12774  48.00   M   16      1       2 12775  20.00   M   16      1       2 12776  17.00   F   19      1       1 12777  37.00   F   16      1       2 12778  21.00   M   16      1       2 12779   7.00   M   12      1       1 12780  17.00   F   16      1       1 12781  10.00   M   13      1       1 12782  38.00   M   15      1       2 12783  11.00   F   18      1       1 12784  40.00   M   18      1       2 12785  23.00   M   18      1       2 12786  43.00   M   19      1       2 12787  30.00   M   11      1       2 12788  28.00   M   11      1       2 12789  30.00   M    9      1       2 12790  50.00   M   25      1       2 12791  36.00   F   16      1       2 12792  28.00   F   17      1       2 12793  30.00   F   19      1       2 12794  10.00   M   17      1       1 12795  12.00   F   17      1       1 12796  31.00   F   11      1       2 12797  40.00   M   11      1       2 12798  10.00   F   16      1       1 12799  39.00   M   16      1       2 12800  14.00   F   16      1       1 12801  44.00   M   19      1       2 12802  66.00   F   16      1       3 12803  27.00   F   21      1       2 12804  46.00   M   17      1       2 12805  35.00   F    9      1       2 12806  42.00   M    9      1       2 12807  36.00   M   14      1       2 12808  24.00   F   14      1       2 12809  22.00   M    9      1       2 12810  64.00   M   14      1       3 12811  40.00   M   20      1       2 12812  45.00   F   16      1       2 12813  31.00   F   16      1       2 12814  22.00   M   10      1       2 12815  35.00   M   10      1       2 12816  25.00   M   10      1       2 12817  22.00   F   10      1       2 12818  46.00   M   10      1       2 12819  26.00   M   10      1       2 12820  50.00   M   10      1       2 12821  24.00   F   12      1       2 12822  38.00   F   20      1       2 12823  55.00   M   11      1       2 12824   4.00   F   11      1       1 12825  15.00   F   11      1       1 12826  74.00   M   19      1       3 12827  31.00   F   11      1       2 12828  40.00   F   14      1       2 12829  13.00   M    9      1       1 12830  37.00   M   16      1       2 12831  38.00   M    9      1       2 12832  16.00   M   16      1       1 12833  13.00   M    9      1       1 12834  35.00   F    9      1       2 12835  28.00   F   16      1       2 12836  43.00   M   19      1       2 12837   7.00   F   17      1       1 12838  55.00   F   12      1       2 12839  47.00   F   11      1       2 12840   6.00   F   16      1       1 12841  12.00   F   12      1       1 12842  18.00   M   12      1       2 12843  56.00   M   12      1       2 12844  15.00   M   21      1       1 12845  45.00   M   12      1       2 12846  72.00   M   12      1       3 12847   9.00   F   29      1       1 12848  40.00   F   12      1       2 12849  26.00   F   24      1       2 12850  40.00   F   32      1       2 12851  12.00   F   30      1       1 12852  30.00   F   30      1       2 12853  40.00   F   10      1       2 12854  11.00   M   13      1       1 12855   7.00   F    8      1       1 12856  36.00   M   16      1       2 12857  27.00   M   16      1       2 12858  50.00   F   16      1       2 12859  23.00   M   16      1       2 12860  34.00   M   15      1       2 12861  37.00   M   12      1       2 12862  36.00   F   19      1       2 12863  16.00   M   16      1       1 12864   7.00   F   15      1       1 12865  60.00   M   33      1       3 12866  42.00   M   13      1       2 12867  44.00   M   26      1       2 12868  44.00   M   11      1       2 12869  35.00   M   29      1       2 12870  29.00   M   29      1       2 12871  47.00   M   15      1       2 12872  15.00   M   10      1       1 12873  40.00   F   15      1       2 12874  68.00   F   19      1       3 12875  22.00   M   16      1       2 12876  27.00   M   16      1       2 12877   6.00   F   10      1       1 12878  65.00   F   12      1       3 12879  63.00   F   12      1       3 12880  24.00   M   12      1       2 12881  25.00   M   12      1       2 12882  75.00   M   12      1       3 12883  30.00   M    7      1       2 12884  60.00   M   24      1       3 12885   7.00   F   12      1       1 12886  27.00   M   26      1       2 12887  60.00   F   10      1       3 12888  21.00   F   22      1       2 12889   3.00   M   25      1       1 12890   3.00   F   22      1       1 12891  56.00   M   10      1       2 12892  52.00   M   22      1       2 12893  29.00   F   34      1       2 12894   6.00   F   14      1       1 12895  29.00   F   14      1       2 12896  35.00   M   13      1       2 12897  18.00   M    7      1       2 12898  30.00   M   17      1       2 12899   2.00   M   10      1       1 12900   9.00   M   10      1       1 12901  32.00   F   10      1       2 12902  51.00   M   16      1       2 12903  33.00   M   10      1       2 12904   4.00   F   10      1       1 12905   6.00   M   10      1       1 12906   6.00   M   10      1       1 12907   4.00   M   10      1       1 12908   6.00   M   10      1       1 12909   7.00   F   10      1       1 12910  32.00   M   10      1       2 12911  51.00   F   10      1       2 12912  10.00   M   10      1       1 12913   6.00   M   10      1       1 12914  33.00   M   10      1       2 12915   3.00   F   10      1       1 12916   5.00   F   10      1       1 12917  35.00   F   17      1       2 12918   1.00   M   17      1       1 12919  26.00   F   10      1       2 12920  24.00   M   10      1       2 12921  21.00   M   10      1       2 12922  60.00   F   26      1       3 12923   9.00   M   21      1       1 12924   1.00   F   10      1       1 12925   2.00   M   10      1       1 12926   6.00   F   17      1       1 12927  38.00   F   20      1       2 12928   7.00   F   17      1       1 12929  14.00   M   13      1       1 12930  41.00   M   12      1       2 12931  33.00   M   13      1       2 12932  24.00   M   12      1       2 12933  20.00   F   12      1       2 12934  31.00   F   12      1       2 12935  31.00   F   12      1       2 12936  19.00   F   13      1       2 12937  15.00   F   10      1       1 12938  40.00   M   10      1       2 12939  17.00   F   10      1       1 12940  14.00   M   26      1       1 12941  35.00   F   16      1       2 12942  42.00   M   17      1       2 12943  35.00   F   16      1       2 12944  15.00   M   16      1       1 12945  33.00   F   17      1       2 12946  36.00   M   20      1       2 12947  42.00   M   17      1       2 12948  58.00   M   14      1       2 12949  43.00   F   12      1       2 12950  36.00   F   12      1       2 12951  48.00   F   13      1       2 12952  29.00   F   12      1       2 12953  32.00   M   15      1       2 12954  38.00   M    9      1       2 12955  39.00   M    8      1       2 12956  17.00   M    8      1       1 12957  18.00   F    8      1       2 12958  33.00   M    8      1       2 12959   4.00   M   13      1       1 12960  28.00   F   15      1       2 12961  41.00   M   15      1       2 12962  28.00   M   18      1       2 12963  23.00   M   15      1       2 12964   8.00   M   13      1       1 12965  52.00   M   15      1       2 12966  33.00   M   23      1       2 12967  12.00   M   11      1       1 12968  14.00   M   29      1       1 12969  32.00   F   35      1       2 12970  13.00   F   11      1       1 12971  47.00   F   16      1       2 12972  28.00   M   16      1       2 12973  32.00   M   20      1       2 12974  42.00   M   12      1       2 12975  37.00   F   19      1       2 12976  42.00   M   16      1       2 12977  32.00   F   16      1       2 12978  13.00   M   16      1       1 12979  11.00   M   16      1       1 12980  21.00   F   16      1       2 12981  56.00   F   16      1       2 12982  52.00   M   16      1       2 12983  16.00   M   16      1       1 12984  47.00   M   15      1       2 12985  35.00   M   29      1       2 12986  32.00   F   16      1       2 12987  12.00   M   16      1       1 12988  10.00   F   16      1       1 12989  29.00   M   19      1       2 12990  41.00   M   16      1       2 12991  36.00   M   11      1       2 12992  37.00   M   18      1       2 12993  22.00   F    9      1       2 12994  35.00   F    9      1       2 12995  45.00   F    9      1       2 12996  22.00   M   11      1       2 12997  23.00   M   11      1       2 12998  29.00   F   15      1       2 12999  39.00   M   17      1       2 13000  10.00   M   15      1       1 13001  21.00   M   17      1       2 13002   8.00   M   15      1       1 13003  17.00   F   20      1       1 13004  46.00   F   15      1       2 13005  45.00   M   18      1       2 13006  45.00   M    9      1       2 13007  14.00   M   19      1       1 13008   3.00   M   17      1       1 13009  14.00   M   17      1       1 13010  60.00   M   13      1       3 13011  50.00   M   12      1       2 13012   7.00   M    9      1       1 13013  49.00   M   10      1       2 13014  46.00   M   10      1       2 13015  48.00   F   10      1       2 13016  14.00   F   13      1       1 13017  25.00   M   14      1       2 13018  12.00   M   15      1       1 13019  32.00   F    9      1       2 13020  33.00   M   18      1       2 13021  32.00   M   14      1       2 13022  23.00   M   25      1       2 13023  12.00   M    9      1       1 13024  35.00   F   12      1       2 13025  35.00   M   15      1       2 13026  35.00   M    9      1       2 13027  25.00   F   18      1       2 13028  51.00   M    9      1       2 13029  17.00   F   12      1       1 13030  17.00   M   18      1       1 13031  35.00   M   25      1       2 13032  18.00   M   15      1       2 13033   1.00   F   21      1       1 13034  35.00   F   12      1       2 13035  53.00   F    9      1       2 13036  35.00   F   12      1       2 13037  14.00   F    9      1       1 13038  45.00   M   15      1       2 13039  31.00   F   20      1       2 13040  17.00   F    9      1       1 13041  55.00   M   20      1       2 13042  20.00   M   12      1       2 13043  42.00   F    9      1       2 13044  14.00   F    9      1       1 13045  18.00   M    9      1       2 13046  36.00   F   14      1       2 13047  34.00   M   14      1       2 13048  33.00   M   11      1       2 13049  60.00   M   18      1       3 13050  65.00   M   19      1       3 13051  23.00   F   18      1       2 13052   2.00   M   15      1       1 13053  32.00   M   10      1       2 13054  28.00   M   10      1       2 13055  42.00   M    9      1       2 13056  52.00   M    9      1       2 13057  34.00   M   21      1       2 13058  30.00   M   18      1       2 13059  22.00   F    9      1       2 13060  34.00   F   25      1       2 13061  15.00   M   15      1       1 13062  32.00   M   25      1       2 13063   7.00   M   15      1       1 13064  18.00   M    9      1       2 13065  34.00   F   18      1       2 13066  22.00   F   18      1       2 13067  15.00   M   15      1       1 13068  19.00   F   12      1       2 13069  66.00   M   15      1       3 13070  75.00   M   10      1       3 13071  24.00   F    9      1       2 13072  55.00   F   11      1       2 13073  28.00   F   11      1       2 13074  22.00   M   11      1       2 13075  36.00   M    9      1       2 13076  44.00   M   16      1       2 13077  29.00   F   28      1       2 13078  42.00   F   12      1       2 13079  33.00   F   34      1       2 13080  29.00   F   14      1       2 13081  35.00   M   14      1       2 13082  40.00   F    7      1       2 13083  45.00   M    7      1       2 13084  30.00   M    7      1       2 13085  17.00   M    7      1       1 13086  40.00   F   14      1       2 13087  47.00   M    7      1       2 13088  45.00   M   18      1       2 13089  40.00   M   14      1       2 13090  32.00   F   14      1       2 13091  38.00   M   14      1       2 13092  40.00   M   14      1       2 13093  33.00   F   20      1       2 13094  25.00   F   15      1       2 13095  27.00   F   10      1       2 13096   4.00   M   10      1       1 13097  31.00   M   10      1       2 13098  20.00   M   28      1       2 13099  25.00   F   32      1       2 13100   1.00   M   32      1       1 13101  13.00   F   16      1       1 13102  17.00   F   32      1       1 13103  45.00   M   10      1       2 13104  35.00   F   10      1       2 13105  60.00   F   18      1       3 13106  20.00   F   12      1       2 13107  30.00   M   20      1       2 13108  15.00   F   20      1       1 13109  36.00   F   15      1       2 13110  29.00   F    6      1       2 13111  20.00   M    6      1       2 13112  39.00   M   10      1       2 13113  18.00   M   10      1       2 13114   5.00   F   10      1       1 13115  42.00   F    9      1       2 13116  46.00   M   17      1       2 13117  50.00   M   17      1       2 13118  49.00   F   17      1       2 13119  20.00   F   17      1       2 13120  14.00   F   25      1       1 13121  31.00   M   19      1       2 13122  55.00   M    9      1       2 13123  48.00   F   17      1       2 13124   9.00   M   10      1       1 13125  25.00   M   15      1       2 13126  17.00   M    8      1       1 13127   7.00   F   15      1       1 13128  20.00   M   15      1       2 13129  50.00   F   18      1       2 13130  32.00   F   14      1       2 13131   8.00   M   14      1       1 13132  10.00   F   14      1       1 13133  28.00   F   14      1       2 13134  11.00   M   14      1       1 13135  30.00   F   14      1       2 13136   6.00   F    9      1       1 13137   1.00   F   28      1       1 13138  30.00   F    9      1       2 13139  30.00   M   16      1       2 13140  23.00   M   16      1       2 13141  30.00   M   16      1       2 13142   8.00   F    9      1       1 13143  17.00   F   15      1       1 13144  12.00   M    8      1       1 13145  38.00   M   11      1       2 13146  53.00   M   17      1       2 13147  34.00   M   17      1       2 13148  49.00   F   11      1       2 13149  26.00   M    7      1       2 13150  14.00   M    9      1       1 13151  38.00   M   31      1       2 13152  22.00   F   21      1       2 13153   8.00   F   33      1       1 13154  40.00   M    9      1       2 13155  55.00   M    9      1       2 13156  29.00   M    9      1       2 13157  40.00   M    9      1       2 13158  35.00   M    9      1       2 13159  54.00   F   24      1       2 13160  29.00   M    9      1       2 13161  65.00   F    9      1       3 13162  35.00   F    9      1       2 13163   7.00   M    6      1       1 13164  45.00   F    6      1       2 13165  54.00   M    6      1       2 13166  33.00   F    6      1       2 13167   0.30   M   24      1       1 13168  15.00   M   11      1       1 13169  16.00   M    8      1       1 13170  31.00   F   15      1       2 13171  41.00   M   15      1       2 13172  39.00   M   15      1       2 13173  39.00   F   15      1       2 13174  28.00   M   15      1       2 13175  15.00   F   15      1       1 13176  35.00   F   19      1       2 13177  11.00   M   15      1       1 13178  11.00   M   15      1       1 13179   6.00   M   15      1       1 13180  33.00   F   15      1       2 13181  17.00   F   15      1       1 13182  42.00   M    8      1       2 13183  69.00   F    8      1       3 13184  41.00   F    9      1       2 13185  30.00   M   14      1       2 13186  60.00   F   14      1       3 13187  13.00   M   10      1       1 13188  27.00   F    9      1       2 13189  34.00   M    9      1       2 13190  35.00   F   33      1       2 13191  13.00   F   24      1       1 13192  30.00   M    9      1       2 13193   9.00   M    9      1       1 13194  46.00   M    9      1       2 13195  45.00   M   27      1       2 13196   1.00   F    9      1       1 13197  48.00   M    9      1       2 13198  35.00   F    9      1       2 13199  33.00   F   27      1       2 13200  10.00   M   21      1       1 13201  70.00   F    9      1       3 13202   8.00   F    9      1       1 13203   6.00   F   24      1       1 13204   3.00   F    9      1       1 13205  30.00   M   33      1       2 13206  22.00   F   33      1       2 13207  24.00   M   15      1       2 13208  36.00   M   21      1       2 13209   2.00   F    9      1       1 13210  33.00   F   16      1       2 13211  46.00   M    9      1       2 13212  52.00   F    9      1       2 13213   4.00   M   14      1       1 13214   5.00   F    8      1       1 13215  35.00   M   34      1       2 13216  34.00   M   11      1       2 13217   7.00   M   19      1       1 13218  20.00   M   19      1       2 13219  27.00   M   17      1       2 13220  21.00   M    8      1       2 13221  27.00   M   11      1       2 13222  30.00   M   23      1       2 13223  40.00   M   14      1       2 13224  30.00   F    8      1       2 13225  45.00   M    8      1       2 13226  20.00   F   35      1       2 13227  35.00   F   27      1       2 13228   2.00   M    9      1       1 13229  45.00   M   33      1       2 13230  40.00   F   16      1       2 13231  25.00   M   16      1       2 13232  18.00   F   27      1       2 13233  18.00   F   16      1       2 13234  18.00   M   16      1       2 13235  17.00   F   29      1       1 13236  14.00   M   14      1       1 13237  40.00   F   33      1       2 13238  23.00   M   22      1       2 13239  48.00   F   14      1       2 13240  25.00   M   14      1       2 13241  31.00   M   14      1       2 13242  27.00   M   14      1       2 13243   7.00   F   14      1       1 13244  28.00   M   14      1       2 13245  26.00   F   14      1       2 13246  47.00   M   14      1       2 13247   2.00   F    9      1       1 13248  30.00   M   35      1       2 13249  24.00   M   18      1       2 13250  21.00   F   20      1       2 13251  28.00   M   16      1       2 13252  30.00   F   16      1       2 13253   8.00   F    9      1       1 13254   6.00   F    9      1       1 13255  23.00   M   35      1       2 13256  20.00   M   16      1       2 13257  21.00   F   16      1       2 13258  10.00   F    9      1       1 13259  24.00   M   27      1       2 13260  19.00   M   16      1       2 13261  42.00   M   16      1       2 13262  17.00   M   16      1       1 13263  15.00   M    9      1       1 13264  26.00   M   30      1       2 13265  18.00   M   16      1       2 13266  20.00   F   16      1       2 13267  52.00   M   20      1       2 13268  48.00   F   16      1       2 13269  22.00   F   16      1       2 13270  21.00   M   16      1       2 13271  35.00   F   16      1       2 13272  17.00   M   16      1       1 13273  55.00   M   14      1       2 13274  48.00   F    9      1       2 13275  32.00   M   16      1       2 13276   9.00   F    9      1       1 13277   7.00   M    9      1       1 13278  38.00   M   37      1       2 13279  31.00   M   16      1       2 13280  21.00   F   33      1       2 13281  32.00   F   18      1       2 13282  17.00   F   14      1       1 13283  15.00   F    9      1       1 13284  34.00   F   15      1       2 13285  23.00   M   15      1       2 13286  51.00   M   16      1       2 13287  20.00   F   15      1       2 13288   2.00   M   15      1       1 13289  45.00   M   15      1       2 13290  30.00   M   15      1       2 13291  58.00   F   15      1       2 13292  15.00   F   15      1       1 13293  45.00   M   15      1       2 13294  37.00   F   15      1       2 13295  13.00   M   15      1       1 13296  43.00   M   15      1       2 13297  20.00   F   15      1       2 13298  36.00   M   15      1       2 13299  16.00   F   19      1       1 13300  33.00   M   15      1       2 13301  23.00   F   15      1       2 13302  35.00   M   15      1       2 13303  57.00   M   22      1       2 13304  34.00   F    8      1       2 13305  50.00   F   13      1       2 13306  20.00   F   13      1       2 13307  22.00   F   13      1       2 13308  18.00   M   13      1       2 13309  18.00   F   13      1       2 13310   8.00   F   13      1       1 13311  21.00   M   13      1       2 13312  17.00   M    9      1       1 13313  40.00   M   21      1       2 13314  49.00   M    8      1       2 13315  29.00   F   14      1       2 13316   2.00   M   14      1       1 13317  49.00   F   18      1       2 13318  31.00   M   10      1       2 13319  11.00   M   10      1       1 13320  50.00   F   21      1       2 13321  23.00   M   10      1       2 13322  59.00   F    9      1       2 13323  25.00   F    9      1       2 13324  35.00   F   11      1       2 13325  35.00   F   11      1       2 13326  18.00   M    8      1       2 13327  28.00   M    8      1       2 13328  55.00   F   15      1       2 13329  32.00   M   10      1       2 13330  35.00   F   17      1       2 13331  60.00   F   10      1       3 13332  33.00   F    7      1       2 13333  33.00   F    7      1       2 13334  32.00   M    6      1       2 13335  20.00   M    6      1       2 13336  48.00   M    6      1       2 13337  50.00   F    6      1       2 13338  22.00   M    9      1       2 13339  50.00   M   15      1       2 13340   4.00   F   13      1       1 13341  17.00   F   19      1       1 13342   8.00   M   27      1       1 13343  25.00   M   17      1       2 13344  27.00   F   13      1       2 13345  43.00   M   18      1       2 13346  42.00   M   23      1       2 13347  15.00   F   16      1       1 13348  45.00   F   16      1       2 13349  37.00   F   13      1       2 13350  40.00   M   13      1       2 13351  14.00   M   16      1       1 13352  35.00   F    7      1       2 13353  11.00   F   16      1       1 13354  50.00   M   16      1       2 13355  70.00   F    7      1       3 13356  27.00   M   15      1       2 13357  24.00   M   13      1       2 13358  52.00   F   12      1       2 13359  24.00   F   12      1       2 13360  38.00   F   12      1       2 13361  38.00   M   10      1       2 13362  23.00   M   13      1       2 13363  45.00   M   13      1       2 13364  30.00   M   13      1       2 13365  24.00   F   20      1       2 13366  26.00   M   31      1       2 13367  31.00   M   20      1       2 13368  42.00   M   23      1       2 13369  21.00   F   16      1       2 13370  24.00   F   16      1       2 13371  42.00   F   16      1       2 13372  40.00   F   16      1       2 13373  22.00   M   26      1       2 13374  40.00   M   16      1       2 13375  18.00   F   16      1       2 13376   6.00   M   16      1       1 13377   6.00   M   16      1       1 13378  39.00   F   26      1       2 13379  32.00   M   16      1       2 13380  44.00   M   23      1       2 13381  38.00   M   23      1       2 13382   7.00   F   16      1       1 13383  20.00   F   14      1       2 13384  24.00   M   14      1       2 13385  42.00   M    8      1       2 13386  34.00   M   13      1       2 13387  35.00   F   10      1       2 13388  55.00   M   36      1       2 13389  16.00   M   15      1       1 13390  18.00   M    5      1       2 13391  23.00   M   13      1       2 13392  58.00   M   10      1       2 13393  26.00   F   18      1       2 13394  32.00   F   23      1       2 13395  22.00   M   13      1       2 13396  33.00   M   13      1       2 13397  20.00   M   13      1       2 13398  36.00   M   16      1       2 13399  32.00   F   13      1       2 13400  51.00   M    7      1       2 13401  41.00   M    7      1       2 13402  32.00   M   17      1       2 13403   4.00   M    7      1       1 13404  50.00   M   17      1       2 13405  22.00   F    5      1       2 13406  37.00   M   17      1       2 13407  26.00   M   17      1       2 13408  24.00   F    7      1       2 13409  35.00   F   11      1       2 13410   1.00   M   11      1       1 13411  29.00   M    7      1       2 13412   4.00   M   11      1       1 13413  29.00   F   21      1       2 13414  48.00   M   17      1       2 13415  23.00   F   11      1       2 13416  29.00   F   11      1       2 13417  30.00   M   24      1       2 13418  44.00   F    7      1       2 13419  54.00   M   13      1       2 13420   1.50   F   13      1       1 13421   7.00   M   13      1       1 13422   9.00   M   13      1       1 13423  25.00   M   13      1       2 13424  21.00   M   13      1       2 13425   2.00   M   15      1       1 13426  45.00   M   15      1       2 13427  26.00   M   21      1       2 13428  23.00   F    7      1       2 13429  22.00   F   17      1       2 13430  25.00   F   17      1       2 13431  21.00   F   13      1       2 13432  21.00   F   19      1       2 13433  70.00   F   13      1       3 13434  37.00   F   19      1       2 13435  21.00   F   19      1       2 13436  18.00   F   13      1       2 13437  31.00   M   13      1       2 13438   3.00   F   13      1       1 13439  14.00   M   13      1       1 13440   9.00   F   13      1       1 13441  26.00   M   19      1       2 13442  61.00   F   13      1       3 13443   1.00   F   13      1       1 13444  31.00   M   21      1       2 13445  21.00   M   21      1       2 13446  31.00   M   10      1       2 13447  74.00   M   22      1       3 13448  25.00   M   19      1       2 13449  30.00   F   13      1       2 13450   9.00   F   10      1       1 13451  34.00   M   16      1       2 13452  27.00   M   20      1       2 13453  54.00   F   20      1       2 13454   3.00   F   23      1       1 13455  30.00   F   23      1       2 13456   5.00   M   10      1       1 13457   4.00   M   10      1       1 13458  65.00   M   25      1       3 13459  10.00   M   25      1       1 13460  43.00   M   12      1       2 13461   6.00   F    6      1       1 13462  72.00   F   10      1       3 13463   9.00   F   14      1       1 13464  33.00   F   10      1       2 13465  11.00   F   16      1       1 13466   6.00   F   14      1       1 13467  67.00   F   16      1       3 13468  30.00   M   31      1       2 13469  25.00   M   10      1       2 13470  55.00   M   25      1       2 13471   3.00   F   12      1       1 13472  33.00   M   18      1       2 13473  25.00   M   18      1       2 13474  17.00   F   18      1       1 13475  22.00   F   18      1       2 13476   5.00   M   18      1       1 13477  40.00   M   25      1       2 13478  20.00   F   23      1       2 13479   3.00   M   33      1       1 13480  22.00   F   35      1       2 13481  25.00   F   18      1       2 13482  30.00   M   18      1       2 13483  22.00   F   12      1       2 13484  22.00   F   23      1       2 13485  20.00   F   22      1       2 13486  22.00   M   18      1       2 13487  48.00   M   12      1       2 13488  53.00   F    7      1       2 13489  25.00   M    7      1       2 13490  47.00   F   31      1       2 13491  21.00   F   16      1       2 13492   2.00   M   12      1       1 13493  34.00   M   10      1       2 13494  28.00   F    6      1       2 13495  60.00   M   12      1       3 13496  14.00   M   15      1       1 13497  42.00   M   12      1       2 13498  18.00   M   12      1       2 13499  30.00   M   12      1       2 13500  10.00   M   18      1       1 13501  55.00   F   12      1       2 13502  45.00   M   12      1       2 13503  18.00   M   12      1       2 13504  40.00   M   12      1       2 13505  15.00   F   12      1       1 13506  26.00   M   18      1       2 13507  29.00   F   18      1       2 13508  68.00   M   18      1       3 13509  35.00   F   12      1       2 13510  29.00   M   12      1       2 13511  40.00   F   19      1       2 13512  48.00   M   12      1       2 13513  23.00   M    9      1       2 13514  33.00   F   12      1       2 13515  17.00   M   12      1       1 13516  60.00   M   20      1       3 13517  20.00   F    6      1       2 13518  11.00   F    6      1       1 13519  58.00   F   24      1       2 13520  46.00   M   16      1       2 13521   1.00   F   16      1       1 13522  33.00   M   20      1       2 13523  38.00   M    6      1       2 13524  30.00   M    4      1       2 13525  65.00   F   14      1       3 13526  45.00   M   12      1       2 13527  26.00   F   14      1       2 13528  40.00   M    6      1       2 13529  46.00   M   23      1       2 13530  44.00   M    6      1       2 13531  57.00   M    5      1       2 13532  50.00   M   13      1       2 13533  48.00   M    5      1       2 13534  36.00   M   23      1       2 13535  21.00   F   12      1       2 13536  33.00   M   16      1       2 13537  47.00   M   17      1       2 13538  28.00   M   11      1       2 13539   9.00   M   11      1       1 13540  20.00   F   19      1       2 13541  25.00   M   19      1       2 13542  22.00   F   19      1       2 13543  25.00   M   25      1       2 13544  34.00   M   19      1       2 13545  15.00   M   19      1       1 13546  25.00   M   19      1       2 13547  37.00   M   37      1       2 13548  27.00   M    6      1       2 13549  30.00   M   21      1       2 13550  30.00   M    6      1       2 13551   6.00   M   19      1       1 13552  25.00   F   19      1       2 13553  19.00   M   19      1       2 13554  18.00   M   19      1       2 13555  45.00   M   20      1       2 13556  18.00   M   11      1       2 13557  34.00   M   24      1       2 13558  30.00   M    7      1       2 13559  60.00   M   13      1       3 13560  17.00   M   11      1       1 13561  47.00   F   11      1       2 13562  55.00   F   11      1       2 13563  28.00   M   15      1       2 13564  58.00   M   11      1       2 13565  43.00   M   11      1       2 13566  23.00   M   11      1       2 13567  42.00   F   11      1       2 13568  34.00   M   11      1       2 13569  43.00   M   14      1       2 13570  37.00   F   11      1       2 13571  16.00   M   14      1       1 13572  53.00   M   13      1       2 13573  20.00   F   14      1       2 13574  52.00   M   14      1       2 13575  14.00   M   14      1       1 13576  35.00   F   19      1       2 13577  40.00   F   14      1       2 13578  10.00   M   14      1       1 13579  52.00   M   14      1       2 13580  17.00   F   19      1       1 13581  45.00   M   16      1       2 13582  35.00   F   20      1       2 13583  27.00   M   11      1       2 13584  30.00   M   23      1       2 13585  30.00   F   21      1       2 13586  21.00   M   17      1       2 13587  33.00   M   11      1       2 13588  25.00   M   11      1       2 13589  46.00   M   17      1       2 13590  17.00   M   11      1       1 13591  23.00   M   17      1       2 13592  25.00   M   17      1       2 13593  31.00   M   17      1       2 13594  26.00   M   17      1       2 13595  30.00   M   11      1       2 13596  36.00   M   17      1       2 13597  20.00   M   17      1       2 13598  48.00   M   17      1       2 13599  27.00   M   11      1       2 13600  15.00   M   17      1       1 13601  30.00   M   17      1       2 13602  17.00   M   11      1       1 13603  20.00   M   11      1       2 13604  22.00   M   11      1       2 13605  31.00   M   17      1       2 13606  27.00   M   14      1       2 13607  35.00   M    9      1       2 13608   9.00   F   11      1       1 13609  65.00   F   11      1       3 13610  35.00   M    8      1       2 13611  27.00   F    8      1       2 13612  32.00   M    8      1       2 13613  56.00   M    8      1       2 13614  39.00   F    8      1       2 13615  43.00   M   17      1       2 13616  38.00   F    8      1       2 13617  38.00   F   17      1       2 13618  27.00   F    8      1       2 13619  39.00   M   17      1       2 13620  17.00   F   30      1       1 13621  38.00   M   17      1       2 13622  28.00   F   17      1       2 13623  54.00   M   11      1       2 13624  55.00   F    5      1       2 13625  38.00   M    5      1       2 13626   9.00   M    8      1       1 13627  36.00   F    5      1       2 13628  14.00   M    5      1       1 13629  63.00   F    8      1       3 13630  39.00   M    8      1       2 13631   9.00   M    5      1       1 13632  26.00   F    8      1       2 13633  12.00   M   16      1       1 13634  12.00   F   11      1       1 13635  35.00   M   23      1       2 13636  36.00   F    5      1       2 13637  46.00   M   13      1       2 13638  11.00   F   23      1       1 13639  59.00   F   16      1       2 13640   3.00   F   23      1       1 13641  26.00   M   11      1       2 13642  30.00   M   16      1       2 13643  36.00   F   14      1       2 13644  27.00   F   10      1       2 13645   6.00   F   10      1       1 13646   4.00   M   10      1       1 13647  13.00   M   16      1       1 13648  25.00   F   14      1       2 13649  39.00   M   14      1       2 13650   2.00   F   10      1       1 13651   3.00   M   10      1       1 13652  33.00   M   10      1       2 13653   1.00   F   10      1       1 13654   7.00   F   10      1       1 13655  26.00   F   16      1       2 13656  50.00   M   31      1       2 13657  45.00   M    8      1       2 13658  31.00   M   14      1       2 13659  25.00   F   10      1       2 13660  12.00   M   13      1       1 13661  31.00   F   13      1       2 13662  28.00   M   13      1       2 13663  35.00   M   10      1       2 13664  43.00   M   10      1       2 13665  34.00   M   13      1       2 13666  28.00   F   10      1       2 13667  25.00   M   10      1       2 13668  21.00   M   14      1       2 13669  20.00   M   10      1       2 13670  25.00   M   17      1       2 13671  30.00   M   29      1       2 13672  20.00   F   23      1       2 13673  60.00   F   29      1       3 13674  12.00   F   12      1       1 13675  37.00   F   29      1       2 13676  45.00   F    8      1       2 13677  22.00   M    8      1       2 13678  39.00   M    8      1       2 13679  45.00   M    7      1       2 13680  27.00   M   36      1       2 13681  57.00   M   10      1       2 13682  60.00   M   18      1       3 13683  25.00   F    7      1       2 13684  32.00   M   20      1       2 13685  40.00   M    7      1       2 13686  22.00   M   10      1       2 13687  49.00   M   10      1       2 13688  55.00   F   21      1       2 13689  21.00   M   14      1       2 13690  29.00   M    7      1       2 13691  32.00   M    8      1       2 13692  38.00   M   10      1       2 13693  17.00   F   10      1       1 13694  13.00   M   10      1       1 13695  18.00   F   10      1       2 13696  50.00   M   10      1       2 13697  40.00   M   10      1       2 13698  35.00   F   10      1       2 13699  17.00   M   10      1       1 13700  10.00   M   10      1       1 13701  34.00   M   16      1       2 13702  34.00   M   10      1       2 13703   7.00   F   10      1       1 13704   4.00   F   10      1       1 13705  41.00   M   10      1       2 13706  20.00   M   10      1       2 13707  36.00   M   10      1       2 13708  23.00   M   10      1       2 13709  16.00   M   10      1       1 13710  22.00   M   10      1       2 13711  27.00   M   10      1       2 13712  11.00   F   10      1       1 13713   7.00   M   10      1       1 13714  26.00   M   21      1       2 13715  40.00   M   10      1       2 13716  36.00   M   22      1       2 13717  23.00   F    7      1       2 13718  28.00   F   10      1       2 13719  22.00   F    7      1       2 13720  42.00   M    7      1       2 13721  19.00   M   10      1       2 13722  60.00   M   13      1       3 13723  50.00   F   12      1       2 13724  10.00   F    7      1       1 13725   9.00   F   10      1       1 13726  35.00   M   10      1       2 13727  39.00   M   13      1       2 13728  48.00   M   17      1       2 13729  38.00   F   18      1       2 13730  18.00   M   13      1       2 13731   2.00   F   11      1       1 13732  32.00   M   11      1       2 13733  28.00   M   11      1       2 13734  37.00   F   11      1       2 13735  22.00   F    7      1       2 13736  28.00   F   37      1       2 13737  41.00   M   21      1       2 13738  31.00   F    4      1       2 13739  27.00   M   14      1       2 13740  30.00   M   14      1       2 13741  32.00   M    4      1       2 13742  17.00   F   12      1       1 13743  34.00   M   29      1       2 13744  25.00   M   14      1       2 13745  24.00   M   10      1       2 13746  52.00   M   11      1       2 13747   9.00   M   11      1       1 13748   4.00   M   14      1       1 13749   9.00   M    8      1       1 13750  35.00   M    8      1       2 13751   3.00   F    7      1       1 13752  32.00   M    8      1       2 13753  31.00   M    8      1       2 13754  68.00   F   16      1       3 13755  30.00   F   10      1       2 13756  23.00   F   15      1       2 13757  40.00   F   10      1       2 13758  25.00   M   15      1       2 13759  16.00   F    7      1       1 13760  48.00   M    7      1       2 13761  21.00   F    7      1       2 13762  46.00   M   10      1       2 13763  20.00   M    8      1       2 13764  73.00   F   15      1       3 13765  46.00   M   14      1       2 13766  26.00   M    6      1       2 13767  32.00   M   14      1       2 13768  23.00   M    7      1       2 13769  30.00   F   11      1       2 13770  39.00   M   22      1       2 13771  61.00   M   19      1       3 13772  62.00   F    7      1       3 13773  27.00   M   28      1       2 13774  34.00   F   11      1       2 13775  28.00   M   11      1       2 13776  48.00   M   13      1       2 13777  32.00   M   21      1       2 13778  34.00   F   29      1       2 13779  29.00   F   11      1       2 13780  39.00   M    3      1       2 13781  32.00   M    3      1       2 13782  34.00   M   11      1       2 13783  34.00   M   11      1       2 13784  41.00   M   13      1       2 13785  12.00   M    6      1       1 13786  25.00   M    9      1       2 13787  46.00   M    9      1       2 13788  18.00   F   12      1       2 13789  30.00   F    6      1       2 13790  42.00   F    9      1       2 13791  45.00   M    6      1       2 13792  32.00   M    6      1       2 13793  30.00   M    6      1       2 13794  32.00   M    6      1       2 13795  44.00   M   16      1       2 13796  45.00   F    6      1       2 13797  40.00   M   12      1       2 13798  37.00   M    8      1       2 13799  15.00   M    9      1       1 13800  36.00   M    9      1       2 13801  19.00   M    9      1       2 13802  21.00   M   15      1       2 13803  24.00   M   15      1       2 13804  25.00   M   15      1       2 13805  48.00   M   11      1       2 13806  19.00   M   13      1       2 13807  43.00   M   11      1       2 13808  59.00   F   21      1       2 13809  30.00   F   11      1       2 13810   6.00   F   11      1       1 13811  33.00   F   11      1       2 13812  34.00   M   23      1       2 13813  50.00   M   13      1       2 13814  24.00   F   11      1       2 13815  23.00   M    8      1       2 13816  38.00   M   14      1       2 13817  44.00   M   14      1       2 13818  30.00   M   28      1       2 13819  46.00   M   20      1       2 13820  50.00   M    4      1       2 13821  25.00   M   10      1       2 13822  28.00   M   15      1       2 13823  24.00   M   20      1       2 13824  28.00   M   15      1       2 13825  24.00   F   15      1       2 13826   8.00   M   23      1       1 13827  45.00   M   25      1       2 13828  11.00   M    9      1       1 13829  36.00   M    9      1       2 13830  18.00   F    6      1       2 13831  31.00   F   14      1       2 13832  36.00   M    8      1       2 13833  35.00   F    2      1       2 13834  11.00   F   17      1       1 13835  30.00   F   11      1       2 13836   9.00   M   20      1       1 13837   2.00   M   13      1       1 13838  34.00   M   15      1       2 13839  28.00   F   13      1       2 13840  22.00   M   13      1       2 13841  43.00   M   18      1       2 13842  40.00   M    6      1       2 13843  31.00   F    9      1       2 13844  36.00   M   12      1       2 13845  25.00   M   17      1       2 13846  35.00   M    6      1       2 13847  25.00   F    6      1       2 13848  23.00   M   12      1       2 13849   2.00   M    7      1       1 13850  42.00   M   17      1       2 13851  46.00   F    9      1       2 13852  60.00   M    6      1       3 13853  47.00   M    9      1       2 13854  36.00   M   17      1       2 13855  65.00   M   17      1       3 13856  29.00   M    9      1       2 13857  30.00   F    9      1       2 13858  48.00   M    9      1       2 13859  47.00   M   13      1       2 13860  42.00   M    7      1       2 13861  25.00   F   11      1       2 13862   4.00   M   11      1       1 13863  34.00   M   12      1       2 13864  22.00   M    9      1       2 13865  17.00   M    9      1       1 13866  32.00   F    9      1       2 13867  59.00   M    9      1       2 13868  30.00   M    9      1       2 13869  31.00   F   13      1       2 13870  20.00   F   15      1       2 13871  51.00   M    9      1       2 13872  20.00   M    9      1       2 13873  10.00   M    9      1       1 13874   6.00   M    9      1       1 13875  49.00   M   10      1       2 13876  63.00   M   10      1       3 13877  38.00   F   19      1       2 13878   7.00   M   19      1       1 13879  39.00   M   13      1       2 13880  34.00   M   13      1       2 13881  36.00   M   19      1       2 13882  39.00   M   10      1       2 13883  80.00   F    8      1       3 13884  27.00   F   13      1       2 13885  41.00   M    8      1       2 13886  28.00   M   26      1       2 13887   6.00   M   10      1       1 13888  36.00   M   10      1       2 13889  10.00   M   12      1       1 13890  50.00   M   18      1       2 13891  35.00   M   34      1       2 13892  55.00   F   27      1       2 13893  22.00   F   29      1       2 13894  15.00   F   24      1       1 13895  10.00   F   31      1       1 13896  13.00   M   12      1       1 13897  30.00   M   14      1       2 13898  65.00   F    6      1       3 13899  68.00   F    6      1       3 13900   8.00   M    6      1       1 13901  68.00   F    6      1       3 13902  26.00   M   11      1       2 13903  23.00   F    7      1       2 13904  28.00   M   16      1       2 13905  28.00   M   20      1       2 13906  35.00   F    6      1       2 13907  33.00   M   19      1       2 13908  20.00   M   10      1       2 13909  25.00   F   10      1       2 13910   8.00   F   10      1       1 13911  23.00   M   10      1       2 13912  10.00   F   10      1       1 13913  20.00   M   10      1       2 13914  34.00   F   10      1       2 13915  36.00   F   14      1       2 13916  33.00   F   14      1       2 13917   6.00   M   10      1       1 13918  42.00   M   10      1       2 13919  32.00   F   10      1       2 13920  12.00   F   10      1       1 13921  35.00   M   10      1       2 13922  38.00   M   12      1       2 13923  23.00   M    7      1       2 13924  27.00   M   18      1       2 13925  20.00   M   22      1       2 13926  18.00   F    9      1       2 13927  54.00   M   21      1       2 13928  34.00   M   21      1       2 13929  13.00   M   27      1       1 13930  29.00   M   22      1       2 13931  32.00   M    9      1       2 13932  29.00   F   22      1       2 13933  20.00   M   21      1       2 13934  21.00   M   22      1       2 13935  20.00   M   22      1       2 13936  16.00   M   22      1       1 13937  41.00   F   21      1       2 13938  11.00   F   22      1       1 13939  12.00   M   22      1       1 13940  35.00   F    9      1       2 13941  24.00   F   21      1       2 13942   9.00   F    9      1       1 13943  49.00   M   25      1       2 13944  37.00   M   22      1       2 13945  54.00   F   23      1       2 13946  44.00   F   22      1       2 13947  46.00   M    9      1       2 13948  42.00   M   29      1       2 13949  40.00   M   18      1       2 13950  16.00   M   22      1       1 13951  35.00   F   23      1       2 13952  48.00   M   23      1       2 13953  34.00   F    9      1       2 13954  16.00   F   22      1       1 13955  48.00   F   22      1       2 13956  19.00   M   22      1       2 13957  32.00   M   18      1       2 13958  30.00   F   15      1       2 13959  35.00   M   22      1       2 13960   7.00   M   23      1       1 13961  11.00   M   22      1       1 13962  19.00   F   18      1       2 13963  18.00   M    9      1       2 13964  45.00   M   22      1       2 13965  22.00   M   22      1       2 13966  30.00   F   22      1       2 13967  16.00   F    9      1       1 13968   8.00   M    9      1       1 13969  13.00   M    9      1       1 13970  23.00   F   27      1       2 13971  17.00   F    9      1       1 13972  32.00   M   22      1       2 13973  34.00   M   22      1       2 13974  15.00   M   22      1       1 13975  38.00   F   22      1       2 13976  18.00   F    9      1       2 13977  22.00   M   22      1       2 13978  13.00   M   22      1       1 13979  18.00   M   22      1       2 13980  45.00   M   21      1       2 13981  15.00   M    9      1       1 13982  38.00   M   22      1       2 13983  26.00   M   35      1       2 13984  17.00   M   20      1       1 13985  32.00   M   24      1       2 13986  36.00   M   36      1       2 13987  49.00   F   35      1       2 13988  68.00   M   13      1       3 13989  31.00   M   35      1       2 13990  28.00   F   20      1       2 13991  25.00   M   14      1       2 13992  32.00   F   29      1       2 13993   3.00   M   29      1       1 13994  27.00   F   14      1       2 13995  26.00   M   14      1       2 13996  35.00   M   14      1       2 13997  38.00   M   14      1       2 13998  30.00   F   14      1       2 13999  27.00   M   14      1       2 14000  18.00   F   14      1       2 14001  17.00   F   14      1       1 14002  17.00   M   14      1       1 14003  44.00   M   14      1       2 14004   8.00   F   14      1       1 14005   5.00   M   24      1       1 14006  14.00   M   14      1       1 14007  53.00   M   14      1       2 14008  27.00   M   14      1       2 14009  25.00   M   16      1       2 14010  17.00   M   14      1       1 14011  27.00   M   14      1       2 14012  54.00   M   22      1       2 14013  24.00   F   12      1       2 14014  35.00   M   14      1       2 14015  12.00   M   12      1       1 14016  35.00   M   14      1       2 14017   8.00   M   29      1       1 14018  24.00   M   14      1       2 14019   3.00   F   12      1       1 14020  20.00   F   22      1       2 14021  40.00   F   14      1       2 14022  38.00   M   14      1       2 14023  24.00   M   14      1       2 14024  29.00   M   14      1       2 14025  50.00   M   14      1       2 14026  40.00   M   12      1       2 14027  10.00   F   12      1       1 14028  40.00   M   14      1       2 14029  12.00   M   12      1       1 14030  50.00   F   14      1       2 14031  48.00   M   14      1       2 14032  33.00   F   14      1       2 14033  35.00   F   14      1       2 14034  22.00   F   14      1       2 14035   2.00   M   12      1       1 14036  22.00   M   22      1       2 14037  24.00   F   14      1       2 14038  34.00   M   14      1       2 14039  25.00   F   14      1       2 14040  28.00   F   14      1       2 14041  37.00   M   14      1       2 14042  14.00   F   29      1       1 14043  30.00   M   14      1       2 14044  65.00   M   21      1       3 14045  26.00   M   10      1       2 14046   3.00   M    7      1       1 14047  30.00   M   16      1       2 14048  28.00   M    7      1       2 14049  50.00   F   10      1       2 14050  15.00   F   10      1       1 14051  13.00   F   10      1       1 14052   7.00   M   10      1       1 14053   6.00   M   10      1       1 14054  35.00   F   12      1       2 14055  20.00   F   10      1       2 14056  20.00   F   12      1       2 14057  25.00   M   10      1       2 14058  26.00   F   12      1       2 14059   2.00   F   12      1       1 14060  35.00   M   10      1       2 14061  37.00   F   12      1       2 14062  22.00   F   10      1       2 14063  32.00   M   12      1       2 14064   8.00   M   12      1       1 14065  20.00   F   10      1       2 14066  25.00   M   12      1       2 14067  23.00   M   10      1       2 14068  16.00   F   10      1       1 14069  48.00   M   10      1       2 14070  18.00   M   10      1       2 14071  13.00   F   10      1       1 14072  29.00   F   12      1       2 14073  21.00   M   12      1       2 14074  22.00   M   12      1       2 14075  55.00   M   10      1       2 14076  46.00   F   10      1       2 14077  17.00   F   10      1       1 14078  16.00   F   10      1       1 14079  30.00   M   10      1       2 14080  23.00   F   10      1       2 14081  38.00   M   19      1       2 14082  42.00   M   10      1       2 14083  38.00   F   19      1       2 14084  30.00   M   10      1       2 14085  26.00   F   10      1       2 14086  10.00   F   10      1       1 14087  20.00   M   10      1       2 14088  27.00   M   10      1       2 14089  22.00   F   10      1       2 14090  31.00   M   10      1       2 14091  35.00   M   12      1       2 14092  20.00   M   14      1       2 14093  60.00   F   12      1       3 14094   6.00   F   10      1       1 14095  48.00   M    8      1       2 14096  28.00   M    8      1       2 14097  38.00   M    8      1       2 14098   3.00   M   16      1       1 14099  23.00   M   40      1       2 14100  31.00   M   11      1       2 14101  41.00   F   17      1       2 14102  30.00   M   10      1       2 14103  22.00   M   10      1       2 14104  38.00   F    8      1       2 14105  47.00   M   15      1       2 14106  22.00   M    8      1       2 14107  20.00   F    8      1       2 14108   5.00   F    8      1       1 14109  27.00   F    6      1       2 14110  24.00   M    8      1       2 14111  25.00   F    8      1       2 14112  35.00   M    8      1       2 14113  34.00   F    4      1       2 14114  32.00   M    7      1       2 14115  30.00   M    9      1       2 14116  41.00   M   26      1       2 14117  25.00   M   26      1       2 14118  40.00   M   11      1       2 14119  37.00   M    9      1       2 14120  47.00   M    9      1       2 14121  36.00   M   11      1       2 14122  15.00   M   15      1       1 14123  40.00   M   27      1       2 14124   2.00   F    9      1       1 14125  47.00   F   19      1       2 14126  48.00   M   34      1       2 14127  29.00   M   13      1       2 14128  20.00   M   13      1       2 14129  45.00   M   13      1       2 14130  40.00   M   13      1       2 14131  43.00   M   13      1       2 14132  19.00   M   13      1       2 14133  11.00   M   13      1       1 14134  39.00   M   13      1       2 14135  32.00   M   13      1       2 14136  30.00   F   20      1       2 14137  27.00   F   23      1       2 14138  66.00   M   13      1       3 14139  17.00   M   28      1       1 14140  30.00   M   13      1       2 14141   7.00   F   13      1       1 14142   3.00   F   13      1       1 14143   2.00   M   13      1       1 14144   1.00   F   13      1       1 14145  19.00   M   28      1       2 14146  16.00   F   13      1       1 14147  24.00   M    4      1       2 14148  45.00   F    4      1       2 14149  33.00   F    5      1       2 14150  18.00   M   33      1       2 14151  55.00   F   10      1       2 14152  32.00   F    5      1       2 14153  40.00   M   12      1       2 14154   7.00   F   10      1       1 14155  32.00   M   10      1       2 14156  29.00   F   10      1       2 14157  38.00   F   10      1       2 14158  23.00   M   16      1       2 14159  27.00   M   13      1       2 14160  42.00   M   11      1       2 14161  32.00   M    6      1       2 14162  25.00   F   26      1       2 14163  28.00   M   25      1       2 14164  20.00   M    7      1       2 14165  35.00   M   20      1       2 14166  30.00   F    7      1       2 14167  37.00   F    9      1       2 14168  14.00   F    7      1       1 14169  28.00   M    8      1       2 14170  35.00   F   12      1       2 14171  15.00   M   14      1       1 14172  44.00   M   18      1       2 14173  35.00   F   13      1       2 14174  38.00   M    4      1       2 14175  44.00   M   24      1       2 14176  45.00   M   11      1       2 14177  65.00   M   11      1       3 14178  47.00   M   11      1       2 14179  23.00   M   13      1       2 14180   7.00   M   13      1       1 14181   3.00   F   13      1       1 14182  27.00   F   13      1       2 14183  19.00   M    9      1       2 14184  13.00   M    9      1       1 14185  15.00   M    9      1       1 14186  11.00   M    9      1       1 14187  51.00   F   17      1       2 14188  54.00   M    8      1       2 14189  24.00   M   21      1       2 14190  21.00   M   10      1       2 14191  38.00   M   17      1       2 14192  48.00   M   20      1       2 14193  38.00   F   20      1       2 14194  11.00   F   16      1       1 14195  61.00   M    5      1       3 14196  24.00   M    5      1       2 14197  31.00   M   10      1       2 14198  17.00   M    5      1       1 14199  20.00   M   16      1       2 14200  33.00   F    5      1       2 14201  26.00   M    4      1       2 14202  18.00   F    9      1       2 14203  32.00   M   39      1       2 14204  22.00   F    9      1       2 14205  19.00   M    9      1       2 14206  27.00   M   38      1       2 14207  18.00   M    9      1       2 14208  36.00   M    9      1       2 14209  29.00   M   16      1       2 14210  55.00   M   16      1       2 14211  30.00   M   34      1       2 14212   8.00   M   34      1       1 14213  30.00   M   15      1       2 14214  46.00   M   15      1       2 14215  29.00   M    4      1       2 14216  18.00   M    4      1       2 14217  20.00   M    6      1       2 14218  11.00   F    8      1       1 14219  12.00   M   34      1       1 14220   2.00   F    8      1       1 14221  36.00   F    6      1       2 14222  54.00   M   17      1       2 14223  19.00   F    4      1       2 14224  53.00   M    4      1       2 14225  29.00   M    4      1       2 14226  25.00   F    7      1       2 14227  35.00   F    7      1       2 14228  58.00   M    9      1       2 14229  17.00   F    9      1       1 14230   5.00   F    9      1       1 14231  26.00   F    9      1       2 14232  44.00   F    9      1       2 14233  16.00   F   17      1       1 14234  46.00   M   17      1       2 14235  34.00   M   17      1       2 14236  18.00   M    8      1       2 14237  12.00   F   34      1       1 14238   5.00   M   17      1       1 14239  17.00   M   34      1       1 14240  19.00   F   34      1       2 14241  30.00   M   17      1       2 14242  32.00   F   17      1       2 14243  33.00   F   13      1       2 14244  34.00   F   13      1       2 14245  45.00   M   10      1       2 14246  17.00   F   10      1       1 14247   5.00   F   13      1       1 14248  65.00   M   17      1       3 14249  70.00   M    7      1       3 14250  55.00   M    6      1       2 14251  55.00   F    6      1       2 14252  38.00   M    6      1       2 14253  28.00   F    7      1       2 14254  38.00   M    8      1       2 14255  30.00   M    8      1       2 14256  19.00   F   13      1       2 14257  24.00   M   16      1       2 14258  21.00   M   33      1       2 14259   8.00   M   16      1       1 14260  40.00   F   10      1       2 14261  25.00   F   25      1       2 14262  34.00   M   25      1       2 14263  36.00   M   25      1       2 14264  20.00   M   20      1       2 14265  30.00   M   25      1       2 14266  27.00   F   19      1       2 14267   6.00   M   25      1       1 14268   3.00   M   25      1       1 14269  25.00   M   21      1       2 14270   1.00   M   21      1       1 14271  40.00   F   27      1       2 14272  18.00   M   25      1       2 14273  12.00   M   19      1       1 14274   2.00   M   21      1       1 14275  40.00   M   25      1       2 14276  31.00   M   19      1       2 14277  45.00   M   16      1       2 14278  35.00   F   23      1       2 14279  25.00   F    8      1       2 14280   5.00   M   25      1       1 14281  22.00   F   12      1       2 14282  30.00   F   10      1       2 14283  25.00   M   10      1       2 14284  30.00   F   10      1       2 14285  20.00   M   27      1       2 14286   6.00   M   10      1       1 14287  28.00   M    9      1       2 14288  65.00   F    9      1       3 14289  34.00   F    9      1       2 14290   8.00   F    9      1       1 14291  60.00   F   10      1       3 14292  40.00   M    9      1       2 14293  44.00   F    9      1       2 14294  27.00   F   10      1       2 14295   6.00   F   10      1       1 14296   0.90   M   10      1       1 14297  25.00   M   10      1       2 14298  40.00   M    4      1       2 14299  35.00   F   10      1       2 14300   7.00   F   10      1       1 14301   6.00   F   10      1       1 14302  32.00   F   10      1       2 14303  18.00   M   24      1       2 14304  30.00   M   23      1       2 14305  29.00   M    9      1       2 14306   4.00   M    9      1       1 14307  31.00   M    9      1       2 14308  48.00   M    9      1       2 14309  38.00   F    9      1       2 14310  13.00   M    9      1       1 14311  32.00   M    9      1       2 14312  35.00   F    9      1       2 14313  21.00   F    9      1       2 14314  18.00   F    9      1       2 14315  62.00   M    9      1       3 14316  52.00   F    9      1       2 14317  24.00   M   10      1       2 14318  21.00   M    9      1       2 14319  48.00   M    9      1       2 14320  24.00   M    9      1       2 14321  11.00   F    9      1       1 14322  10.00   F    9      1       1 14323  27.00   M    9      1       2 14324  28.00   M   12      1       2 14325  32.00   F   12      1       2 14326  19.00   F   12      1       2 14327   6.00   F    9      1       1 14328  19.00   M   12      1       2 14329  37.00   F   10      1       2 14330  45.00   M   10      1       2 14331  46.00   F   16      1       2 14332  51.00   M   24      1       2 14333  27.00   F   16      1       2 14334  51.00   M    9      1       2 14335  32.00   M    8      1       2 14336  15.00   M   14      1       1 14337  46.00   M   23      1       2 14338  26.00   F   10      1       2 14339  37.00   F   10      1       2 14340  23.00   F   10      1       2 14341  32.00   M   16      1       2 14342  42.00   M   10      1       2 14343  26.00   F   14      1       2 14344   6.00   M   12      1       1 14345  36.00   M   14      1       2 14346   3.00   M   12      1       1 14347  18.00   M   12      1       2 14348  40.00   M   12      1       2 14349  29.00   M   12      1       2 14350  16.00   M   27      1       1 14351  11.00   M   12      1       1 14352  19.00   F   12      1       2 14353  12.00   M   19      1       1 14354  50.00   M   12      1       2 14355  50.00   F   33      1       2 14356  37.00   F   22      1       2 14357  18.00   F   27      1       2 14358  15.00   F   12      1       1 14359  13.00   M   12      1       1 14360  50.00   F   12      1       2 14361  11.00   F   12      1       1 14362  19.00   F   27      1       2 14363  25.00   F   12      1       2 14364  11.00   F   12      1       1 14365  16.00   M   12      1       1 14366  13.00   M   12      1       1 14367  25.00   M   12      1       2 14368  37.00   M   12      1       2 14369  18.00   F   12      1       2 14370  36.00   F   12      1       2 14371  14.00   M   12      1       1 14372  22.00   F   12      1       2 14373  30.00   F   12      1       2 14374  21.00   F   27      1       2 14375  18.00   F   12      1       2 14376  21.00   F   12      1       2 14377   4.00   F   12      1       1 14378  12.00   F   12      1       1 14379  42.00   M   22      1       2 14380  13.00   F   12      1       1 14381  22.00   F   12      1       2 14382  23.00   M   20      1       2 14383   6.00   M   20      1       1 14384  45.00   F   11      1       2 14385  31.00   F   11      1       2 14386  36.00   M   11      1       2 14387  28.00   F   16      1       2 14388  27.00   M   11      1       2 14389  50.00   M   11      1       2 14390  40.00   F   23      1       2 14391  27.00   F    5      1       2 14392  32.00   M    5      1       2 14393  22.00   M    5      1       2 14394  14.00   F    5      1       1 14395  38.00   M   16      1       2 14396  23.00   M   25      1       2 14397  35.00   M   25      1       2 14398  16.00   M   19      1       1 14399  24.00   M   19      1       2 14400  20.00   M   27      1       2 14401  22.00   M    8      1       2 14402  46.00   F   16      1       2 14403  42.00   M   21      1       2 14404   9.00   M   21      1       1 14405  13.00   F   27      1       1 14406  35.00   F   16      1       2 14407  13.00   F    8      1       1 14408  36.00   F   16      1       2 14409  35.00   M   19      1       2 14410   1.00   M   20      1       1 14411  28.00   M    8      1       2 14412  24.00   M   19      1       2 14413  18.00   F    8      1       2 14414  21.00   F   25      1       2 14415  20.00   F   16      1       2 14416  11.00   M   23      1       1 14417  35.00   F   25      1       2 14418  10.00   M   25      1       1 14419  18.00   M    8      1       2 14420  13.00   M   19      1       1 14421  17.00   F   25      1       1 14422  18.00   M   16      1       2 14423  35.00   M   20      1       2 14424   5.00   M   25      1       1 14425   5.00   M   16      1       1 14426  30.00   F    8      1       2 14427  36.00   M   25      1       2 14428  30.00   M    8      1       2 14429  30.00   M   25      1       2 14430  30.00   F   16      1       2 14431  18.00   F   31      1       2 14432  20.00   F   23      1       2 14433  40.00   M    8      1       2 14434  36.00   F   23      1       2 14435  25.00   F   31      1       2 14436  22.00   F    8      1       2 14437  22.00   F   20      1       2 14438  25.00   F   16      1       2 14439  18.00   F   25      1       2 14440   6.00   F   25      1       1 14441  20.00   M   25      1       2 14442  28.00   M   25      1       2 14443  48.00   F   16      1       2 14444  28.00   F   16      1       2 14445  34.00   F   19      1       2 14446   3.00   F   25      1       1 14447  20.00   F   31      1       2 14448  25.00   F   16      1       2 14449  22.00   M   16      1       2 14450  30.00   F   25      1       2 14451   3.00   M   19      1       1 14452  35.00   M   20      1       2 14453   6.00   F   20      1       1 14454  47.00   M   16      1       2 14455  18.00   M   20      1       2 14456  21.00   M   16      1       2 14457  50.00   M   19      1       2 14458  27.00   M   24      1       2 14459  32.00   M    7      1       2 14460  21.00   F   19      1       2 14461   1.00   M   26      1       1 14462  21.00   M    7      1       2 14463  23.00   M   12      1       2 14464  28.00   F    7      1       2 14465  35.00   M    7      1       2 14466  27.00   M   12      1       2 14467  25.00   M   15      1       2 14468   3.00   M   21      1       1 14469  47.00   M   15      1       2 14470  35.00   M   12      1       2 14471  55.00   M   24      1       2 14472   4.00   F   11      1       1 14473  45.00   F    8      1       2 14474  35.00   F    9      1       2 14475  23.00   F    9      1       2 14476  24.00   M   11      1       2 14477  62.00   M   13      1       3 14478  22.00   M   11      1       2 14479  26.00   F   18      1       2 14480  26.00   F   18      1       2 14481  25.00   M   10      1       2 14482  10.00   F   10      1       1 14483   4.00   M   18      1       1 14484  18.00   F   10      1       2 14485  30.00   M   10      1       2 14486  16.00   F   10      1       1 14487  16.00   F   10      1       1 14488  21.00   M   10      1       2 14489  35.00   F   10      1       2 14490  30.00   F   17      1       2 14491  30.00   F   24      1       2 14492  33.00   M   21      1       2 14493  59.00   M    8      1       2 14494  37.00   M   14      1       2 14495   5.00   M    8      1       1 14496   2.00   M   11      1       1 14497  20.00   F   16      1       2 14498  54.00   F   16      1       2 14499  30.00   M    8      1       2 14500  22.00   F   14      1       2 14501  25.00   F   14      1       2 14502   3.00   F    8      1       1 14503  28.00   F   19      1       2 14504  50.00   M   16      1       2 14505  49.00   M   18      1       2 14506  42.00   M    4      1       2 14507  35.00   M   10      1       2 14508  39.00   F   18      1       2 14509  29.00   M    5      1       2 14510  25.00   F    5      1       2 14511  31.00   M    6      1       2 14512  51.00   M    5      1       2 14513  32.00   M   10      1       2 14514  25.00   F    5      1       2 14515   6.00   F   12      1       1 14516  33.00   M   16      1       2 14517  43.00   M    7      1       2 14518  31.00   F   14      1       2 14519  27.00   M   18      1       2 14520  52.00   M   18      1       2 14521  50.00   M   18      1       2 14522  44.00   M   16      1       2 14523  30.00   M    7      1       2 14524  17.00   M    6      1       1 14525  31.00   M    6      1       2 14526  52.00   M    6      1       2 14527  35.00   F    6      1       2 14528  40.00   M    9      1       2 14529  38.00   F    6      1       2 14530  43.00   M   14      1       2 14531  20.00   M   10      1       2 14532  31.00   F    6      1       2 14533  42.00   F   17      1       2 14534   7.00   F    6      1       1 14535  36.00   M    6      1       2 14536  45.00   M    6      1       2 14537  12.00   M    5      1       1 14538  31.00   F    5      1       2 14539  28.00   M   10      1       2 14540  58.00   M   10      1       2 14541  36.00   M   10      1       2 14542  48.00   F   10      1       2 14543  10.00   M   12      1       1 14544  26.00   M    8      1       2 14545  42.00   M    8      1       2 14546  43.00   M    8      1       2 14547  16.00   F    8      1       1 14548  36.00   F    8      1       2 14549  14.00   M   12      1       1 14550  30.00   F    8      1       2 14551  14.00   F   12      1       1 14552  28.00   F    8      1       2 14553  43.00   F    8      1       2 14554  49.00   M   12      1       2 14555  32.00   F    8      1       2 14556  31.00   F   12      1       2 14557   5.00   F    8      1       1 14558  16.00   F   32      1       1 14559  20.00   M   12      1       2 14560  34.00   M   15      1       2 14561  64.00   F    8      1       3 14562  24.00   F   15      1       2 14563  10.00   M   15      1       1 14564  40.00   M   12      1       2 14565  44.00   M   12      1       2 14566  38.00   M   12      1       2 14567  40.00   M   13      1       2 14568  13.00   M   12      1       1 14569  42.00   M    9      1       2 14570  48.00   M   13      1       2 14571  11.00   M   15      1       1 14572  38.00   M    7      1       2 14573  35.00   M    7      1       2 14574  26.00   F   16      1       2 14575  54.00   F   16      1       2 14576  28.00   F    7      1       2 14577  25.00   M    7      1       2 14578  36.00   M   13      1       2 14579  29.00   M    3      1       2 14580  50.00   M    9      1       2 14581  32.00   F    7      1       2 14582  35.00   F    7      1       2 14583  29.00   M    5      1       2 14584  39.00   M   11      1       2 14585  25.00   M    7      1       2 14586  55.00   F   10      1       2 14587  67.00   M    5      1       3 14588  45.00   M    7      1       2 14589  10.00   F    3      1       1 14590  28.00   M   23      1       2 14591  56.00   M    6      1       2 14592  57.00   M    7      1       2 14593  22.00   M    7      1       2 14594  35.00   M    7      1       2 14595  37.00   F    5      1       2 14596  41.00   M    7      1       2 14597  65.00   M    5      1       3 14598  53.00   M   16      1       2 14599  33.00   M    7      1       2 14600  49.00   M    7      1       2 14601  59.00   M    7      1       2 14602   4.00   M   13      1       1 14603  43.00   M    3      1       2 14604  47.00   M    9      1       2 14605  33.00   M    7      1       2 14606  39.00   M    5      1       2 14607  43.00   M    3      1       2 14608  42.00   M    3      1       2 14609  34.00   F   15      1       2 14610  40.00   M    7      1       2 14611  33.00   F    7      1       2 14612  10.00   M    9      1       1 14613  43.00   M    3      1       2 14614  31.00   F    7      1       2 14615  29.00   M    7      1       2 14616  70.00   M    5      1       3 14617   9.00   F   13      1       1 14618  41.00   M   11      1       2 14619  39.00   M   13      1       2 14620  30.00   F   10      1       2 14621  16.00   M   13      1       1 14622  33.00   M    7      1       2 14623  22.00   M   14      1       2 14624  23.00   M    8      1       2 14625  29.00   M   11      1       2 14626  29.00   M   11      1       2 14627  24.00   M   11      1       2 14628  30.00   F    8      1       2 14629  52.00   M   11      1       2 14630  52.00   M   14      1       2 14631  49.00   M    8      1       2 14632   2.00   F    8      1       1 14633  28.00   M    8      1       2 14634  57.00   M   18      1       2 14635  35.00   F   23      1       2 14636   0.30   M    9      1       1 14637   8.00   F   15      1       1 14638  19.00   M   15      1       2 14639  20.00   M   12      1       2 14640  14.00   F   15      1       1 14641  51.00   M   15      1       2 14642  58.00   M   15      1       2 14643  43.00   M   12      1       2 14644  44.00   M   17      1       2 14645  30.00   F    7      1       2 14646  35.00   F   17      1       2 14647  55.00   M    7      1       2 14648  50.00   M   15      1       2 14649  13.00   F   15      1       1 14650  15.00   F   14      1       1 14651   9.00   M   14      1       1 14652  32.00   M   10      1       2 14653  30.00   F   10      1       2 14654  42.00   M   17      1       2 14655  50.00   M    9      1       2 14656  39.00   F   17      1       2 14657  15.00   M   13      1       1 14658  44.00   M   18      1       2 14659  36.00   M   11      1       2 14660  40.00   M    8      1       2 14661  55.00   F   13      1       2 14662  47.00   M   13      1       2 14663  30.00   M    9      1       2 14664  44.00   M    7      1       2 14665  45.00   M    7      1       2 14666  58.00   M   24      1       2 14667  50.00   M    9      1       2 14668   3.00   F   16      1       1 14669  13.00   F    7      1       1 14670  47.00   M    7      1       2 14671  37.00   M    7      1       2 14672  22.00   M   13      1       2 14673  47.00   M   13      1       2 14674  29.00   M    9      1       2 14675  36.00   F    5      1       2 14676  45.00   F    9      1       2 14677  24.00   F    9      1       2 14678  50.00   M    9      1       2 14679  27.00   M    9      1       2 14680  43.00   M    4      1       2 14681  48.00   F    6      1       2 14682  28.00   M    6      1       2 14683  29.00   M   13      1       2 14684  51.00   F   13      1       2 14685  27.00   F    6      1       2 14686  59.00   M    6      1       2 14687  27.00   M   13      1       2 14688  23.00   M   13      1       2 14689  15.00   F   13      1       1 14690  30.00   F   23      1       2 14691  37.00   F   23      1       2 14692  43.00   M    9      1       2 14693  22.00   F    9      1       2 14694   3.00   F    9      1       1 14695   7.00   M   10      1       1 14696   8.00   F   10      1       1 14697  12.00   F   10      1       1 14698  15.00   F   16      1       1 14699  35.00   F   16      1       2 14700  45.00   M   16      1       2 14701  65.00   F    9      1       3 14702  34.00   M    9      1       2 14703  16.00   M    9      1       1 14704  35.00   M    9      1       2 14705  20.00   M    9      1       2 14706  32.00   M    9      1       2 14707  30.00   M    9      1       2 14708  26.00   M    9      1       2 14709  20.00   M    9      1       2 14710  20.00   M    9      1       2 14711  19.00   M    9      1       2 14712  54.00   M    9      1       2 14713  44.00   M    9      1       2 14714  25.00   M   10      1       2 14715  25.00   M    9      1       2 14716  47.00   M    9      1       2 14717  25.00   M    9      1       2 14718  45.00   F    6      1       2 14719  50.00   M    6      1       2 14720  27.00   M   23      1       2 14721  28.00   F    4      1       2 14722  28.00   M   13      1       2 14723  15.00   F    8      1       1 14724  12.00   F   13      1       1 14725  38.00   F   16      1       2 14726  31.00   M   27      1       2 14727  28.00   F   10      1       2 14728  34.00   M    7      1       2 14729  31.00   M    5      1       2 14730  14.00   M    7      1       1 14731  30.00   F   10      1       2 14732  31.00   M   10      1       2 14733  13.00   F   10      1       1 14734  30.00   F   10      1       2 14735   4.00   M   11      1       1 14736   6.00   M   10      1       1 14737  65.00   F    9      1       3 14738   2.00   F   12      1       1 14739  11.00   M    6      1       1 14740  53.00   M    8      1       2 14741  31.00   M    8      1       2 14742  29.00   F    8      1       2 14743  35.00   M    8      1       2 14744  44.00   M    8      1       2 14745  38.00   M    8      1       2 14746  32.00   F    8      1       2 14747  26.00   M    8      1       2 14748  20.00   M    8      1       2 14749  48.00   M    8      1       2 14750  66.00   F    8      1       3 14751  42.00   M   10      1       2 14752  25.00   M   12      1       2 14753  47.00   M   12      1       2 14754  22.00   M    6      1       2 14755  21.00   M    6      1       2 14756  37.00   F    6      1       2 14757  30.00   M    6      1       2 14758  45.00   M    8      1       2 14759  35.00   M   16      1       2 14760  30.00   F   14      1       2 14761  40.00   M   10      1       2 14762  24.00   M    6      1       2 14763  30.00   M    6      1       2 14764  39.00   M   10      1       2 14765  36.00   M   12      1       2 14766  36.00   M    6      1       2 14767  28.00   M   10      1       2 14768  36.00   M    9      1       2 14769   8.00   M    6      1       1 14770  28.00   M   14      1       2 14771  32.00   M   14      1       2 14772  28.00   F   14      1       2 14773  50.00   M   14      1       2 14774  36.00   F   30      1       2 14775  59.00   F   30      1       2 14776  40.00   M   14      1       2 14777  18.00   F   10      1       2 14778  19.00   F   14      1       2 14779  21.00   M   14      1       2 14780  28.00   M   10      1       2 14781  38.00   F    5      1       2 14782  17.00   M    5      1       1 14783  24.00   F    5      1       2 14784  29.00   M   12      1       2 14785  37.00   M   12      1       2 14786  42.00   F   12      1       2 14787  30.00   F   11      1       2 14788  19.00   M   16      1       2 14789  12.00   F   10      1       1 14790  18.00   M   12      1       2 14791  40.00   M   10      1       2 14792  36.00   M   10      1       2 14793  33.00   M   10      1       2 14794  30.00   M   10      1       2 14795  29.00   F   10      1       2 14796  40.00   M   10      1       2 14797  28.00   F   15      1       2 14798  44.00   F   10      1       2 14799  30.00   F   15      1       2 14800  28.00   M   10      1       2 14801  24.00   F   10      1       2 14802  30.00   F   10      1       2 14803  28.00   M   12      1       2 14804  26.00   F   15      1       2 14805   4.00   M   12      1       1 14806  40.00   M   15      1       2 14807  35.00   F   15      1       2 14808  31.00   M   16      1       2 14809  21.00   M   12      1       2 14810  28.00   F   16      1       2 14811  22.00   F   12      1       2 14812   4.00   F   12      1       1 14813   1.00   F   12      1       1 14814  28.00   M   12      1       2 14815   2.00   F   15      1       1 14816  43.00   M   15      1       2 14817  35.00   F   15      1       2 14818  21.00   M   15      1       2 14819  42.00   M   12      1       2 14820  58.00   M    6      1       2 14821  30.00   M   10      1       2 14822  29.00   M   23      1       2 14823  36.00   M    6      1       2 14824  33.00   F    6      1       2 14825  36.00   M   11      1       2 14826  14.00   F   10      1       1 14827  20.00   M   12      1       2 14828  42.00   F   15      1       2 14829  42.00   M   12      1       2 14830  26.00   M   10      1       2 14831  27.00   M    6      1       2 14832  42.00   M   12      1       2 14833  20.00   M   12      1       2 14834  44.00   M   12      1       2 14835  22.00   M   14      1       2 14836  49.00   M   10      1       2 14837  21.00   M    9      1       2 14838  39.00   M   10      1       2 14839  16.00   F   10      1       1 14840  30.00   F   17      1       2 14841  42.00   F   10      1       2 14842  47.00   M   11      1       2 14843  26.00   F   10      1       2 14844  32.00   M    9      1       2 14845  56.00   M   10      1       2 14846  42.00   M    6      1       2 14847  38.00   M   10      1       2 14848  42.00   M   12      1       2 14849  37.00   M    6      1       2 14850  45.00   M   14      1       2 14851  24.00   M   10      1       2 14852  67.00   M   12      1       3 14853  48.00   M   14      1       2 14854  42.00   M   11      1       2 14855  34.00   F   12      1       2 14856  39.00   M   14      1       2 14857  33.00   M   14      1       2 14858  44.00   M   14      1       2 14859  29.00   F    6      1       2 14860  44.00   M    6      1       2 14861  42.00   M   12      1       2 14862  36.00   F    6      1       2 14863  29.00   M   10      1       2 14864  34.00   M    6      1       2 14865  23.00   M   17      1       2 14866  32.00   M   10      1       2 14867  21.00   M   23      1       2 14868  38.00   M   10      1       2 14869  29.00   F    6      1       2 14870  34.00   M    6      1       2 14871  39.00   M    6      1       2 14872  47.00   M   10      1       2 14873  32.00   M   24      1       2 14874  16.00   M    6      1       1 14875  38.00   M   12      1       2 14876  38.00   M   10      1       2 14877  29.00   M   14      1       2 14878  31.00   M   14      1       2 14879  30.00   M   15      1       2 14880  42.00   M   11      1       2 14881  36.00   M    6      1       2 14882  41.00   M   12      1       2 14883  26.00   M   23      1       2 14884  29.00   M    6      1       2 14885  33.00   F   10      1       2 14886   4.00   M   23      1       1 14887  47.00   M   11      1       2 14888  40.00   M    6      1       2 14889  32.00   F   10      1       2 14890  31.00   M    6      1       2 14891  54.00   M   15      1       2 14892  31.00   M   10      1       2 14893  32.00   M   11      1       2 14894  30.00   F   12      1       2 14895  25.00   M   12      1       2 14896  29.00   M   10      1       2 14897  43.00   M   14      1       2 14898  39.00   M    6      1       2 14899  33.00   F   14      1       2 14900  44.00   M   12      1       2 14901  31.00   M   11      1       2 14902  27.00   M   10      1       2 14903  32.00   F   11      1       2 14904  45.00   M   18      1       2 14905  26.00   M   14      1       2 14906  40.00   F    6      1       2 14907  28.00   M   14      1       2 14908  37.00   M    6      1       2 14909  35.00   M    6      1       2 14910  55.00   M    6      1       2 14911  47.00   M    6      1       2 14912  44.00   M   19      1       2 14913  38.00   M   19      1       2 14914  35.00   M   12      1       2 14915  33.00   M   11      1       2 14916  18.00   M   12      1       2 14917  40.00   M   12      1       2 14918  50.00   F    6      1       2 14919  35.00   M    6      1       2 14920  52.00   M    6      1       2 14921  24.00   F   12      1       2 14922  57.00   M   15      1       2 14923  18.00   M   10      1       2 14924  35.00   F    6      1       2 14925  36.00   M   12      1       2 14926  46.00   M   12      1       2 14927  50.00   M   14      1       2 14928  22.00   F    6      1       2 14929  43.00   M   12      1       2 14930  49.00   M   14      1       2 14931  34.00   F    6      1       2 14932  39.00   M    6      1       2 14933  37.00   F   14      1       2 14934  33.00   M   15      1       2 14935  36.00   M   10      1       2 14936  14.00   M    6      1       1 14937  46.00   M   13      1       2 14938  43.00   M   14      1       2 14939  33.00   F   11      1       2 14940  43.00   M   14      1       2 14941  42.00   M    6      1       2 14942  26.00   M   11      1       2 14943  24.00   M    6      1       2 14944  35.00   M    7      1       2 14945  32.00   M    7      1       2 14946  40.00   M    7      1       2 14947   0.20   F   10      1       1 14948  14.00   F    7      1       1 14949  30.00   F   10      1       2 14950  30.00   F   23      1       2 14951   6.00   F   23      1       1 14952   1.00   M   23      1       1 14953  22.00   M   17      1       2 14954   3.00   M   23      1       1 14955  27.00   M   17      1       2 14956  32.00   M   23      1       2 14957  49.00   F   19      1       2 14958  22.00   F   14      1       2 14959  42.00   M   23      1       2 14960  23.00   M   23      1       2 14961  21.00   M   23      1       2 14962  26.00   F   14      1       2 14963  35.00   M   18      1       2 14964  25.00   F   23      1       2 14965   5.00   F   11      1       1 14966  65.00   F    7      1       3 14967  37.00   F    5      1       2 14968  24.00   M    9      1       2 14969  46.00   F    9      1       2 14970  35.00   F    8      1       2 14971  24.00   M   14      1       2 14972  19.00   M    7      1       2 14973  48.00   M   10      1       2 14974  32.00   M   10      1       2 14975  42.00   F   10      1       2 14976  28.00   M   10      1       2 14977  24.00   F   10      1       2 14978   7.00   F   10      1       1 14979  23.00   M   13      1       2 14980  40.00   F   17      1       2 14981  21.00   M   22      1       2 14982  18.00   M   12      1       2 14983  25.00   M   17      1       2 14984  25.00   F   10      1       2 14985  45.00   M   12      1       2 14986  30.00   M   10      1       2 14987  34.00   M   10      1       2 14988  26.00   F   10      1       2 14989  43.00   M   12      1       2 14990   2.00   M   10      1       1 14991  55.00   M   10      1       2 14992  12.00   M   10      1       1 14993  29.00   M   13      1       2 14994  19.00   M   13      1       2 14995  16.00   F    8      1       1 14996  42.00   M   12      1       2 14997  14.00   M   10      1       1 14998  27.00   F   13      1       2 14999  37.00   F   10      1       2 15000  55.00   M   10      1       2 15001  32.00   M   17      1       2 15002  56.00   F   10      1       2 15003  20.00   M   10      1       2 15004  14.00   M   17      1       1 15005  31.00   F    5      1       2 15006  28.00   F   10      1       2 15007  45.00   M   10      1       2 15008  26.00   F    5      1       2 15009  62.00   F    5      1       3 15010  30.00   F    5      1       2 15011  32.00   F    5      1       2 15012  26.00   F    5      1       2 15013  13.00   M    5      1       1 15014  46.00   M    5      1       2 15015  48.00   F   32      1       2 15016  17.00   M   10      1       1 15017  47.00   M    5      1       2 15018  15.00   M    5      1       1 15019  45.00   M    5      1       2 15020  41.00   M    5      1       2 15021   6.00   M    5      1       1 15022  51.00   M   17      1       2 15023  22.00   F    5      1       2 15024  24.00   M    5      1       2 15025  42.00   F    5      1       2 15026  26.00   F    5      1       2 15027  41.00   F   17      1       2 15028   4.00   F   10      1       1 15029  65.00   F    5      1       3 15030  25.00   M   12      1       2 15031  30.00   M   12      1       2 15032  25.00   M   12      1       2 15033  30.00   F   10      1       2 15034  22.00   M   10      1       2 15035  26.00   M   10      1       2 15036  22.00   M   10      1       2 15037  30.00   M   10      1       2 15038   9.00   M    9      1       1 15039  10.00   M   12      1       1 15040  21.00   M   10      1       2 15041  39.00   M   10      1       2 15042   4.00   M    9      1       1 15043  32.00   F    9      1       2 15044  35.00   M    9      1       2 15045  65.00   F    9      1       3 15046  24.00   F    9      1       2 15047  35.00   F    9      1       2 15048  39.00   M   27      1       2 15049  45.00   M   12      1       2 15050  50.00   F   10      1       2 15051  48.00   F   10      1       2 15052  18.00   M   10      1       2 15053   6.00   F   10      1       1 15054  30.00   F    8      1       2 15055  35.00   F   12      1       2 15056   5.00   F   10      1       1 15057  30.00   M   10      1       2 15058  15.00   M   15      1       1 15059  13.00   F   10      1       1 15060   5.00   M   12      1       1 15061  30.00   F   15      1       2 15062  12.00   M   15      1       1 15063  37.00   F   10      1       2 15064  27.00   M   10      1       2 15065   4.00   F   10      1       1 15066  40.00   M   17      1       2 15067  24.00   M   17      1       2 15068  32.00   M   10      1       2 15069  30.00   M   10      1       2 15070  10.00   M   17      1       1 15071  35.00   M   12      1       2 15072  26.00   F   17      1       2 15073  33.00   F   12      1       2 15074  35.00   M   12      1       2 15075  11.00   M   12      1       1 15076  39.00   F   17      1       2 15077  48.00   F   12      1       2 15078   2.00   F   12      1       1 15079   5.00   M   15      1       1 15080  28.00   M   10      1       2 15081   6.00   M   12      1       1 15082  20.00   M   17      1       2 15083  25.00   F   12      1       2 15084  38.00   M   15      1       2 15085  13.00   M   15      1       1 15086  33.00   F   12      1       2 15087  35.00   M   12      1       2 15088  37.00   F   12      1       2 15089  32.00   F   12      1       2 15090  15.00   M   12      1       1 15091  42.00   M   12      1       2 15092  24.00   M   14      1       2 15093  25.00   F   10      1       2 15094  55.00   M   10      1       2 15095   8.00   M   12      1       1 15096  35.00   F   12      1       2 15097  38.00   M   10      1       2 15098  40.00   M   17      1       2 15099  50.00   M   17      1       2 15100  55.00   M    7      1       2 15101  32.00   F   14      1       2 15102  23.00   F   12      1       2 15103  40.00   F   12      1       2 15104  27.00   F   11      1       2 15105  38.00   M    5      1       2 15106  45.00   M   22      1       2 15107  43.00   M   11      1       2 15108  30.00   M   11      1       2 15109  51.00   M   22      1       2 15110  40.00   M    9      1       2 15111  36.00   F   11      1       2 15112  32.00   M   11      1       2 15113  35.00   M   15      1       2 15114  38.00   M    3      1       2 15115  39.00   M   15      1       2 15116  32.00   M   10      1       2 15117  35.00   M   10      1       2 15118  43.00   F   11      1       2 15119  59.00   F   15      1       2 15120  28.00   M   15      1       2 15121  35.00   M   14      1       2 15122  45.00   M   13      1       2 15123  37.00   M   14      1       2 15124  11.00   F   20      1       1 15125  43.00   M   18      1       2 15126  42.00   F   10      1       2 15127  61.00   F   10      1       3 15128  25.00   M   12      1       2 15129  29.00   F    9      1       2 15130  43.00   M   14      1       2 15131  35.00   M   22      1       2 15132  33.00   M   11      1       2 15133  35.00   M    5      1       2 15134  22.00   M    7      1       2 15135  11.00   F   11      1       1 15136  28.00   M    9      1       2 15137  10.00   M   13      1       1 15138  18.00   F    7      1       2 15139  34.00   F   13      1       2 15140  31.00   M    5      1       2 15141  30.00   M   13      1       2 15142  33.00   M   11      1       2 15143  27.00   M   13      1       2 15144  36.00   M   13      1       2 15145  23.00   M    9      1       2 15146  48.00   M   13      1       2 15147  37.00   M   11      1       2 15148  52.00   M    9      1       2 15149  33.00   F    7      1       2 15150  47.00   M    7      1       2 15151  66.00   M    3      1       3 15152  55.00   F    7      1       2 15153   3.00   F    9      1       1 15154   4.00   M    7      1       1 15155  63.00   M   15      1       3 15156  32.00   F    7      1       2 15157  49.00   M   22      1       2 15158  12.00   M    7      1       1 15159   5.00   F    9      1       1 15160  60.00   M   14      1       3 15161  37.00   M    9      1       2 15162  32.00   M    7      1       2 15163  23.00   M   11      1       2 15164  23.00   M    6      1       2 15165  25.00   M   22      1       2 15166  25.00   M   16      1       2 15167  40.00   M   18      1       2 15168  25.00   M   12      1       2 15169  36.00   M   11      1       2 15170  29.00   M   18      1       2 15171  45.00   M    5      1       2 15172  15.00   F   22      1       1 15173  23.00   F   22      1       2 15174  26.00   M   22      1       2 15175  35.00   F    5      1       2 15176  15.00   M   13      1       1 15177  10.00   F    5      1       1 15178  35.00   F   22      1       2 15179  30.00   M    5      1       2 15180  39.00   M   13      1       2 15181  16.00   M    5      1       1 15182  12.00   M   13      1       1 15183  27.00   M    5      1       2 15184  48.00   M   22      1       2 15185  28.00   F    5      1       2 15186  14.00   F    5      1       1 15187  42.00   M    5      1       2 15188  13.00   F   24      1       1 15189  11.00   F    5      1       1 15190  27.00   M    5      1       2 15191  28.00   M   24      1       2 15192  23.00   M    5      1       2 15193  47.00   F    5      1       2 15194  14.00   M   16      1       1 15195  49.00   M    5      1       2 15196  73.00   M   16      1       3 15197  36.00   M    5      1       2 15198  20.00   F    5      1       2 15199  30.00   F    5      1       2 15200  32.00   M   20      1       2 15201  45.00   M   20      1       2 15202  40.00   F   20      1       2 15203  28.00   F   20      1       2 15204  39.00   M   14      1       2 15205  23.00   M   13      1       2 15206  46.00   M   24      1       2 15207  36.00   F   14      1       2 15208   2.00   M    9      1       1 15209   1.00   M   12      1       1 15210  22.00   M   12      1       2 15211  54.00   M    8      1       2 15212  42.00   M   14      1       2 15213  33.00   M   14      1       2 15214  33.00   M    4      1       2 15215  53.00   M   20      1       2 15216  35.00   M   20      1       2 15217  47.00   M   17      1       2 15218  45.00   M   14      1       2 15219  55.00   M   17      1       2 15220  32.00   M   14      1       2 15221  49.00   M   20      1       2 15222  50.00   M   14      1       2 15223  52.00   M   17      1       2 15224  55.00   M   15      1       2 15225  35.00   F    7      1       2 15226  30.00   M   24      1       2 15227  64.00   F    7      1       3 15228  20.00   F    6      1       2 15229  37.00   F    9      1       2 15230  36.00   F   21      1       2 15231  35.00   M   17      1       2 15232  17.00   M   10      1       1 15233  13.00   M   10      1       1 15234  25.00   F   10      1       2 15235  40.00   F   10      1       2 15236   1.00   M   10      1       1 15237  35.00   M   10      1       2 15238  35.00   F   11      1       2 15239  35.00   M    9      1       2 15240   5.00   F   20      1       1 15241  21.00   F   10      1       2 15242  32.00   M   16      1       2 15243   7.00   M   10      1       1 15244  20.00   M   10      1       2 15245  60.00   M   20      1       3 15246  40.00   M   16      1       2 15247  38.00   M   10      1       2 15248  36.00   M   10      1       2 15249  26.00   F   10      1       2 15250  10.00   F   11      1       1 15251  24.00   M   10      1       2 15252  42.00   M   16      1       2 15253  38.00   F   16      1       2 15254  17.00   M   16      1       1 15255  12.00   F   18      1       1 15256  27.00   M   18      1       2 15257  28.00   M   15      1       2 15258  22.00   F   14      1       2 15259   2.00   M   14      1       1 15260  36.00   M   10      1       2 15261  25.00   F   10      1       2 15262  27.00   M   10      1       2 15263  20.00   F   14      1       2 15264  35.00   M   10      1       2 15265   8.00   M   10      1       1 15266  30.00   F   10      1       2 15267   7.00   F   10      1       1 15268  20.00   M   14      1       2 15269  36.00   M   16      1       2 15270  70.00   F   21      1       3 15271  70.00   F   14      1       3 15272  68.00   M    8      1       3 15273  55.00   F   11      1       2 15274  68.00   M    8      1       3 15275  26.00   M    7      1       2 15276  23.00   F   10      1       2 15277  24.00   M   10      1       2 15278  18.00   M   10      1       2 15279  68.00   F   10      1       3 15280   6.00   F   18      1       1 15281  28.00   M   18      1       2 15282  28.00   F   18      1       2 15283  30.00   M   14      1       2 15284  28.00   M   14      1       2 15285  35.00   F   10      1       2 15286  45.00   F   27      1       2 15287  27.00   F   13      1       2 15288  50.00   F   13      1       2 15289  29.00   M   29      1       2 15290  31.00   M    4      1       2 15291  30.00   M   11      1       2 15292  24.00   M    4      1       2 15293  39.00   M   11      1       2 15294  18.00   M   11      1       2 15295  20.00   M   11      1       2 15296  27.00   F   16      1       2 15297  54.00   M   16      1       2 15298  20.00   M   20      1       2 15299  22.00   M   20      1       2 15300  14.00   M   16      1       1 15301  24.00   F   16      1       2 15302  16.00   M   10      1       1 15303  18.00   M   10      1       2 15304  35.00   F   21      1       2 15305   7.00   F   14      1       1 15306  40.00   F   10      1       2 15307   3.00   F   16      1       1 15308  35.00   F    9      1       2 15309  45.00   M   14      1       2 15310  26.00   F    7      1       2 15311  12.00   M   16      1       1 15312   4.00   M   14      1       1 15313  14.00   F   25      1       1 15314  32.00   M   11      1       2 15315  12.00   M   34      1       1 15316  22.00   M   10      1       2 15317  25.00   M    9      1       2 15318  23.00   F   17      1       2 15319  39.00   M   16      1       2 15320  15.00   F   14      1       1 15321  12.00   M   17      1       1 15322   8.00   M    9      1       1 15323  40.00   M    9      1       2 15324  37.00   F    9      1       2 15325  17.00   M    9      1       1 15326  14.00   M    9      1       1 15327  33.00   M   13      1       2 15328  26.00   M   34      1       2 15329  65.00   M   13      1       3 15330   6.00   F   13      1       1 15331  31.00   M   11      1       2 15332  29.00   M   11      1       2 15333  26.00   M   21      1       2 15334  24.00   F   21      1       2 15335  11.00   M   11      1       1 15336  37.00   M   11      1       2 15337  13.00   M   11      1       1 15338   8.00   M   11      1       1 15339  20.00   M   21      1       2 15340  26.00   M   14      1       2 15341  18.00   M   13      1       2 15342  17.00   M   14      1       1 15343  50.00   F   14      1       2 15344  28.00   M    9      1       2 15345  24.00   M   11      1       2 15346  57.00   F   14      1       2 15347  25.00   M   11      1       2 15348  17.00   M    9      1       1 15349  22.00   M    9      1       2 15350  28.00   M   11      1       2 15351  21.00   M   10      1       2 15352   7.00   M   11      1       1 15353  56.00   F   14      1       2 15354   8.00   M   11      1       1 15355  27.00   F   15      1       2 15356  24.00   F    8      1       2 15357  40.00   F   21      1       2 15358  37.00   F   11      1       2 15359  18.00   F    8      1       2 15360  49.00   M   11      1       2 15361  25.00   M   11      1       2 15362  16.00   M   11      1       1 15363  58.00   F    8      1       2 15364  21.00   M    8      1       2 15365  24.00   M    8      1       2 15366  24.00   M   12      1       2 15367  10.00   F   11      1       1 15368  36.00   M   11      1       2 15369  54.00   M    7      1       2 15370  62.00   F   20      1       3 15371  55.00   F    6      1       2 15372  22.00   F    6      1       2 15373  36.00   M   11      1       2 15374  26.00   F    9      1       2 15375   5.00   F    6      1       1 15376   1.00   M    6      1       1 15377  35.00   F    9      1       2 15378  21.00   F    6      1       2 15379  55.00   M    6      1       2 15380  37.00   M    7      1       2 15381  35.00   F    7      1       2 15382  25.00   M   15      1       2 15383  65.00   M   23      1       3 15384  24.00   M   23      1       2 15385  30.00   M   23      1       2 15386  35.00   F   15      1       2 15387  28.00   M   23      1       2 15388  37.00   M   23      1       2 15389  33.00   M   19      1       2 15390  12.00   M   19      1       1 15391  18.00   M   19      1       2 15392  35.00   F   19      1       2 15393  40.00   M   19      1       2 15394  13.00   M   17      1       1 15395  16.00   F   17      1       1 15396   6.00   M   15      1       1 15397  11.00   M   15      1       1 15398  28.00   M   19      1       2 15399  12.00   F   12      1       1 15400  18.00   F   15      1       2 15401  11.00   M   12      1       1 15402  14.00   F   15      1       1 15403  35.00   F   12      1       2 15404  38.00   M   15      1       2 15405   9.00   M   17      1       1 15406  40.00   M   15      1       2 15407  14.00   F   19      1       1 15408   9.00   F   23      1       1 15409  35.00   M   16      1       2 15410  37.00   F   12      1       2 15411  19.00   M   23      1       2 15412  60.00   M   19      1       3 15413   6.00   F   19      1       1 15414  30.00   F   19      1       2 15415   9.00   M   15      1       1 15416  14.00   F   19      1       1 15417  21.00   F   15      1       2 15418   9.00   M   23      1       1 15419  12.00   M   19      1       1 15420  36.00   M    2      1       2 15421   3.00   M   12      1       1 15422  52.00   F   23      1       2 15423   8.00   F   15      1       1 15424  15.00   M   15      1       1 15425  18.00   F   19      1       2 15426  25.00   M   19      1       2 15427   1.00   F   17      1       1 15428  10.00   M   15      1       1 15429  37.00   M   17      1       2 15430  22.00   F   19      1       2 15431  32.00   M   19      1       2 15432  12.00   M   19      1       1 15433   7.00   F   19      1       1 15434  35.00   F   23      1       2 15435  10.00   M   16      1       1 15436   2.00   M   19      1       1 15437  42.00   M   16      1       2 15438  16.00   M   23      1       1 15439  30.00   F   23      1       2 15440  20.00   F   15      1       2 15441  35.00   M   23      1       2 15442  11.00   M   12      1       1 15443   3.00   M   19      1       1 15444   6.00   M   19      1       1 15445  41.00   M   23      1       2 15446   7.00   M   19      1       1 15447  30.00   M   23      1       2 15448  10.00   F   23      1       1 15449  25.00   F   23      1       2 15450  34.00   F   23      1       2 15451  35.00   M   17      1       2 15452  25.00   F   19      1       2 15453  35.00   F   19      1       2 15454  12.00   F   15      1       1 15455  12.00   F   12      1       1 15456  13.00   F   19      1       1 15457  32.00   F   23      1       2 15458   6.00   M   12      1       1 15459  17.00   M   12      1       1 15460  28.00   M   16      1       2 15461  25.00   F    9      1       2 15462  22.00   F   19      1       2 15463  39.00   M   12      1       2 15464   7.00   F   23      1       1 15465   1.00   M   30      1       1 15466  28.00   F   19      1       2 15467   3.00   M   12      1       1 15468  26.00   F   15      1       2 15469  35.00   M   14      1       2 15470  28.00   F    8      1       2 15471  48.00   M   21      1       2 15472  50.00   M    9      1       2 15473   3.00   M   10      1       1 15474   8.00   F   10      1       1 15475  32.00   M   10      1       2 15476  25.00   F   21      1       2 15477  23.00   F    9      1       2 15478  15.00   F    9      1       1 15479  27.00   M   21      1       2 15480  31.00   F    9      1       2 15481   9.00   M   21      1       1 15482   7.00   M    9      1       1 15483  34.00   M    9      1       2 15484   4.00   F   21      1       1 15485  42.00   F    9      1       2 15486  35.00   M   21      1       2 15487  42.00   F    9      1       2 15488  54.00   M   14      1       2 15489  33.00   M   14      1       2 15490  48.00   M   14      1       2 15491   7.00   M   14      1       1 15492  43.00   M   14      1       2 15493  39.00   M   14      1       2 15494  32.00   F   14      1       2 15495   9.00   M   14      1       1 15496  53.00   M   14      1       2 15497  28.00   M   14      1       2 15498  18.00   M   14      1       2 15499  21.00   F   19      1       2 15500  42.00   M   14      1       2 15501  41.00   F   14      1       2 15502  47.00   M   14      1       2 15503  29.00   F    5      1       2 15504  16.00   M    4      1       1 15505  47.00   M    4      1       2 15506  12.00   F    6      1       1 15507  42.00   M    4      1       2 15508  37.00   F   16      1       2 15509  36.00   M    9      1       2 15510  57.00   M    9      1       2 15511  46.00   M    9      1       2 15512  45.00   M   13      1       2 15513  45.00   M   13      1       2 15514  40.00   M   12      1       2 15515  25.00   M   10      1       2 15516  30.00   M    8      1       2 15517  28.00   M    4      1       2 15518  32.00   M   14      1       2 15519  52.00   M    4      1       2 15520  39.00   M    4      1       2 15521  28.00   M    4      1       2 15522  37.00   M   10      1       2 15523  43.00   M    4      1       2 15524  45.00   M    8      1       2 15525  29.00   M   12      1       2 15526  51.00   M    4      1       2 15527  38.00   M    9      1       2 15528  30.00   M   10      1       2 15529  34.00   F   22      1       2 15530  32.00   M    4      1       2 15531  42.00   M   10      1       2 15532  26.00   M    9      1       2 15533  47.00   F   20      1       2 15534  25.00   M    4      1       2 15535  25.00   M    4      1       2 15536  53.00   M    4      1       2 15537  39.00   M   12      1       2 15538  28.00   M   13      1       2 15539  27.00   M    8      1       2 15540  27.00   M    4      1       2 15541  33.00   F    8      1       2 15542  61.00   M    4      1       3 15543  50.00   M    8      1       2 15544  59.00   M    4      1       2 15545  54.00   M   10      1       2 15546  51.00   M   10      1       2 15547  21.00   M    4      1       2 15548  37.00   M   20      1       2 15549  54.00   M    6      1       2 15550  48.00   M   10      1       2 15551  25.00   F    6      1       2 15552  35.00   M    6      1       2 15553  30.00   M    9      1       2 15554  42.00   M   22      1       2 15555  29.00   F    6      1       2 15556  56.00   F   13      1       2 15557  26.00   F   13      1       2 15558  22.00   M   13      1       2 15559  51.00   M   13      1       2 15560  49.00   M   10      1       2 15561  39.00   M    6      1       2 15562  36.00   M    6      1       2 15563  23.00   M   11      1       2 15564  34.00   M    8      1       2 15565  33.00   M    9      1       2 15566  33.00   F    6      1       2 15567  19.00   M   10      1       2 15568  30.00   M   13      1       2 15569  34.00   M   11      1       2 15570  43.00   M   10      1       2 15571  29.00   M    8      1       2 15572  42.00   M   20      1       2 15573  26.00   M   13      1       2 15574  32.00   M    8      1       2 15575  22.00   M   13      1       2 15576  27.00   M    8      1       2 15577  53.00   M    6      1       2 15578  43.00   F   10      1       2 15579  45.00   M   13      1       2 15580  40.00   M    9      1       2 15581  19.00   M    7      1       2 15582  17.00   F   10      1       1 15583  44.00   M   13      1       2 15584  35.00   M   11      1       2 15585  42.00   M   10      1       2 15586  27.00   M   13      1       2 15587  41.00   M   22      1       2 15588  30.00   M   13      1       2 15589  35.00   M   13      1       2 15590  31.00   M   13      1       2 15591  20.00   M   22      1       2 15592  30.00   F   14      1       2 15593   9.00   F    4      1       1 15594  38.00   M   16      1       2 15595  32.00   M    8      1       2 15596  36.00   M   10      1       2 15597  54.00   M   10      1       2 15598  19.00   M   12      1       2 15599  19.00   M   29      1       2 15600  60.00   M   21      1       3 15601  55.00   F   21      1       2 15602  54.00   M   27      1       2 15603  51.00   F   27      1       2 15604  21.00   M   27      1       2 15605  18.00   M   27      1       2 15606  20.00   F   27      1       2 15607  55.00   F   27      1       2 15608  24.00   M   27      1       2 15609  32.00   M   27      1       2 15610  38.00   M   20      1       2 15611  38.00   M   20      1       2 15612  24.00   M   27      1       2 15613  46.00   M   27      1       2 15614  23.00   M   13      1       2 15615  25.00   M   21      1       2 15616  25.00   M   10      1       2 15617  27.00   M    7      1       2 15618  24.00   F    2      1       2 15619  18.00   M    9      1       2 15620  30.00   F   19      1       2 15621  28.00   M   19      1       2 15622  68.00   M   12      1       3 15623  23.00   M   28      1       2 15624  52.00   M    9      1       2 15625   2.00   M    6      1       1 15626  41.00   M    6      1       2 15627   2.00   M    6      1       1 15628  37.00   F    6      1       2 15629  48.00   M   10      1       2 15630  11.00   F    7      1       1 15631  18.00   M    7      1       2 15632  55.00   M    7      1       2 15633  25.00   M    7      1       2 15634  47.00   M    6      1       2 15635  47.00   M   15      1       2 15636  37.00   M    6      1       2 15637  34.00   F    7      1       2 15638  36.00   M    6      1       2 15639  22.00   M    6      1       2 15640  20.00   F    6      1       2 15641  48.00   M    7      1       2 15642  52.00   M    6      1       2 15643  13.00   M    6      1       1 15644   6.00   M    6      1       1 15645  16.00   M    6      1       1 15646  16.00   M    6      1       1 15647  21.00   F   13      1       2 15648  19.00   M    6      1       2 15649  35.00   F    7      1       2 15650  34.00   F    7      1       2 15651  40.00   M    6      1       2 15652  34.00   M    7      1       2 15653  30.00   F    6      1       2 15654  45.00   M    7      1       2 15655  40.00   F    6      1       2 15656  32.00   M    6      1       2 15657  27.00   M    7      1       2 15658  11.00   F    6      1       1 15659  13.00   M    6      1       1 15660  14.00   M    6      1       1 15661  30.00   M    6      1       2 15662  20.00   M    6      1       2 15663  18.00   F   10      1       2 15664  45.00   F   11      1       2 15665  21.00   F   20      1       2 15666  12.00   F   15      1       1 15667  37.00   M   11      1       2 15668  25.00   F   11      1       2 15669  28.00   M   11      1       2 15670   5.00   F    9      1       1 15671  35.00   M   11      1       2 15672  13.00   M    9      1       1 15673  16.00   M   11      1       1 15674  48.00   M   11      1       2 15675  19.00   M   12      1       2 15676  22.00   M   12      1       2 15677  20.00   F   12      1       2 15678  12.00   M   15      1       1 15679  60.00   F   22      1       3 15680  30.00   M    9      1       2 15681   8.00   F   22      1       1 15682  38.00   F   25      1       2 15683  10.00   F   15      1       1 15684  52.00   F    9      1       2 15685  36.00   F   11      1       2 15686  13.00   F   15      1       1 15687  14.00   F   11      1       1 15688  24.00   M   11      1       2 15689  11.00   F   15      1       1 15690  40.00   F   11      1       2 15691  50.00   M   11      1       2 15692  36.00   F   11      1       2 15693  20.00   M   11      1       2 15694  35.00   F   12      1       2 15695  28.00   M   11      1       2 15696  30.00   F   17      1       2 15697  13.00   F    9      1       1 15698  25.00   M   12      1       2 15699  37.00   M   12      1       2 15700  52.00   M   20      1       2 15701  44.00   M    7      1       2 15702  37.00   F   11      1       2 15703  25.00   M   11      1       2 15704  29.00   M   11      1       2 15705  40.00   F   11      1       2 15706  17.00   M   11      1       1 15707  18.00   M   11      1       2 15708  18.00   M   20      1       2 15709  36.00   M   11      1       2 15710  28.00   M   11      1       2 15711  40.00   M   11      1       2 15712  23.00   M   12      1       2 15713  27.00   F   11      1       2 15714  44.00   M   11      1       2 15715  38.00   M   11      1       2 15716  38.00   F   11      1       2 15717  48.00   M   11      1       2 15718  20.00   F    7      1       2 15719  14.00   F    7      1       1 15720  29.00   M   15      1       2 15721   8.00   F    9      1       1 15722  48.00   M   11      1       2 15723  51.00   M   12      1       2 15724   6.00   M    9      1       1 15725  11.00   M    9      1       1 15726  10.00   M    9      1       1 15727  47.00   F   11      1       2 15728  30.00   F    7      1       2 15729  18.00   M    7      1       2 15730  36.00   F   22      1       2 15731  30.00   F    7      1       2 15732  11.00   M    7      1       1 15733  42.00   M    7      1       2 15734  22.00   M   22      1       2 15735  27.00   M    7      1       2 15736  15.00   F   10      1       1 15737  18.00   F   10      1       2 15738  60.00   M   10      1       3 15739  32.00   M   12      1       2 15740  33.00   M   12      1       2 15741  23.00   M   12      1       2 15742  25.00   M   11      1       2 15743   7.00   M   12      1       1 15744  13.00   F   11      1       1 15745  31.00   M   15      1       2 15746  36.00   M   12      1       2 15747  30.00   F    9      1       2 15748   8.00   M   10      1       1 15749  13.00   F   11      1       1 15750   5.00   M   11      1       1 15751  28.00   F    7      1       2 15752  45.00   F   32      1       2 15753   3.00   M    9      1       1 15754   3.00   M   15      1       1 15755  36.00   M   12      1       2 15756  37.00   M   15      1       2 15757  56.00   F   12      1       2 15758  26.00   M    5      1       2 15759   8.00   M   12      1       1 15760  30.00   M    8      1       2 15761  33.00   M    7      1       2 15762  36.00   M   14      1       2 15763  32.00   F   12      1       2 15764  13.00   F   10      1       1 15765  46.00   M   10      1       2 15766  35.00   M   11      1       2 15767  39.00   M   11      1       2 15768  27.00   M    5      1       2 15769  45.00   F    5      1       2 15770  18.00   M    5      1       2 15771   2.00   F    5      1       1 15772   1.00   F    5      1       1 15773  22.00   M    5      1       2 15774  16.00   F    5      1       1 15775   6.00   M    5      1       1 15776  30.00   M    5      1       2 15777  14.00   M    5      1       1 15778  55.00   M   14      1       2 15779  21.00   M   18      1       2 15780  29.00   F   14      1       2 15781  35.00   M   18      1       2 15782  36.00   M   18      1       2 15783  50.00   M   14      1       2 15784  48.00   M   14      1       2 15785  47.00   F   18      1       2 15786  57.00   M   14      1       2 15787  14.00   M   14      1       1 15788  33.00   F   18      1       2 15789  43.00   M   18      1       2 15790  76.00   F   18      1       3 15791  58.00   M    7      1       2 15792  36.00   M    7      1       2 15793  52.00   M    4      1       2 15794  24.00   M   20      1       2 15795  60.00   F    7      1       3 15796  48.00   M    7      1       2 15797  43.00   M    1      1       2 15798  43.00   M   20      1       2 15799  57.00   M   16      1       2 15800  35.00   M   10      1       2 15801  22.00   F   18      1       2 15802  53.00   F    8      1       2 15803  32.00   F   16      1       2 15804  60.00   F   14      1       3 15805  62.00   M    7      1       3 15806  46.00   F    7      1       2 15807  17.00   F    9      1       1 15808  62.00   M    9      1       3 15809  22.00   M    9      1       2 15810  25.00   F    9      1       2 15811  10.00   F    9      1       1 15812   8.00   M    9      1       1 15813  29.00   F    9      1       2 15814  49.00   M    9      1       2 15815  17.00   M    7      1       1 15816  41.00   F    7      1       2 15817  55.00   M   10      1       2 15818  25.00   M   10      1       2 15819  26.00   M    7      1       2 15820  22.00   M   23      1       2 15821  23.00   M   20      1       2 15822  17.00   M    7      1       1 15823  25.00   M   10      1       2 15824  27.00   F    7      1       2 15825  30.00   M    7      1       2 15826  20.00   F   14      1       2 15827  24.00   M   10      1       2 15828  22.00   M   10      1       2 15829  45.00   M   19      1       2 15830  25.00   M    7      1       2 15831  32.00   M   14      1       2 15832   9.00   F    7      1       1 15833  36.00   M    5      1       2 15834   2.00   F    5      1       1 15835  12.00   F   25      1       1 15836  31.00   M   14      1       2 15837  56.00   M   14      1       2 15838  46.00   M    9      1       2 15839  12.00   F   14      1       1 15840   9.00   M   23      1       1 15841   5.00   M   19      1       1 15842  44.00   M   19      1       2 15843  17.00   M   14      1       1 15844  13.00   M   14      1       1 15845  31.00   M   14      1       2 15846  27.00   M    7      1       2 15847  58.00   M    5      1       2 15848  12.00   M    7      1       1 15849  21.00   M    3      1       2 15850  40.00   M    3      1       2 15851  28.00   F    6      1       2 15852  50.00   M    3      1       2 15853   8.00   F    3      1       1 15854  35.00   F    3      1       2 15855  25.00   M    3      1       2 15856  24.00   M   19      1       2 15857  24.00   M    3      1       2 15858  28.00   M    3      1       2 15859  16.00   F    3      1       1 15860  23.00   F    3      1       2 15861   9.00   F    3      1       1 15862  27.00   F    3      1       2 15863  16.00   M    3      1       1 15864  23.00   M    3      1       2 15865  30.00   M    3      1       2 15866  35.00   M    3      1       2 15867  35.00   F    3      1       2 15868  26.00   F   12      1       2 15869  23.00   M    4      1       2 15870  20.00   F   15      1       2 15871  39.00   M    3      1       2 15872  31.00   F    4      1       2 15873  20.00   F    6      1       2 15874   2.00   F    9      1       1 15875  39.00   M   11      1       2 15876  22.00   M    9      1       2 15877  61.00   M    7      1       3 15878  25.00   M    3      1       2 15879  42.00   M    3      1       2 15880  30.00   F    3      1       2 15881  52.00   M    7      1       2 15882  24.00   M   12      1       2 15883  32.00   M   12      1       2 15884  38.00   M   12      1       2 15885  11.00   M    7      1       1 15886  40.00   M    7      1       2 15887  33.00   M   12      1       2 15888  33.00   M    7      1       2 15889  39.00   M    9      1       2 15890  13.00   M   11      1       1 15891  47.00   M   20      1       2 15892  32.00   M    9      1       2 15893  24.00   M   21      1       2 15894  32.00   M    7      1       2 15895  26.00   F    7      1       2 15896  32.00   M    8      1       2 15897  39.00   F    9      1       2 15898  43.00   M    9      1       2 15899  42.00   M    6      1       2 15900  29.00   M   11      1       2 15901  46.00   M    7      1       2 15902  41.00   F   20      1       2 15903  36.00   F    9      1       2 15904  46.00   M    7      1       2 15905  50.00   M    7      1       2 15906  43.00   M    7      1       2 15907  49.00   M    8      1       2 15908  36.00   M    7      1       2 15909  16.00   F   11      1       1 15910  40.00   M    9      1       2 15911  30.00   F   11      1       2 15912  49.00   M   10      1       2 15913  32.00   M    8      1       2 15914  55.00   M   15      1       2 15915  48.00   M    9      1       2 15916  28.00   F    7      1       2 15917  28.00   F    9      1       2 15918  47.00   M   15      1       2 15919  42.00   F    5      1       2 15920  33.00   F    5      1       2 15921  40.00   M   11      1       2 15922  41.00   M    5      1       2 15923  42.00   M    8      1       2 15924   3.00   M    7      1       1 15925  41.00   M   20      1       2 15926  33.00   M    9      1       2 15927  37.00   F    9      1       2 15928  44.00   M   12      1       2 15929  54.00   M    3      1       2 15930  33.00   M    9      1       2 15931  41.00   M    8      1       2 15932  44.00   M   20      1       2 15933  56.00   F    7      1       2 15934  28.00   M    7      1       2 15935  49.00   M   11      1       2 15936  57.00   M   11      1       2 15937  11.00   M   12      1       1 15938  46.00   M   11      1       2 15939  39.00   M    5      1       2 15940  34.00   F    7      1       2 15941  56.00   M    7      1       2 15942  47.00   F    5      1       2 15943  33.00   M    7      1       2 15944  33.00   M    7      1       2 15945  30.00   F    7      1       2 15946  20.00   M    7      1       2 15947  19.00   M    9      1       2 15948  47.00   M   11      1       2 15949  30.00   M    9      1       2 15950  48.00   F   12      1       2 15951  18.00   M   11      1       2 15952  44.00   M   18      1       2 15953  55.00   M    5      1       2 15954  42.00   M   11      1       2 15955  44.00   M    9      1       2 15956  36.00   M    7      1       2 15957  24.00   M    8      1       2 15958  35.00   M   11      1       2 15959  20.00   M    7      1       2 15960  37.00   M    7      1       2 15961  54.00   M    7      1       2 15962  16.00   M   11      1       1 15963  44.00   M   10      1       2 15964  26.00   M    5      1       2 15965  51.00   M   11      1       2 15966  55.00   M   11      1       2 15967  39.00   M    7      1       2 15968  48.00   M   12      1       2 15969  42.00   M   11      1       2 15970  35.00   M   11      1       2 15971  40.00   F    5      1       2 15972   6.00   M   13      1       1 15973  42.00   M    5      1       2 15974  54.00   M   13      1       2 15975  18.00   M    9      1       2 15976  46.00   M   18      1       2 15977  25.00   M    7      1       2 15978  37.00   M   11      1       2 15979  42.00   M    5      1       2 15980  43.00   M    9      1       2 15981  42.00   M    7      1       2 15982  44.00   M    5      1       2 15983  61.00   M    7      1       3 15984  13.00   M   11      1       1 15985  40.00   F   11      1       2 15986  38.00   M   11      1       2 15987  41.00   M   11      1       2 15988  20.00   M   11      1       2 15989  58.00   M    5      1       2 15990  33.00   M    8      1       2 15991  19.00   M    7      1       2 15992  22.00   M    7      1       2 15993  18.00   F   11      1       2 15994  48.00   M    7      1       2 15995  22.00   M    7      1       2 15996  18.00   M   13      1       2 15997  37.00   F    8      1       2 15998  40.00   M   11      1       2 15999  22.00   M   11      1       2 16000  16.00   F    7      1       1 16001  41.00   M   13      1       2 16002  58.00   F    8      1       2 16003  26.00   F   11      1       2 16004  35.00   M   21      1       2 16005  27.00   M   12      1       2 16006  32.00   F   21      1       2 16007  42.00   M    8      1       2 16008  45.00   M   10      1       2 16009  45.00   M    3      1       2 16010  48.00   M    7      1       2 16011  41.00   M   11      1       2 16012  52.00   M   20      1       2 16013  42.00   M   12      1       2 16014  29.00   M   11      1       2 16015  26.00   M    8      1       2 16016  40.00   M   20      1       2 16017  48.00   M    9      1       2 16018  52.00   M    7      1       2 16019  18.00   F   20      1       2 16020  38.00   M    9      1       2 16021  41.00   M    7      1       2 16022  28.00   M   12      1       2 16023  33.00   F   13      1       2 16024  29.00   F    7      1       2 16025  39.00   F    3      1       2 16026  32.00   M    7      1       2 16027  41.00   M    7      1       2 16028  48.00   M    8      1       2 16029  55.00   M   12      1       2 16030  30.00   M    5      1       2 16031  26.00   M    5      1       2 16032   9.00   F    7      1       1 16033  37.00   M    8      1       2 16034  32.00   F    7      1       2 16035  42.00   M   10      1       2 16036  55.00   M    9      1       2 16037  33.00   M    5      1       2 16038   2.00   M    6      1       1 16039  32.00   F    6      1       2 16040  42.00   M    6      1       2 16041  28.00   F    5      1       2 16042  34.00   F    5      1       2 16043  50.00   M    8      1       2 16044  47.00   M    7      1       2 16045  40.00   M    5      1       2 16046  32.00   M   12      1       2 16047  26.00   M    9      1       2 16048  60.00   M   11      1       3 16049  38.00   M    9      1       2 16050  31.00   M   11      1       2 16051  28.00   M    9      1       2 16052  46.00   F    5      1       2 16053  32.00   F    5      1       2 16054  22.00   M    7      1       2 16055  41.00   M    7      1       2 16056  42.00   M   13      1       2 16057  32.00   M    9      1       2 16058  31.00   M    7      1       2 16059  52.00   M   13      1       2 16060  28.00   F    5      1       2 16061  33.00   M   11      1       2 16062  53.00   M    7      1       2 16063  37.00   F    9      1       2 16064  33.00   M    5      1       2 16065  25.00   F    9      1       2 16066  33.00   M    8      1       2 16067  21.00   M    7      1       2 16068  32.00   M    9      1       2 16069  33.00   M    9      1       2 16070  46.00   M    3      1       2 16071  47.00   M   11      1       2 16072  38.00   M   13      1       2 16073  18.00   M    6      1       2 16074  24.00   M    7      1       2 16075  18.00   M    7      1       2 16076  43.00   F   11      1       2 16077  39.00   M    7      1       2 16078  26.00   M    3      1       2 16079  60.00   M    6      1       3 16080  30.00   M    4      1       2 16081  36.00   M   12      1       2 16082   8.00   F    4      1       1 16083  19.00   M    4      1       2 16084  28.00   M    4      1       2 16085  45.00   M    4      1       2 16086  34.00   M    4      1       2 16087  45.00   F    4      1       2 16088  51.00   M    4      1       2 16089  35.00   M    4      1       2 16090  35.00   F    4      1       2 16091  69.00   M   11      1       3 16092  56.00   M    9      1       2 16093  39.00   M    4      1       2 16094  70.00   F    4      1       3 16095  65.00   F   15      1       3 16096  47.00   F    4      1       2 16097  58.00   F    8      1       2 16098  60.00   F    4      1       3 16099  58.00   M   19      1       2 16100  25.00   F    4      1       2 16101  70.00   M    6      1       3 16102  58.00   F   12      1       2 16103  70.00   M    4      1       3 16104  25.00   F   11      1       2 16105  33.00   M    7      1       2 16106  64.00   M    7      1       3 16107  36.00   F   10      1       2 16108  60.00   F    7      1       3 16109  35.00   F   10      1       2 16110  42.00   F    7      1       2 16111  29.00   M   13      1       2 16112  61.00   M   24      1       3 16113  27.00   F    8      1       2 16114  47.00   F    8      1       2 16115   4.00   M   12      1       1 16116  46.00   F   10      1       2 16117  45.00   F   18      1       2 16118  24.00   F   27      1       2 16119  24.00   M   10      1       2 16120  23.00   M   20      1       2 16121  42.00   M   32      1       2 16122  32.00   M   17      1       2 16123  45.00   F   27      1       2 16124  28.00   M   10      1       2 16125  37.00   F    5      1       2 16126  18.00   F    9      1       2 16127  40.00   M   14      1       2 16128  28.00   M    9      1       2 16129  60.00   F    9      1       3 16130   4.00   M   14      1       1 16131  50.00   M    9      1       2 16132  45.00   F    9      1       2 16133  46.00   M    9      1       2 16134  42.00   M   11      1       2 16135  25.00   M   16      1       2 16136  18.00   M    9      1       2 16137  16.00   M    9      1       1 16138   8.00   M   10      1       1 16139  29.00   M    9      1       2 16140  27.00   F    6      1       2 16141   7.00   F    9      1       1 16142  25.00   M   14      1       2 16143  20.00   M   11      1       2 16144  38.00   M   31      1       2 16145  16.00   F   11      1       1 16146  24.00   M   31      1       2 16147  56.00   F    9      1       2 16148   3.00   M   16      1       1 16149  60.00   F   10      1       3 16150   6.00   F    9      1       1 16151  19.00   M   11      1       2 16152   6.00   M   11      1       1 16153   4.00   M   11      1       1 16154  28.00   F    6      1       2 16155  42.00   M   11      1       2 16156  50.00   F   28      1       2 16157  35.00   F   10      1       2 16158  38.00   F   11      1       2 16159   5.00   M   11      1       1 16160  17.00   F   11      1       1 16161  14.00   M    9      1       1 16162  30.00   M    9      1       2 16163  11.00   M    9      1       1 16164  99.00   F   11      1       3 16165  35.00   M    9      1       2 16166   5.00   M    9      1       1 16167  30.00   F   20      1       2 16168  29.00   M   11      1       2 16169  12.00   M   11      1       1 16170  10.00   M    9      1       1 16171  35.00   M    9      1       2 16172  22.00   M   14      1       2 16173   5.00   F   11      1       1 16174  24.00   F   11      1       2 16175   4.00   F   11      1       1 16176  25.00   F   11      1       2 16177   1.00   F    9      1       1 16178  38.00   M    9      1       2 16179  29.00   M    9      1       2 16180  40.00   M    9      1       2 16181  30.00   M   11      1       2 16182  45.00   M    9      1       2 16183   2.00   M    9      1       1 16184  50.00   F    9      1       2 16185  36.00   F   11      1       2 16186   7.00   M   31      1       1 16187  13.00   F   11      1       1 16188  26.00   M   11      1       2 16189  35.00   M   31      1       2 16190  30.00   M    9      1       2 16191  25.00   F   11      1       2 16192  20.00   M   11      1       2 16193  30.00   M   11      1       2 16194  32.00   M   11      1       2 16195  18.00   F   11      1       2 16196   7.00   F   11      1       1 16197  28.00   M   11      1       2 16198  35.00   M   13      1       2 16199  24.00   F   13      1       2 16200  26.00   F   11      1       2 16201  32.00   M   11      1       2 16202  49.00   F   11      1       2 16203  30.00   M   11      1       2 16204  52.00   M    9      1       2 16205  20.00   M   17      1       2 16206  38.00   M    9      1       2 16207  22.00   F   11      1       2 16208  25.00   M   11      1       2 16209  40.00   F   11      1       2 16210  22.00   F   11      1       2 16211  45.00   M   11      1       2 16212  40.00   F   11      1       2 16213  18.00   M   11      1       2 16214  35.00   F   11      1       2 16215  28.00   M   11      1       2 16216  31.00   M   36      1       2 16217  27.00   M   11      1       2 16218  15.00   F   11      1       1 16219  12.00   F   11      1       1 16220  39.00   M    9      1       2 16221  20.00   F    9      1       2 16222  30.00   F    9      1       2 16223   7.00   F    9      1       1 16224  26.00   M   22      1       2 16225  23.00   F    9      1       2 16226  20.00   M    9      1       2 16227  25.00   F   21      1       2 16228  24.00   F   19      1       2 16229  48.00   F    6      1       2 16230  45.00   F    6      1       2 16231  27.00   M   21      1       2 16232  21.00   F    9      1       2 16233  23.00   F    9      1       2 16234  14.00   M   21      1       1 16235  12.00   M    8      1       1 16236   6.00   F    8      1       1 16237   9.00   M   21      1       1 16238   8.00   F   21      1       1 16239  19.00   F   11      1       2 16240  18.00   M   11      1       2 16241  17.00   M   11      1       1 16242  40.00   M   11      1       2 16243  16.00   F   11      1       1 16244  31.00   F   11      1       2 16245  35.00   M   10      1       2 16246  30.00   F   10      1       2 16247  10.00   M   10      1       1 16248   8.00   M   10      1       1 16249  30.00   M   11      1       2 16250  62.00   M   21      1       3 16251  45.00   M   11      1       2 16252  35.00   F   11      1       2 16253  21.00   F   11      1       2 16254  40.00   M    9      1       2 16255  35.00   F    9      1       2 16256   9.00   M    9      1       1 16257  12.00   M   21      1       1 16258  11.00   M   11      1       1 16259  52.00   M   11      1       2 16260  48.00   F   21      1       2 16261  15.00   F   11      1       1 16262  24.00   M   21      1       2 16263  34.00   M    8      1       2 16264  61.00   F   17      1       3 16265  50.00   F    6      1       2 16266  45.00   M    6      1       2 16267  27.00   F    6      1       2 16268  54.00   F    8      1       2 16269  36.00   M    8      1       2 16270   2.00   F   14      1       1 16271  40.00   M   10      1       2 16272  25.00   F   10      1       2 16273  30.00   M   11      1       2 16274  38.00   M    8      1       2 16275  42.00   F   11      1       2 16276  29.00   M    6      1       2 16277  28.00   M    7      1       2 16278  32.00   M    7      1       2 16279  29.00   M    7      1       2 16280  40.00   M   15      1       2 16281  35.00   M   13      1       2 16282  10.00   M    9      1       1 16283   8.00   F    6      1       1 16284  23.00   M    9      1       2 16285  35.00   F   11      1       2 16286  10.00   M   11      1       1 16287   8.00   M   21      1       1 16288  24.00   M   11      1       2 16289  30.00   M   11      1       2 16290  55.00   F   11      1       2 16291  55.00   F    6      1       2 16292  34.00   F    9      1       2 16293  17.00   M   21      1       1 16294  34.00   M   21      1       2 16295  12.00   M    8      1       1 16296   6.00   M    8      1       1 16297   5.00   F    8      1       1 16298  14.00   M    8      1       1 16299  55.00   F   11      1       2 16300  36.00   F   11      1       2 16301   2.00   M    8      1       1 16302   8.00   M   11      1       1 16303  21.00   M   11      1       2 16304  18.00   M   11      1       2 16305  20.00   M   21      1       2 16306  20.00   M   11      1       2 16307  24.00   M   21      1       2 16308  34.00   M   11      1       2 16309  58.00   M   10      1       2 16310  20.00   M   21      1       2 16311   7.00   M   16      1       1 16312  35.00   M   16      1       2 16313  50.00   M   11      1       2 16314  30.00   M   11      1       2 16315  35.00   M   11      1       2 16316  58.00   M   11      1       2 16317  26.00   M    8      1       2 16318  37.00   F   11      1       2 16319  29.00   M   11      1       2 16320   6.00   M    8      1       1 16321   2.00   M    8      1       1 16322  23.00   F   19      1       2 16323  29.00   M   11      1       2 16324  26.00   M   14      1       2 16325  30.00   M   13      1       2 16326  38.00   M    8      1       2 16327  44.00   F    8      1       2 16328  43.00   M   27      1       2 16329  36.00   M    6      1       2 16330  27.00   M    6      1       2 16331  37.00   M    6      1       2 16332  31.00   M    6      1       2 16333  35.00   F    6      1       2 16334  24.00   M    8      1       2 16335  33.00   M    6      1       2 16336  42.00   F    6      1       2 16337  13.00   F    3      1       1 16338  53.00   M   13      1       2 16339  42.00   F   13      1       2 16340  16.00   M   13      1       1 16341  29.00   M    6      1       2 16342  30.00   M   13      1       2 16343  15.00   M    6      1       1 16344  38.00   F   14      1       2 16345  49.00   M    1      1       2 16346  30.00   M   19      1       2 16347  24.00   M    6      1       2 16348  32.00   F    3      1       2 16349   6.00   F   13      1       1 16350  27.00   M   13      1       2 16351   9.00   M   13      1       1 16352  37.00   M   10      1       2 16353  50.00   M   10      1       2 16354  40.00   M    9      1       2 16355  38.00   F    6      1       2 16356  73.00   M   11      1       3 16357  34.00   M    7      1       2 16358  39.00   M    7      1       2 16359  43.00   M    4      1       2 16360  45.00   M   11      1       2 16361  49.00   M    7      1       2 16362   8.00   F    7      1       1 16363  48.00   F    7      1       2 16364  59.00   M    7      1       2 16365  55.00   F    7      1       2 16366  56.00   F    8      1       2 16367  41.00   M    8      1       2 16368  39.00   M    6      1       2 16369  26.00   F    6      1       2 16370  39.00   M    6      1       2 16371   3.00   F   11      1       1 16372  34.00   M   17      1       2 16373  61.00   M   12      1       3 16374  23.00   M   10      1       2 16375  35.00   M   11      1       2 16376   1.00   F   11      1       1 16377  48.00   F    7      1       2 16378  33.00   M    4      1       2 16379  33.00   M    7      1       2 16380  46.00   M   11      1       2 16381  12.00   F    7      1       1 16382  10.00   F    7      1       1 16383  55.00   M   11      1       2 16384  44.00   M    7      1       2 16385  10.00   M    7      1       1 16386  40.00   M    7      1       2 16387  36.00   M    7      1       2 16388  46.00   M    7      1       2 16389  13.00   F    7      1       1 16390  39.00   M   11      1       2 16391  24.00   M    7      1       2 16392  32.00   M   11      1       2 16393  20.00   F    9      1       2 16394  48.00   M   11      1       2 16395  35.00   M    7      1       2 16396  44.00   F    8      1       2 16397  31.00   M    7      1       2 16398  33.00   F    7      1       2 16399  35.00   F   11      1       2 16400  46.00   M    2      1       2 16401   6.00   M    6      1       1 16402  38.00   M    6      1       2 16403  36.00   M    4      1       2 16404   5.00   M    7      1       1 16405  10.00   M    4      1       1 16406  13.00   F    7      1       1 16407  42.00   M    7      1       2 16408   7.00   F    7      1       1 16409  13.00   F   11      1       1 16410  48.00   M   16      1       2 16411  39.00   F   11      1       2 16412  12.00   F   16      1       1 16413  17.00   F   11      1       1 16414  37.00   F   11      1       2 16415  12.00   F   11      1       1 16416  26.00   F    4      1       2 16417  45.00   M    4      1       2 16418  44.00   M    7      1       2 16419  43.00   M   11      1       2 16420  27.00   F    7      1       2 16421  37.00   M   11      1       2 16422  17.00   M   11      1       1 16423  50.00   M    7      1       2 16424  42.00   F   11      1       2 16425  37.00   M    7      1       2 16426  48.00   M   18      1       2 16427  60.00   M    7      1       3 16428  25.00   M    7      1       2 16429  33.00   M    9      1       2 16430  30.00   M   11      1       2 16431  42.00   M    7      1       2 16432  46.00   M   10      1       2 16433  46.00   M    7      1       2 16434   6.00   F    7      1       1 16435  71.00   M   11      1       3 16436   6.00   M   10      1       1 16437  36.00   F   10      1       2 16438  26.00   M   11      1       2 16439  33.00   M    6      1       2 16440  34.00   M    8      1       2 16441  31.00   M    6      1       2 16442   4.00   F    6      1       1 16443  46.00   M    8      1       2 16444  20.00   M    8      1       2 16445  34.00   F    6      1       2 16446  10.00   F    7      1       1 16447  38.00   F   12      1       2 16448  28.00   M   19      1       2 16449  52.00   F   10      1       2 16450  19.00   M    8      1       2 16451  33.00   M   11      1       2 16452  25.00   F    6      1       2 16453  48.00   F    6      1       2 16454  45.00   M    6      1       2 16455  45.00   F   10      1       2 16456  34.00   F    8      1       2 16457  13.00   M    6      1       1 16458  36.00   M    6      1       2 16459  31.00   M    6      1       2 16460  48.00   M    4      1       2 16461  43.00   F    5      1       2 16462  41.00   F   12      1       2 16463  46.00   M    8      1       2 16464  10.00   F    4      1       1 16465  38.00   M    4      1       2 16466  61.00   M    8      1       3 16467   1.00   M    5      1       1 16468  39.00   M   11      1       2 16469   4.00   M    7      1       1 16470  27.00   F    7      1       2 16471  19.00   M   11      1       2 16472  30.00   M   11      1       2 16473  28.00   M    2      1       2 16474  31.00   F    8      1       2 16475  64.00   M    4      1       3 16476   1.00   F   12      1       1 16477  52.00   M    8      1       2 16478  46.00   F    8      1       2 16479  26.00   F    8      1       2 16480  17.00   M   12      1       1 16481  65.00   M    8      1       3 16482  54.00   M    8      1       2 16483  47.00   F   15      1       2 16484  72.00   F   19      1       3 16485  65.00   F    9      1       3 16486  17.00   M   26      1       1 16487  20.00   F   31      1       2 16488  35.00   M   26      1       2 16489  12.00   M   15      1       1 16490  18.00   M   15      1       2 16491  26.00   M   15      1       2 16492  26.00   F   14      1       2 16493  19.00   F   13      1       2 16494  22.00   M   13      1       2 16495  44.00   F   13      1       2 16496  29.00   M   13      1       2 16497  18.00   M   13      1       2 16498   4.00   F   13      1       1 16499  10.00   F   28      1       1 16500  37.00   M   13      1       2 16501  33.00   F   14      1       2 16502   4.00   M   14      1       1 16503  45.00   M   13      1       2 16504  43.00   M   19      1       2 16505  24.00   F   31      1       2 16506  25.00   M   13      1       2 16507  18.00   M   26      1       2 16508  24.00   M   13      1       2 16509  45.00   M   13      1       2 16510  16.00   M   15      1       1 16511  22.00   M   15      1       2 16512  26.00   M   15      1       2 16513  24.00   F   19      1       2 16514   2.00   F   31      1       1 16515  28.00   F   15      1       2 16516  25.00   F   15      1       2 16517   7.00   M   15      1       1 16518   7.00   M   15      1       1 16519  51.00   M   13      1       2 16520  32.00   M   10      1       2 16521  30.00   F   10      1       2 16522  20.00   M    5      1       2 16523  20.00   M    5      1       2 16524  45.00   F    5      1       2 16525  55.00   F    5      1       2 16526  55.00   M    5      1       2 16527  19.00   F   10      1       2 16528  55.00   M   20      1       2 16529   3.00   F    9      1       1 16530  20.00   F    9      1       2 16531  10.00   F    9      1       1 16532   6.00   M   10      1       1 16533  60.00   M    9      1       3 16534  20.00   M   20      1       2 16535  25.00   M    5      1       2 16536  21.00   F    8      1       2 16537  35.00   F    5      1       2 16538  25.00   M   16      1       2 16539  20.00   F   16      1       2 16540  28.00   M    8      1       2 16541  18.00   F   21      1       2 16542  12.00   M    9      1       1 16543   9.00   M    9      1       1 16544  25.00   F   20      1       2 16545  10.00   F   20      1       1 16546  20.00   M    9      1       2 16547  35.00   F    8      1       2 16548  18.00   F    8      1       2 16549  40.00   M    9      1       2 16550  13.00   F    9      1       1 16551  25.00   M   10      1       2 16552  25.00   F    8      1       2 16553  52.00   M   10      1       2 16554  20.00   M    8      1       2 16555  18.00   M    8      1       2 16556  30.00   F   20      1       2 16557  32.00   M    8      1       2 16558  29.00   M   10      1       2 16559  71.00   F    7      1       3 16560  50.00   F    7      1       2 16561  23.00   F    6      1       2 16562  20.00   F    6      1       2 16563  46.00   F    6      1       2 16564  45.00   M    6      1       2 16565  42.00   F    6      1       2 16566  46.00   F    7      1       2 16567  24.00   F    6      1       2 16568   9.00   M    7      1       1 16569  42.00   M    7      1       2 16570  31.00   F    7      1       2 16571  36.00   F    7      1       2 16572  42.00   F    7      1       2 16573  48.00   F    7      1       2 16574  53.00   F    6      1       2 16575  44.00   M    7      1       2 16576  33.00   F   25      1       2 16577  49.00   F   25      1       2 16578  40.00   M   25      1       2 16579  26.00   M   25      1       2 16580  37.00   F   25      1       2 16581  32.00   F    9      1       2 16582  25.00   M    9      1       2 16583  57.00   F   11      1       2 16584  52.00   M   26      1       2 16585  57.00   M    9      1       2 16586  33.00   M   19      1       2 16587  50.00   F   16      1       2 16588  55.00   M   18      1       2 16589  18.00   M    9      1       2 16590  42.00   F    9      1       2 16591  48.00   F    9      1       2 16592  50.00   M   26      1       2 16593  22.00   F   26      1       2 16594   7.00   M   13      1       1 16595  17.00   F   10      1       1 16596  45.00   F   13      1       2 16597  16.00   F   13      1       1 16598  28.00   F   13      1       2 16599  38.00   F   10      1       2 16600  15.00   M   10      1       1 16601  54.00   M   10      1       2 16602  48.00   M   10      1       2 16603  13.00   M   13      1       1 16604  42.00   F   10      1       2 16605  16.00   F   10      1       1 16606  52.00   M   12      1       2 16607  37.00   M   14      1       2 16608  50.00   M    3      1       2 16609  17.00   M    3      1       1 16610  32.00   F    3      1       2 16611  52.00   M    8      1       2 16612  57.00   M   18      1       2 16613  32.00   M    7      1       2 16614  34.00   M   18      1       2 16615  33.00   M   14      1       2 16616  40.00   M    3      1       2 16617  41.00   F    3      1       2 16618  15.00   M    3      1       1 16619  40.00   F    6      1       2 16620  38.00   M   18      1       2 16621  41.00   M   18      1       2 16622  45.00   M    9      1       2 16623  14.00   M    6      1       1 16624  35.00   F   20      1       2 16625  12.00   F   19      1       1 16626  10.00   M   19      1       1 16627  50.00   M    7      1       2 16628  31.00   M   11      1       2 16629  39.00   M    7      1       2 16630  21.00   F   16      1       2 16631  24.00   F   16      1       2 16632  11.00   F   16      1       1 16633  59.00   M    9      1       2 16634  49.00   F    9      1       2 16635  14.00   M   14      1       1 16636  24.00   F   14      1       2 16637  35.00   M    8      1       2 16638  64.00   M   12      1       3 16639  61.00   F    5      1       3 16640  31.00   F    9      1       2 16641  25.00   M    5      1       2 16642  43.00   M    5      1       2 16643  24.00   M    9      1       2 16644  28.00   M    8      1       2 16645  36.00   M   12      1       2 16646  30.00   F   12      1       2 16647  20.00   M    5      1       2 16648   7.00   F    9      1       1 16649   4.00   F    5      1       1 16650  28.00   F    5      1       2 16651  36.00   M    5      1       2 16652  30.00   F    8      1       2 16653   5.00   M   27      1       1 16654  20.00   M    5      1       2 16655  10.00   F   27      1       1 16656  52.00   M    8      1       2 16657  20.00   M    9      1       2 16658  47.00   M    9      1       2 16659  20.00   M    9      1       2 16660  22.00   M    5      1       2 16661  26.00   M    5      1       2 16662  12.00   M    5      1       1 16663  25.00   M    7      1       2 16664  35.00   M    5      1       2 16665  17.00   M    5      1       1 16666  45.00   M    9      1       2 16667  24.00   M   12      1       2 16668   7.00   M    5      1       1 16669  28.00   M    5      1       2 16670  27.00   F    5      1       2 16671   6.00   F    5      1       1 16672  26.00   M    9      1       2 16673  13.00   M   13      1       1 16674   8.00   F   10      1       1 16675  28.00   F   10      1       2 16676  22.00   F   10      1       2 16677  18.00   M    9      1       2 16678  30.00   M    9      1       2 16679   6.00   F    9      1       1 16680  30.00   F    9      1       2 16681   2.00   F    8      1       1 16682  42.00   M   14      1       2 16683  50.00   M   18      1       2 16684  41.00   F   14      1       2 16685  15.00   M   14      1       1 16686  42.00   F    7      1       2 16687  32.00   F    6      1       2 16688  35.00   M    7      1       2 16689  47.00   F   18      1       2 16690   7.00   M    3      1       1 16691  42.00   M    5      1       2 16692  42.00   M    6      1       2 16693  57.00   M   10      1       2 16694  53.00   F    7      1       2 16695  52.00   M    3      1       2 16696  30.00   F   23      1       2 16697  45.00   M   20      1       2 16698   8.00   M    4      1       1 16699  23.00   M   17      1       2 16700  26.00   F   11      1       2 16701  23.00   M   10      1       2 16702  47.00   M   15      1       2 16703  38.00   F   15      1       2 16704  18.00   F    8      1       2 16705  48.00   F   15      1       2 16706  39.00   F   30      1       2 16707  10.00   F   15      1       1 16708  22.00   F   15      1       2 16709  16.00   M   16      1       1 16710  20.00   F   16      1       2 16711  36.00   M   16      1       2 16712  45.00   F   10      1       2 16713  64.00   F   19      1       3 16714  38.00   M    9      1       2 16715  34.00   F    9      1       2 16716   9.00   F    9      1       1 16717  20.00   M    9      1       2 16718  36.00   M   30      1       2 16719  23.00   F   15      1       2 16720  58.00   F    7      1       2 16721  46.00   M   17      1       2 16722  42.00   F   17      1       2 16723  14.00   M   17      1       1 16724   4.00   F   14      1       1 16725  30.00   F   17      1       2 16726  22.00   M   13      1       2 16727  42.00   F   13      1       2 16728  46.00   M   13      1       2 16729  12.00   M   17      1       1 16730  23.00   F   17      1       2 16731  14.00   M   16      1       1 16732   5.00   F   30      1       1 16733  24.00   F    9      1       2 16734  18.00   F    9      1       2 16735  38.00   F   14      1       2 16736  18.00   F   30      1       2 16737  30.00   M   30      1       2 16738  42.00   M    9      1       2 16739  27.00   F    9      1       2 16740  44.00   M    9      1       2 16741  42.00   F   14      1       2 16742  16.00   M   16      1       1 16743  20.00   M   14      1       2 16744  78.00   M   12      1       3 16745  29.00   F   16      1       2 16746  20.00   F    9      1       2 16747  31.00   M    9      1       2 16748  20.00   F   15      1       2 16749  45.00   F    9      1       2 16750  22.00   F    9      1       2 16751  20.00   F    9      1       2 16752  15.00   M   16      1       1 16753  23.00   M    9      1       2 16754  24.00   F   14      1       2 16755  42.00   F    9      1       2 16756  26.00   M    9      1       2 16757  32.00   F    9      1       2 16758  25.00   F   14      1       2 16759  71.00   F   29      1       3 16760  40.00   F   30      1       2 16761  18.00   M    9      1       2 16762  26.00   M   25      1       2 16763  10.00   M    9      1       1 16764  20.00   F   10      1       2 16765   7.00   M   12      1       1 16766  17.00   F    7      1       1 16767  30.00   F    7      1       2 16768  35.00   M   15      1       2 16769  33.00   M   12      1       2 16770  30.00   F    7      1       2 16771   8.00   M    7      1       1 16772  23.00   F    7      1       2 16773  28.00   F    7      1       2 16774  18.00   M   10      1       2 16775  27.00   M    7      1       2 16776  23.00   M   12      1       2 16777  34.00   M   10      1       2 16778  36.00   M    7      1       2 16779  30.00   M   15      1       2 16780  41.00   M    7      1       2 16781  42.00   M    7      1       2 16782  43.00   M    7      1       2 16783  29.00   M    7      1       2 16784  33.00   M    7      1       2 16785  28.00   M    7      1       2 16786  26.00   M    7      1       2 16787  22.00   M   10      1       2 16788  25.00   M    7      1       2 16789  27.00   M    7      1       2 16790  35.00   M    7      1       2 16791  25.00   M    7      1       2 16792  41.00   M   15      1       2 16793  28.00   F    7      1       2 16794   7.00   F   10      1       1 16795  12.00   M    7      1       1 16796  60.00   F   11      1       3 16797  30.00   F    7      1       2 16798  12.00   F   11      1       1 16799  30.00   M    7      1       2 16800  24.00   M   12      1       2 16801  25.00   F    7      1       2 16802  38.00   M    7      1       2 16803  32.00   F   10      1       2 16804  24.00   F    7      1       2 16805  16.00   F   10      1       1 16806  36.00   M    7      1       2 16807   6.00   M    7      1       1 16808  10.00   M   12      1       1 16809  13.00   M   10      1       1 16810  11.00   F   10      1       1 16811  35.00   F   10      1       2 16812  45.00   F    6      1       2 16813  54.00   M    6      1       2 16814  48.00   M   15      1       2 16815  34.00   M   13      1       2 16816  29.00   M   15      1       2 16817  42.00   M   15      1       2 16818  36.00   F   10      1       2 16819  44.00   M   19      1       2 16820  20.00   M    7      1       2 16821  19.00   F    7      1       2 16822  24.00   M    7      1       2 16823   3.00   M    9      1       1 16824   8.00   F    9      1       1 16825  40.00   M   20      1       2 16826  30.00   M    9      1       2 16827  25.00   F    9      1       2 16828   3.00   F   14      1       1 16829  25.00   F    9      1       2 16830   7.00   F   21      1       1 16831  36.00   M    9      1       2 16832  28.00   F    9      1       2 16833  20.00   F    9      1       2 16834  20.00   M    9      1       2 16835  48.00   M   17      1       2 16836  25.00   F   17      1       2 16837  30.00   M   21      1       2 16838  11.00   F   21      1       1 16839  21.00   M    9      1       2 16840  15.00   M    9      1       1 16841  20.00   M    7      1       2 16842  43.00   F   17      1       2 16843  28.00   M   17      1       2 16844   6.00   M   17      1       1 16845  47.00   F   21      1       2 16846  38.00   M   21      1       2 16847   5.00   F   17      1       1 16848  33.00   M   21      1       2 16849   6.00   F   14      1       1 16850  40.00   F   17      1       2 16851  17.00   F   14      1       1 16852  17.00   M   17      1       1 16853  17.00   F   19      1       1 16854  28.00   F   19      1       2 16855   8.00   F    7      1       1 16856  20.00   M    9      1       2 16857  28.00   F   19      1       2 16858  30.00   M    9      1       2 16859   5.00   F    9      1       1 16860  35.00   M    9      1       2 16861  40.00   M   12      1       2 16862  16.00   F   17      1       1 16863  16.00   F    9      1       1 16864  12.00   F   11      1       1 16865  18.00   M   15      1       2 16866  22.00   M    9      1       2 16867  58.00   F   15      1       2 16868  13.00   M   17      1       1 16869  33.00   M    9      1       2 16870  40.00   F   14      1       2 16871  12.00   M   14      1       1 16872  15.00   M   20      1       1 16873  30.00   F   15      1       2 16874  40.00   F    9      1       2 16875  23.00   M    9      1       2 16876  25.00   F    9      1       2 16877  48.00   F   15      1       2 16878  19.00   M    7      1       2 16879  42.00   M    9      1       2 16880  38.00   M   15      1       2 16881  18.00   F    7      1       2 16882   1.30   M   17      1       1 16883  30.00   M   17      1       2 16884   9.00   F   17      1       1 16885  65.00   M   19      1       3 16886  26.00   F    6      1       2 16887  43.00   M    6      1       2 16888  44.00   M    7      1       2 16889  53.00   M    5      1       2 16890  48.00   F    5      1       2 16891  26.00   M    6      1       2 16892  50.00   M   10      1       2 16893  17.00   M    5      1       1 16894  15.00   M   10      1       1 16895  28.00   M    5      1       2 16896  24.00   M    2      1       2 16897  36.00   M    2      1       2 16898  44.00   M   15      1       2 16899  35.00   M   13      1       2 16900  34.00   F    2      1       2 16901  33.00   F    2      1       2 16902  20.00   F    2      1       2 16903  52.00   M    2      1       2 16904  48.00   M    5      1       2 16905  47.00   M    2      1       2 16906  51.00   M    2      1       2 16907   6.00   M    6      1       1 16908   1.00   F    6      1       1 16909  36.00   F    6      1       2 16910  39.00   F    6      1       2 16911  16.00   M    6      1       1 16912  54.00   M    3      1       2 16913  22.00   M   18      1       2 16914  20.00   F    5      1       2 16915  36.00   M    5      1       2 16916  43.00   M    5      1       2 16917  27.00   F   18      1       2 16918  30.00   F    5      1       2 16919  13.00   M    7      1       1 16920   5.00   M   12      1       1 16921  52.00   M   17      1       2 16922  32.00   M    8      1       2 16923  55.00   M    6      1       2 16924  47.00   M    6      1       2 16925  56.00   M    8      1       2 16926  34.00   M    5      1       2 16927  33.00   F    4      1       2 16928  10.00   M    4      1       1 16929  30.00   F   10      1       2 16930  46.00   M    5      1       2 16931  61.00   M   12      1       3 16932  13.00   M   10      1       1 16933  30.00   F   32      1       2 16934  20.00   M   12      1       2 16935  30.00   F   12      1       2 16936  18.00   F   16      1       2 16937   3.00   M   12      1       1 16938  20.00   F   16      1       2 16939  50.00   M   12      1       2 16940  38.00   M   12      1       2 16941  19.00   M   15      1       2 16942  25.00   F   12      1       2 16943  22.00   M   11      1       2 16944  30.00   M   10      1       2 16945  22.00   M   10      1       2 16946  40.00   M   14      1       2 16947  27.00   F   16      1       2 16948  43.00   M   15      1       2 16949  50.00   M   10      1       2 16950   9.00   F   10      1       1 16951  25.00   F   21      1       2 16952  36.00   M   12      1       2 16953  20.00   F   11      1       2 16954   9.00   M   14      1       1 16955  25.00   M   14      1       2 16956  26.00   M   12      1       2 16957  27.00   M   14      1       2 16958  47.00   M   10      1       2 16959  24.00   F   15      1       2 16960  18.00   M   15      1       2 16961  45.00   M   15      1       2 16962  23.00   F   12      1       2 16963  27.00   M   10      1       2 16964  35.00   M   12      1       2 16965  21.00   F   16      1       2 16966  60.00   M   10      1       3 16967  25.00   M   29      1       2 16968  15.00   M   12      1       1 16969  26.00   F   10      1       2 16970  30.00   F   14      1       2 16971  34.00   M   10      1       2 16972  57.00   F   14      1       2 16973  45.00   M   15      1       2 16974  20.00   M   32      1       2 16975  22.00   M   32      1       2 16976   4.00   M   29      1       1 16977  81.00   M   14      1       3 16978  30.00   M   12      1       2 16979  55.00   F   10      1       2 16980   9.00   F   10      1       1 16981  25.00   F   10      1       2 16982  19.00   M   15      1       2 16983  24.00   F   14      1       2 16984  23.00   M   10      1       2 16985  62.00   M   10      1       3 16986  25.00   F   14      1       2 16987  12.00   F   32      1       1 16988   6.00   F   16      1       1 16989  26.00   M   14      1       2 16990  21.00   M   14      1       2 16991  44.00   F   12      1       2 16992  18.00   F   12      1       2 16993  25.00   F   13      1       2 16994  46.00   M   10      1       2 16995  30.00   M   10      1       2 16996  38.00   M    7      1       2 16997  32.00   M   10      1       2 16998  46.00   M   16      1       2 16999  24.00   M    7      1       2 17000  28.00   M   10      1       2 17001  27.00   M   12      1       2 17002  22.00   M   16      1       2 17003  25.00   M    7      1       2 17004  24.00   M    9      1       2 17005  31.00   M   16      1       2 17006  29.00   M   16      1       2 17007  26.00   M   16      1       2 17008  26.00   M   10      1       2 17009  28.00   M   16      1       2 17010  28.00   M   11      1       2 17011  24.00   M    7      1       2 17012  31.00   M   16      1       2 17013  28.00   M   17      1       2 17014  30.00   M   10      1       2 17015  27.00   M   10      1       2 17016  27.00   M   11      1       2 17017  24.00   M   16      1       2 17018  76.00   F    7      1       3 17019  30.00   M   24      1       2 17020  12.00   M   24      1       1 17021  50.00   F    9      1       2 17022  52.00   F    9      1       2 17023  38.00   F   15      1       2 17024  42.00   M   15      1       2 17025  34.00   F   24      1       2 17026  24.00   F   15      1       2 17027  32.00   M   15      1       2 17028  50.00   F   24      1       2 17029  32.00   M   24      1       2 17030  60.00   M   12      1       3 17031  24.00   M   15      1       2 17032  54.00   F    9      1       2 17033  34.00   F   24      1       2 17034  38.00   F   24      1       2 17035  68.00   F   24      1       3 17036  40.00   M   13      1       2 17037  11.00   F    9      1       1 17038  42.00   F    7      1       2 17039  34.00   F    7      1       2 17040  40.00   M    7      1       2 17041  62.00   M    7      1       3 17042  58.00   M    7      1       2 17043  48.00   F    7      1       2 17044  66.00   F    7      1       3 17045  36.00   F    7      1       2 17046  38.00   F    7      1       2 17047  58.00   M    8      1       2 17048  48.00   M    8      1       2 17049  27.00   M    8      1       2 17050  25.00   M    8      1       2 17051  68.00   M   18      1       3 17052  56.00   F   16      1       2 17053  38.00   F   20      1       2 17054  60.00   M   15      1       3 17055  65.00   M   14      1       3 17056  50.00   M    8      1       2 17057  45.00   F    5      1       2 17058  23.00   F    5      1       2 17059  52.00   M    5      1       2 17060  60.00   M    5      1       3 17061  18.00   M   12      1       2 17062  33.00   M    8      1       2 17063  28.00   M   14      1       2 17064  20.00   F   14      1       2 17065  52.00   M   14      1       2 17066  60.00   M   13      1       3 17067  21.00   M    8      1       2 17068  18.00   M    8      1       2 17069  52.00   M   16      1       2 17070  16.00   F   20      1       1 17071  25.00   M    8      1       2 17072  35.00   F   20      1       2 17073  30.00   F   20      1       2 17074   7.00   M   10      1       1 17075  25.00   F   18      1       2 17076   4.00   F   13      1       1 17077   2.00   F   18      1       1 17078   8.00   F   10      1       1 17079  50.00   M   20      1       2 17080  43.00   F   13      1       2 17081  13.00   M   10      1       1 17082  28.00   F   20      1       2 17083  21.00   M   16      1       2 17084  33.00   M   13      1       2 17085  33.00   M   13      1       2 17086  10.00   M   20      1       1 17087   4.00   F   16      1       1 17088  35.00   F    8      1       2 17089  16.00   M    8      1       1 17090  20.00   M    8      1       2 17091  50.00   F   14      1       2 17092  30.00   M    8      1       2 17093  20.00   M    8      1       2 17094  37.00   M    8      1       2 17095  28.00   F   18      1       2 17096  32.00   M    8      1       2 17097  30.00   F   20      1       2 17098  60.00   M   13      1       3 17099  15.00   F   10      1       1 17100  10.00   F   13      1       1 17101  35.00   M    8      1       2 17102  26.00   F   18      1       2 17103  20.00   M    8      1       2 17104  15.00   F   11      1       1 17105  30.00   F   13      1       2 17106  42.00   M    6      1       2 17107  25.00   M   18      1       2 17108  25.00   M    6      1       2 17109  29.00   M   11      1       2 17110  10.00   F   18      1       1 17111  46.00   M   16      1       2 17112  10.00   F   16      1       1 17113  22.00   M    8      1       2 17114   4.00   M   16      1       1 17115  30.00   F   10      1       2 17116  20.00   M    8      1       2 17117  15.00   F   16      1       1 17118  60.00   M   16      1       3 17119  13.00   F   16      1       1 17120  30.00   F   11      1       2 17121  15.00   M   18      1       1 17122  27.00   F    8      1       2 17123  45.00   M    8      1       2 17124  39.00   F    8      1       2 17125  20.00   M    8      1       2 17126  40.00   M   13      1       2 17127  32.00   F   10      1       2 17128  18.00   M   10      1       2 17129  30.00   M    9      1       2 17130  30.00   M   16      1       2 17131  20.00   F   14      1       2 17132  30.00   M   11      1       2 17133  27.00   F   14      1       2 17134  52.00   M   11      1       2 17135  45.00   M   11      1       2 17136  50.00   M   14      1       2 17137  53.00   M   14      1       2 17138  36.00   M   11      1       2 17139  56.00   F   11      1       2 17140  22.00   M   11      1       2 17141  22.00   M   16      1       2 17142  60.00   M   16      1       3 17143  29.00   M    8      1       2 17144  60.00   M    6      1       3 17145  30.00   M    1      1       2 17146  40.00   M    5      1       2 17147  10.00   M   28      1       1 17148   8.00   M   13      1       1 17149  30.00   F   28      1       2 17150  22.00   F    7      1       2 17151  35.00   M   13      1       2 17152  32.00   M   17      1       2 17153  10.00   M   17      1       1 17154  48.00   M   13      1       2 17155  17.00   F   15      1       1 17156  35.00   M   17      1       2 17157  25.00   M   15      1       2 17158  22.00   F    7      1       2 17159  45.00   M   13      1       2 17160  38.00   M   13      1       2 17161  17.00   M    7      1       1 17162  19.00   M    7      1       2 17163  19.00   M    7      1       2 17164  15.00   M   15      1       1 17165  10.00   M   15      1       1 17166   7.00   F   15      1       1 17167  25.00   F   17      1       2 17168  50.00   F   13      1       2 17169  36.00   F   13      1       2 17170  27.00   M    7      1       2 17171  17.00   M    7      1       1 17172  20.00   M   15      1       2 17173  60.00   F   15      1       3 17174  60.00   F   22      1       3 17175  28.00   M   17      1       2 17176  27.00   M    8      1       2 17177   5.00   F   15      1       1 17178  30.00   M    9      1       2 17179   7.00   M    9      1       1 17180  13.00   F    9      1       1 17181  35.00   M   15      1       2 17182  24.00   F   15      1       2 17183  13.00   M   10      1       1 17184  20.00   F    5      1       2 17185   2.00   F    9      1       1 17186  26.00   F    9      1       2 17187  61.00   M   31      1       3 17188  29.00   F   10      1       2 17189  31.00   F   15      1       2 17190  26.00   F   10      1       2 17191   5.00   F    5      1       1 17192   3.00   F    5      1       1 17193  50.00   F    6      1       2 17194  25.00   M    7      1       2 17195  24.00   M    7      1       2 17196  23.00   F    9      1       2 17197   7.00   M    3      1       1 17198   2.00   F    6      1       1 17199   1.00   M    3      1       1 17200  10.00   M    3      1       1 17201  25.00   F    7      1       2 17202   7.00   M    9      1       1 17203  25.00   F   13      1       2 17204  15.00   F    7      1       1 17205  23.00   F   15      1       2 17206  30.00   F   10      1       2 17207  32.00   F   15      1       2 17208  56.00   F   15      1       2 17209  35.00   F   15      1       2 17210  37.00   M   15      1       2 17211  28.00   M    9      1       2 17212  42.00   F    4      1       2 17213  46.00   F    7      1       2 17214  34.00   M    7      1       2 17215  25.00   F    9      1       2 17216  32.00   M    7      1       2 17217  19.00   M    7      1       2 17218  54.00   F    7      1       2 17219  62.00   F    7      1       3 17220  43.00   M   11      1       2 17221  34.00   F   24      1       2 17222  19.00   M   14      1       2 17223  16.00   M    6      1       1 17224  23.00   M   23      1       2 17225  60.00   M   24      1       3 17226  37.00   M   23      1       2 17227  28.00   M   23      1       2 17228  43.00   M   23      1       2 17229  22.00   F   13      1       2 17230  20.00   F   24      1       2 17231  29.00   M   24      1       2 17232  54.00   M   24      1       2 17233  27.00   F   11      1       2 17234  62.00   F   15      1       3 17235  57.00   M   32      1       2 17236  24.00   F    8      1       2 17237  17.00   M   17      1       1 17238  25.00   F    7      1       2 17239  39.00   M   10      1       2 17240  40.00   F    7      1       2 17241  32.00   F   10      1       2 17242  23.00   M    5      1       2 17243  23.00   F    5      1       2 17244  21.00   F   10      1       2 17245  34.00   M   10      1       2 17246  24.00   M   10      1       2 17247  17.00   M   10      1       1 17248  32.00   M   14      1       2 17249  38.00   F   18      1       2 17250  40.00   F   14      1       2 17251  65.00   F   14      1       3 17252  30.00   F   18      1       2 17253   7.00   F   16      1       1 17254   7.00   M   16      1       1 17255  10.00   M   14      1       1 17256   9.00   F   11      1       1 17257  35.00   M   16      1       2 17258   5.00   F   14      1       1 17259  25.00   M   10      1       2 17260   6.00   F   10      1       1 17261  24.00   F   10      1       2 17262  53.00   F   10      1       2 17263  24.00   F   10      1       2 17264   7.00   M   11      1       1 17265  20.00   M   14      1       2 17266  20.00   M   14      1       2 17267  29.00   F   10      1       2 17268  25.00   F   10      1       2 17269  38.00   F   10      1       2 17270  42.00   F   14      1       2 17271  20.00   F   10      1       2 17272  15.00   F   16      1       1 17273  50.00   F   16      1       2 17274  28.00   M   16      1       2 17275  10.00   F   14      1       1 17276  25.00   F   16      1       2 17277  30.00   M   14      1       2 17278  42.00   M   16      1       2 17279  24.00   F   18      1       2 17280  30.00   F   12      1       2 17281   8.00   F   16      1       1 17282  25.00   M   10      1       2 17283  45.00   M   14      1       2 17284  10.00   M   16      1       1 17285  21.00   M   16      1       2 17286  25.00   M   14      1       2 17287  30.00   F   16      1       2 17288  31.00   M   14      1       2 17289  35.00   F   10      1       2 17290  28.00   F   14      1       2 17291  47.00   F   10      1       2 17292  23.00   F   14      1       2 17293  17.00   F   14      1       1 17294  17.00   F   14      1       1 17295  17.00   F   14      1       1 17296  20.00   M   14      1       2 17297  30.00   F   14      1       2 17298  46.00   M   14      1       2 17299  23.00   M   14      1       2 17300  17.00   M   24      1       1 17301  20.00   F   16      1       2 17302  14.00   F   16      1       1 17303  46.00   F   10      1       2 17304  22.00   M   14      1       2 17305  29.00   M   14      1       2 17306  28.00   F   16      1       2 17307  35.00   M    6      1       2 17308  13.00   F   10      1       1 17309   7.00   M   10      1       1 17310   2.00   F   13      1       1 17311  45.00   F   14      1       2 17312  33.00   M    5      1       2 17313  15.00   M    6      1       1 17314  31.00   M    7      1       2 17315  29.00   M    5      1       2 17316  59.00   M    7      1       2 17317  24.00   M   14      1       2 17318  35.00   M    5      1       2 17319  32.00   M   11      1       2 17320  58.00   M   22      1       2 17321  23.00   F   13      1       2 17322  22.00   F    7      1       2 17323  25.00   F    7      1       2 17324  55.00   F   22      1       2 17325  45.00   M   23      1       2 17326  37.00   M   12      1       2 17327  43.00   F    8      1       2 17328  43.00   M    8      1       2 17329  25.00   M   15      1       2 17330  25.00   M   15      1       2 17331  26.00   M   15      1       2 17332   9.00   F   14      1       1 17333  27.00   M    9      1       2 17334  46.00   M    9      1       2 17335  52.00   M    9      1       2 17336  10.00   M    9      1       1 17337  47.00   M    9      1       2 17338  35.00   F   15      1       2 17339  66.00   M   15      1       3 17340   9.00   M    9      1       1 17341  52.00   M    9      1       2 17342  25.00   F    9      1       2 17343  27.00   F    7      1       2 17344  38.00   M    7      1       2 17345  38.00   F    7      1       2 17346  42.00   M    7      1       2 17347  18.00   F    7      1       2 17348  62.00   F    7      1       3 17349  66.00   M    7      1       3 17350   4.00   M    7      1       1 17351  64.00   M    9      1       3 17352  45.00   F    6      1       2 17353  58.00   M   14      1       2 17354  16.00   F    6      1       1 17355  22.00   F    9      1       2 17356  46.00   M   15      1       2 17357  22.00   M    4      1       2 17358  24.00   F   16      1       2 17359   2.00   F   16      1       1 17360  25.00   M    9      1       2 17361  23.00   M    9      1       2 17362  21.00   M    9      1       2 17363   0.60   F    8      1       1 17364  21.00   F    8      1       2 17365   3.00   F    8      1       1 17366  20.00   F   31      1       2 17367   5.00   M   17      1       1 17368  60.00   M   24      1       3 17369  14.00   F    8      1       1 17370  26.00   F   11      1       2 17371  29.00   F    8      1       2 17372   6.00   F    8      1       1 17373  40.00   M   14      1       2 17374  35.00   F    8      1       2 17375  14.00   M    4      1       1 17376   5.00   M    9      1       1 17377  32.00   M   20      1       2 17378  63.00   F    8      1       3 17379  33.00   M    5      1       2 17380  24.00   M    5      1       2 17381  40.00   F    5      1       2 17382  65.00   M    8      1       3 17383  56.00   F    5      1       2 17384  25.00   M    5      1       2 17385  28.00   M    5      1       2 17386  10.00   M   16      1       1 17387  35.00   F    5      1       2 17388   7.00   F   10      1       1 17389  33.00   F   10      1       2 17390  28.00   M   10      1       2 17391   4.00   F   10      1       1 17392  23.00   M   10      1       2 17393  22.00   F   10      1       2 17394  12.00   F   10      1       1 17395  11.00   F   10      1       1 17396  25.00   M   10      1       2 17397  13.00   F   10      1       1 17398  27.00   M   10      1       2 17399  30.00   M   10      1       2 17400  20.00   M   25      1       2 17401  65.00   M   12      1       3 17402  59.00   M   14      1       2 17403  11.00   F    5      1       1 17404  35.00   M    3      1       2 17405  36.00   F   14      1       2 17406  34.00   F    5      1       2 17407  25.00   F    3      1       2 17408  23.00   F    9      1       2 17409  30.00   F    7      1       2 17410  30.00   M    3      1       2 17411  47.00   M   12      1       2 17412   5.00   F    1      1       1 17413  35.00   F    1      1       2 17414  33.00   M    4      1       2 17415  39.00   M    1      1       2 17416  60.00   M    2      1       3 17417  60.00   M    2      1       3 17418  16.00   M    1      1       1 17419  72.00   F    2      1       3 17420  41.00   F    1      1       2 17421  30.00   M    2      1       2 17422  47.00   M   14      1       2 17423  30.00   M    1      1       2 17424  34.00   M   10      1       2 17425  20.00   M   12      1       2 17426  21.00   F   10      1       2 17427  34.00   M   10      1       2 17428  24.00   M   10      1       2 17429  56.00   M    9      1       2 17430  34.00   F   11      1       2 17431  72.00   F   21      1       3 17432  37.00   M    8      1       2 17433  40.00   M   13      1       2 17434  49.00   M   11      1       2 17435  63.00   M   12      1       3 17436  62.00   F   11      1       3 17437  34.00   M   13      1       2 17438   0.50   M    8      1       1 17439  50.00   M   13      1       2 17440  46.00   M    8      1       2 17441  33.00   F    8      1       2 17442  12.00   F    8      1       1 17443  33.00   M    8      1       2 17444  75.00   F    9      1       3 17445  35.00   M    9      1       2 17446  28.00   M    8      1       2 17447  32.00   F   13      1       2 17448  38.00   M    4      1       2 17449  22.00   M    4      1       2 17450  18.00   M    4      1       2 17451  18.00   M    6      1       2 17452  17.00   M    4      1       1 17453  15.00   M    6      1       1 17454  18.00   M    4      1       2 17455  24.00   F    4      1       2 17456  54.00   F   13      1       2 17457  38.00   F   10      1       2 17458  52.00   M   21      1       2 17459  52.00   M    6      1       2 17460  43.00   M   13      1       2 17461  36.00   M   10      1       2 17462  43.00   M    7      1       2 17463  26.00   M   13      1       2 17464  25.00   M   13      1       2 17465  30.00   M   10      1       2 17466  37.00   M   13      1       2 17467  45.00   M   13      1       2 17468  28.00   M    7      1       2 17469  29.00   M    7      1       2 17470  27.00   M   13      1       2 17471  45.00   M   10      1       2 17472   2.00   M   23      1       1 17473  29.00   M   19      1       2 17474  20.00   M   13      1       2 17475  23.00   M   13      1       2 17476  23.00   M   21      1       2 17477  35.00   M   13      1       2 17478  28.00   M   13      1       2 17479  52.00   M   13      1       2 17480  10.00   M    9      1       1 17481  55.00   M    9      1       2 17482  31.00   M    7      1       2 17483  26.00   F    8      1       2 17484  16.00   M   11      1       1 17485  54.00   M   11      1       2 17486  36.00   F   11      1       2 17487  60.00   F   11      1       3 17488  36.00   M   10      1       2 17489  23.00   F   10      1       2 17490  42.00   F   11      1       2 17491  34.00   M   11      1       2 17492   7.00   M   11      1       1 17493  12.00   F   11      1       1 17494  30.00   F   11      1       2 17495  11.00   F   11      1       1 17496  23.00   F   11      1       2 17497  25.00   F   30      1       2 17498  28.00   F   11      1       2 17499  38.00   F   12      1       2 17500  27.00   M    9      1       2 17501  24.00   M    9      1       2 17502   7.00   M    9      1       1 17503  34.00   F    9      1       2 17504   4.00   M    9      1       1 17505  35.00   F    9      1       2 17506   4.00   F    9      1       1 17507  25.00   M    9      1       2 17508  55.00   M    9      1       2 17509  56.00   F    9      1       2 17510  60.00   M   13      1       3 17511  33.00   M    1      1       2 17512  34.00   F    1      1       2 17513  33.00   F    1      1       2 17514  44.00   M    1      1       2 17515  52.00   M    1      1       2 17516  17.00   F    1      1       1 17517   8.00   F    1      1       1 17518  22.00   M    1      1       2 17519  40.00   F    1      1       2 17520  48.00   M    1      1       2 17521  43.00   M    1      1       2 17522   9.00   F    1      1       1 17523  47.00   M    1      1       2 17524   9.00   M    1      1       1 17525  42.00   M    2      1       2 17526  32.00   M    6      1       2 17527  28.00   F    2      1       2 17528  34.00   F    2      1       2 17529  46.00   M   13      1       2 17530  55.00   M    1      1       2 17531  71.00   M   12      1       3 17532  28.00   M   12      1       2 17533  27.00   F   21      1       2 17534  25.00   F   12      1       2 17535  46.00   M   13      1       2 17536  20.00   F   12      1       2 17537  73.00   F   22      1       3 17538  33.00   M    6      1       2 17539  54.00   F   21      1       2 17540  32.00   M    9      1       2 17541  76.00   M   21      1       3 17542  48.00   F   21      1       2 17543  30.00   M   21      1       2 17544  24.00   F   21      1       2 17545  19.00   F   21      1       2 17546  25.00   M   21      1       2 17547  64.00   F   12      1       3 17548  40.00   M   12      1       2 17549  24.00   F   21      1       2 17550  45.00   M   12      1       2 17551  22.00   F   12      1       2 17552  42.00   F   21      1       2 17553  53.00   F    9      1       2 17554  46.00   F    7      1       2 17555  41.00   M   12      1       2 17556  59.00   F    8      1       2 17557  41.00   M    9      1       2 17558  32.00   M    9      1       2 17559  35.00   M   10      1       2 17560  31.00   M   10      1       2 17561  60.00   F   10      1       3 17562  27.00   F   10      1       2 17563  12.00   F   10      1       1 17564   8.00   F   10      1       1 17565   3.00   M   10      1       1 17566  23.00   F   14      1       2 17567  25.00   M   14      1       2 17568  23.00   M   14      1       2 17569  55.00   F   26      1       2 17570  25.00   M   11      1       2 17571  24.00   F    8      1       2 17572  21.00   M    8      1       2 17573  21.00   M   11      1       2 17574  34.00   M    8      1       2 17575  38.00   M    8      1       2 17576  29.00   F   22      1       2 17577  50.00   F   14      1       2 17578   3.00   F   11      1       1 17579  34.00   M    8      1       2 17580  59.00   M    8      1       2 17581  30.00   F    6      1       2 17582  15.00   M    6      1       1 17583  32.00   M    6      1       2 17584  30.00   M   11      1       2 17585  22.00   M    6      1       2 17586  34.00   M   11      1       2 17587  34.00   M   15      1       2 17588  40.00   M    6      1       2 17589  41.00   M    6      1       2 17590  33.00   M    9      1       2 17591  48.00   M   16      1       2 17592  31.00   M    9      1       2 17593  20.00   F    6      1       2 17594  27.00   M    6      1       2 17595  31.00   M   15      1       2 17596  32.00   M    6      1       2 17597  27.00   M   11      1       2 17598  35.00   M   15      1       2 17599  51.00   M    6      1       2 17600  20.00   F   20      1       2 17601   9.00   F   14      1       1 17602  36.00   M    6      1       2 17603  67.00   F    9      1       3 17604  44.00   M   18      1       2 17605  30.00   F    6      1       2 17606  25.00   M   15      1       2 17607  24.00   M    9      1       2 17608  44.00   M    6      1       2 17609  32.00   M    6      1       2 17610  38.00   M    6      1       2 17611  50.00   M    6      1       2 17612  33.00   M    9      1       2 17613  31.00   M   18      1       2 17614  21.00   M    8      1       2 17615  26.00   M    6      1       2 17616  32.00   F   11      1       2 17617  22.00   M    9      1       2 17618  32.00   M    9      1       2 17619  29.00   M    6      1       2 17620  29.00   M    8      1       2 17621  29.00   M   11      1       2 17622  31.00   M    6      1       2 17623  25.00   M    6      1       2 17624  47.00   M    6      1       2 17625  42.00   M    8      1       2 17626  29.00   M    6      1       2 17627  54.00   M   11      1       2 17628  44.00   M   14      1       2 17629  43.00   M    6      1       2 17630  31.00   M    6      1       2 17631  46.00   M   14      1       2 17632  61.00   M   11      1       3 17633  23.00   M    6      1       2 17634  24.00   M    6      1       2 17635  22.00   M   11      1       2 17636  35.00   M    9      1       2 17637  60.00   F   11      1       3 17638   3.00   F    9      1       1 17639  24.00   M   14      1       2 17640  36.00   M    6      1       2 17641  31.00   M   11      1       2 17642  43.00   M   15      1       2 17643  50.00   M    9      1       2 17644  35.00   M   15      1       2 17645  51.00   M   15      1       2 17646  21.00   M   14      1       2 17647  20.00   F   15      1       2 17648  20.00   F    6      1       2 17649  21.00   M   11      1       2 17650  29.00   F   15      1       2 17651  42.00   M   15      1       2 17652  30.00   M    6      1       2 17653  42.00   M    8      1       2 17654  34.00   M    8      1       2 17655  35.00   M   11      1       2 17656  50.00   M   11      1       2 17657  63.00   F    6      1       3 17658  39.00   F   15      1       2 17659  58.00   F   15      1       2 17660  24.00   M   15      1       2 17661  24.00   F    9      1       2 17662  18.00   M    6      1       2 17663  40.00   M    6      1       2 17664  10.00   M    6      1       1 17665  46.00   M    8      1       2 17666  36.00   F   15      1       2 17667  55.00   M   14      1       2 17668  43.00   F   14      1       2 17669  42.00   M   11      1       2 17670  19.00   M   11      1       2 17671  45.00   M   15      1       2 17672  45.00   M   11      1       2 17673  31.00   F   15      1       2 17674  31.00   F   15      1       2 17675  24.00   M    7      1       2 17676  23.00   M    9      1       2 17677  34.00   F    9      1       2 17678  55.00   M    8      1       2 17679   4.00   M   10      1       1 17680  48.00   F   12      1       2 17681  29.00   F   10      1       2 17682  27.00   M   11      1       2 17683  10.00   F   12      1       1 17684  28.00   F   13      1       2 17685  23.00   M   12      1       2 17686  34.00   M   12      1       2 17687  33.00   M   12      1       2 17688  31.00   F   12      1       2 17689  58.00   F    8      1       2 17690  27.00   M    8      1       2 17691   2.00   M   12      1       1 17692   5.00   F   12      1       1 17693  31.00   F   16      1       2 17694  20.00   M    8      1       2 17695  19.00   M   11      1       2 17696  44.00   F   11      1       2 17697  46.00   M   11      1       2 17698  49.00   M   12      1       2 17699  54.00   M    8      1       2 17700   5.00   F    8      1       1 17701  24.00   F   12      1       2 17702  21.00   F   12      1       2 17703  40.00   M    8      1       2 17704  35.00   M    8      1       2 17705  38.00   F    8      1       2 17706  62.00   F   22      1       3 17707  19.00   M   20      1       2 17708  18.00   M   20      1       2 17709  63.00   M   20      1       3 17710  57.00   M   20      1       2 17711  54.00   M   20      1       2 17712  36.00   M   20      1       2 17713  20.00   M   12      1       2 17714  46.00   F    8      1       2 17715  40.00   F   11      1       2 17716   1.00   M   10      1       1 17717  68.00   M   20      1       3 17718   4.00   F   20      1       1 17719  28.00   F   20      1       2 17720  30.00   F   20      1       2 17721  38.00   F   10      1       2 17722  36.00   M   14      1       2 17723  85.00   F   13      1       3 17724  79.00   M   13      1       3 17725  17.00   F   13      1       1 17726  16.00   M    7      1       1 17727  24.00   M    7      1       2 17728  12.00   F    7      1       1 17729  23.00   M   13      1       2 17730  45.00   M    9      1       2 17731  12.00   M    9      1       1 17732  33.00   M    9      1       2 17733  39.00   M   12      1       2 17734  35.00   F   12      1       2 17735  50.00   M    9      1       2 17736  21.00   M   11      1       2 17737  46.00   M    6      1       2 17738  55.00   M    6      1       2 17739  58.00   M    7      1       2 17740   9.00   M    6      1       1 17741  23.00   M    8      1       2 17742  28.00   M    6      1       2 17743  31.00   M    6      1       2 17744  25.00   M   12      1       2 17745  29.00   M    8      1       2 17746  24.00   M   12      1       2 17747  30.00   M    9      1       2 17748  40.00   M   12      1       2 17749  46.00   M    8      1       2 17750  28.00   M   12      1       2 17751  55.00   F    8      1       2 17752  30.00   F   12      1       2 17753   8.00   F   12      1       1 17754   1.00   M    6      1       1 17755  27.00   F    6      1       2 17756  22.00   F    6      1       2 17757   4.00   M   12      1       1 17758  35.00   M   20      1       2 17759  25.00   F    6      1       2 17760  61.00   F   12      1       3 17761   1.00   M    6      1       1 17762  29.00   F   12      1       2 17763  24.00   M   12      1       2 17764   2.00   F    6      1       1 17765   8.00   M   12      1       1 17766   2.00   M   12      1       1 17767  20.00   F    6      1       2 17768  12.00   M   12      1       1 17769   4.00   F   20      1       1 17770   1.00   M   20      1       1 17771  60.00   M   22      1       3 17772  42.00   M   10      1       2 17773  35.00   M    9      1       2 17774   7.00   F   14      1       1 17775   9.00   F   24      1       1 17776  29.00   F   14      1       2 17777  39.00   M   10      1       2 17778  50.00   F    8      1       2 17779  25.00   M   12      1       2 17780  37.00   F   14      1       2 17781  24.00   M   25      1       2 17782  15.00   M    9      1       1 17783   5.00   M   10      1       1 17784  35.00   M    5      1       2 17785  19.00   M   12      1       2 17786  75.00   M   12      1       3 17787  50.00   M   16      1       2 17788  21.00   F   14      1       2 17789  14.00   M   12      1       1 17790  35.00   F   20      1       2 17791  37.00   M   12      1       2 17792  50.00   F   14      1       2 17793   0.25   F   12      1       1 17794   3.00   M   12      1       1 17795   5.00   M   12      1       1 17796  24.00   M   12      1       2 17797  28.00   M   12      1       2 17798  12.00   F   12      1       1 17799   6.00   M   12      1       1 17800  10.00   M   12      1       1 17801  30.00   F   14      1       2 17802   7.00   M   12      1       1 17803  19.00   M   12      1       2 17804   8.00   F   12      1       1 17805  10.00   M   14      1       1 17806  45.00   M   12      1       2 17807  12.00   F   12      1       1 17808  13.00   F   12      1       1 17809  33.00   F   12      1       2 17810  35.00   M   12      1       2 17811  25.00   F   12      1       2 17812  22.00   M   12      1       2 17813  28.00   M   12      1       2 17814  42.00   M   12      1       2 17815  30.00   M   12      1       2 17816  38.00   F   14      1       2 17817  13.00   M   14      1       1 17818  30.00   F   12      1       2 17819  38.00   F   12      1       2 17820   5.00   F   14      1       1 17821  25.00   F   12      1       2 17822   6.00   M   12      1       1 17823  38.00   M   12      1       2 17824   3.00   F   12      1       1 17825  18.00   F   12      1       2 17826  46.00   F   14      1       2 17827  30.00   F   12      1       2 17828  12.00   M   12      1       1 17829  35.00   M   12      1       2 17830  14.00   M   12      1       1 17831  16.00   F   12      1       1 17832  32.00   F   12      1       2 17833  35.00   F   12      1       2 17834  11.00   M   12      1       1 17835  12.00   F   12      1       1 17836  25.00   M   14      1       2 17837  35.00   M   19      1       2 17838  46.00   M   14      1       2 17839  80.00   F   19      1       3 17840  11.00   M   13      1       1 17841  50.00   M   15      1       2 17842  55.00   F   12      1       2 17843  60.00   F   12      1       3 17844  75.00   M   14      1       3 17845  38.00   F   13      1       2 17846   4.00   M   13      1       1 17847  35.00   F    8      1       2 17848  26.00   F    8      1       2 17849  17.00   M    8      1       1 17850  28.00   F    8      1       2 17851  38.00   M    8      1       2 17852  35.00   F    8      1       2 17853  28.00   F    8      1       2 17854  19.00   M    8      1       2 17855   5.00   M    8      1       1 17856  50.00   M    8      1       2 17857  33.00   M    8      1       2 17858  30.00   M    8      1       2 17859  32.00   M    8      1       2 17860   8.00   F    8      1       1 17861  27.00   M    8      1       2 17862  26.00   F    8      1       2 17863  28.00   M   10      1       2 17864  32.00   M    8      1       2 17865  30.00   M    8      1       2 17866   6.00   F    8      1       1 17867   5.00   M    8      1       1 17868  16.00   F   10      1       1 17869  14.00   M   10      1       1 17870  46.00   F    8      1       2 17871  48.00   M    8      1       2 17872  17.00   M   10      1       1 17873  26.00   M    8      1       2 17874  16.00   F    8      1       1 17875  11.00   F    8      1       1 17876  45.00   M    8      1       2 17877  14.00   M    8      1       1 17878  36.00   M    8      1       2 17879  18.00   M    8      1       2 17880  38.00   M    8      1       2 17881  36.00   M   10      1       2 17882  21.00   M    8      1       2 17883   5.00   M    8      1       1 17884  36.00   M   10      1       2 17885  27.00   M    8      1       2 17886  14.00   M   10      1       1 17887  14.00   F   10      1       1 17888  22.00   F   10      1       2 17889  40.00   M   10      1       2 17890   9.00   M   24      1       1 17891  24.00   F   10      1       2 17892  28.00   F   10      1       2 17893  28.00   F   10      1       2 17894  16.00   M   10      1       1 17895  11.00   F   17      1       1 17896  32.00   M   16      1       2 17897   6.00   M   10      1       1 17898  27.00   F   10      1       2 17899  47.00   F   17      1       2 17900  19.00   F   10      1       2 17901  16.00   M   10      1       1 17902  20.00   F   10      1       2 17903  42.00   M   10      1       2 17904  20.00   F   10      1       2 17905  38.00   M   10      1       2 17906  28.00   F   11      1       2 17907  45.00   M   11      1       2 17908  32.00   F   24      1       2 17909  23.00   F   11      1       2 17910  70.00   F   22      1       3 17911  33.00   M   11      1       2 17912  25.00   F   10      1       2 17913   3.00   M   11      1       1 17914  28.00   M   11      1       2 17915  30.00   F   11      1       2 17916  62.00   F   13      1       3 17917  35.00   F   11      1       2 17918  30.00   F   12      1       2 17919  71.00   M   13      1       3 17920  22.00   M   13      1       2 17921  55.00   M   13      1       2 17922  30.00   M    6      1       2 17923   8.00   F   11      1       1 17924   2.00   F   11      1       1 17925  33.00   F   11      1       2 17926  27.00   M   11      1       2 17927  25.00   F   11      1       2 17928  10.00   F   11      1       1 17929   2.00   M   11      1       1 17930   1.00   F   11      1       1 17931  35.00   M   11      1       2 17932  30.00   F   11      1       2 17933  18.00   F   11      1       2 17934  19.00   F   16      1       2 17935   2.00   M   11      1       1 17936  40.00   M   11      1       2 17937  18.00   M   11      1       2 17938  30.00   F   13      1       2 17939  43.00   M   16      1       2 17940  40.00   F   11      1       2 17941  16.00   F   11      1       1 17942  15.00   F   11      1       1 17943   8.00   F   11      1       1 17944  51.00   M   11      1       2 17945  35.00   M   11      1       2 17946  18.00   M   11      1       2 17947  28.00   M   11      1       2 17948  22.00   F   11      1       2 17949  40.00   M   11      1       2 17950  30.00   F   16      1       2 17951  29.00   M   11      1       2 17952  25.00   F   11      1       2 17953   2.00   M   24      1       1 17954   2.00   M    6      1       1 17955   4.00   M   26      1       1 17956  48.00   F   14      1       2 17957  30.00   F    6      1       2 17958   5.00   M    6      1       1 17959  60.00   F    6      1       3 17960  36.00   M   14      1       2 17961  37.00   M    2      1       2 17962  44.00   M    4      1       2 17963  40.00   M    1      1       2 17964  24.00   M    1      1       2 17965  59.00   M    1      1       2 17966  23.00   M    1      1       2 17967  24.00   F    5      1       2 17968  33.00   F    2      1       2 17969  40.00   M    1      1       2 17970  23.00   F    5      1       2 17971  62.00   M    5      1       3 17972  24.00   F    2      1       2 17973   6.00   F    1      1       1 17974  35.00   M    7      1       2 17975   3.00   M    5      1       1 17976  62.00   M    8      1       3 17977  26.00   M   12      1       2 17978  54.00   F    7      1       2 17979  28.00   F    7      1       2 17980  25.00   M    7      1       2 17981  46.00   M   25      1       2 17982  54.00   M    7      1       2 17983  30.00   F   11      1       2 17984  46.00   F   11      1       2 17985  49.00   F   21      1       2 17986  36.00   F   11      1       2 17987   8.00   M   11      1       1 17988  20.00   M    7      1       2 17989  20.00   M   15      1       2 17990  39.00   F    7      1       2 17991  35.00   F    9      1       2 17992  32.00   M    3      1       2 17993  60.00   M    5      1       3 17994  24.00   F    5      1       2 17995  12.00   M    5      1       1 17996  26.00   F    5      1       2 17997  45.00   M   13      1       2 17998  15.00   M   13      1       1 17999  30.00   F   13      1       2 18000  47.00   M   13      1       2 18001  62.00   M    8      1       3 18002  30.00   M   12      1       2 18003  25.00   M    1      1       2 18004  26.00   F    9      1       2 18005  45.00   M    7      1       2 18006  35.00   F   11      1       2 18007  12.00   M    7      1       1 18008  65.00   M    7      1       3 18009  38.00   M    7      1       2 18010  23.00   M    9      1       2 18011  11.00   F   21      1       1 18012  73.00   M   19      1       3 18013  40.00   F    9      1       2 18014  34.00   M    9      1       2 18015  38.00   M   24      1       2 18016  28.00   F   19      1       2 18017  73.00   M   19      1       3 18018  37.00   M   20      1       2 18019  56.00   F   20      1       2 18020  38.00   M   11      1       2 18021  36.00   M   19      1       2 18022  42.00   M   19      1       2 18023  25.00   M   11      1       2 18024  23.00   M   11      1       2 18025  53.00   M   20      1       2 18026  46.00   F   11      1       2 18027  33.00   M   19      1       2 18028  67.00   M    7      1       3 18029  68.00   F   21      1       3 18030  29.00   F   11      1       2 18031  51.00   F   11      1       2 18032  26.00   F   19      1       2 18033   7.00   F   11      1       1 18034  45.00   M    9      1       2 18035  36.00   M   10      1       2 18036  85.00   F   14      1       3 18037  79.00   M   10      1       3 18038  17.00   F   10      1       1 18039  21.00   M   12      1       2 18040  35.00   M   22      1       2 18041  35.00   M   12      1       2 18042  34.00   F    5      1       2 18043  38.00   M   11      1       2 18044  53.00   M   18      1       2 18045  52.00   M    7      1       2 18046  50.00   M    7      1       2 18047  13.00   F    2      1       1 18048   4.00   M    9      1       1 18049  28.00   M    4      1       2 18050  42.00   M    2      1       2 18051  23.00   M    2      1       2 18052  39.00   M    4      1       2 18053  28.00   F    1      1       2 18054  36.00   M    1      1       2 18055  34.00   M    1      1       2 18056  33.00   F    4      1       2 18057  32.00   M    1      1       2 18058  41.00   M    1      1       2 18059  37.00   F    4      1       2 18060  19.00   M    1      1       2 18061  47.00   M    2      1       2 18062  10.00   M    9      1       1 18063  35.00   F    9      1       2 18064  61.00   F    9      1       3 18065  65.00   F    9      1       3 18066  32.00   M   11      1       2 18067  24.00   F   27      1       2 18068  41.00   M   11      1       2 18069  33.00   F   21      1       2 18070  50.00   M   11      1       2 18071  21.00   M   13      1       2 18072  62.00   M   11      1       3 18073  28.00   F   11      1       2 18074  10.00   F   11      1       1 18075   6.00   F   11      1       1 18076   4.00   F   11      1       1 18077  26.00   F   13      1       2 18078   6.00   F   11      1       1 18079   8.00   F   11      1       1 18080  29.00   M   13      1       2 18081  25.00   F   11      1       2 18082  21.00   F   11      1       2 18083  17.00   F   13      1       1 18084  18.00   F   21      1       2 18085  22.00   F    9      1       2 18086  28.00   M    9      1       2 18087  26.00   F    9      1       2 18088  10.00   M    7      1       1 18089  23.00   F    9      1       2 18090  17.00   M    9      1       1 18091  30.00   M    9      1       2 18092  25.00   F    9      1       2 18093  41.00   M    9      1       2 18094  36.00   F    9      1       2 18095  12.00   M    7      1       1 18096  17.00   M    9      1       1 18097  65.00   M    9      1       3 18098  26.00   F    9      1       2 18099  14.00   F    9      1       1 18100   8.00   M    7      1       1 18101  14.00   F    9      1       1 18102  48.00   M    9      1       2 18103  36.00   M    9      1       2 18104  43.00   M    9      1       2 18105  27.00   M    9      1       2 18106  27.00   F   13      1       2 18107  60.00   F   21      1       3 18108  56.00   F   21      1       2 18109  63.00   M   21      1       3 18110  44.00   M   21      1       2 18111  18.00   F    9      1       2 18112  33.00   M   12      1       2 18113  22.00   F   12      1       2 18114   6.00   M   12      1       1 18115  24.00   F    9      1       2 18116  16.00   M   10      1       1 18117   2.00   M    5      1       1 18118  60.00   M   11      1       3 18119   2.00   F    5      1       1 18120  44.00   M    5      1       2 18121  45.00   M    5      1       2 18122  66.00   M   12      1       3 18123  34.00   M   13      1       2 18124  55.00   M   11      1       2 18125  46.00   M   11      1       2 18126  43.00   M   11      1       2 18127  43.00   M   13      1       2 18128   5.00   F   12      1       1 18129  34.00   M    9      1       2 18130  26.00   M    4      1       2 18131  25.00   F   10      1       2 18132  23.00   F   10      1       2 18133  58.00   M   18      1       2 18134  56.00   F   10      1       2 18135  26.00   F    4      1       2 18136  25.00   M    6      1       2 18137  44.00   M    6      1       2 18138  25.00   M    6      1       2 18139  22.00   M   11      1       2 18140  23.00   M    6      1       2 18141  24.00   M   18      1       2 18142  39.00   M   10      1       2 18143  42.00   M   10      1       2 18144  24.00   M    6      1       2 18145  47.00   M   10      1       2 18146  23.00   M   10      1       2 18147  23.00   M   10      1       2 18148  26.00   M   10      1       2 18149  23.00   M    6      1       2 18150  25.00   M    6      1       2 18151  39.00   M   10      1       2 18152  24.00   M    6      1       2 18153  51.00   F   10      1       2 18154  29.00   F   10      1       2 18155  26.00   M    7      1       2 18156  23.00   M   22      1       2 18157  23.00   M   22      1       2 18158  23.00   M   14      1       2 18159  41.00   M    8      1       2 18160   9.00   M    7      1       1 18161  50.00   M    8      1       2 18162   2.00   M    8      1       1 18163  36.00   M   12      1       2 18164  10.00   F    8      1       1 18165  57.00   F   10      1       2 18166  27.00   M    8      1       2 18167  25.00   M   10      1       2 18168  50.00   M   18      1       2 18169  72.00   M   10      1       3 18170  63.00   M   10      1       3 18171  78.00   M   10      1       3 18172  60.00   M   10      1       3 18173  55.00   F   18      1       2 18174  17.00   M   10      1       1 18175  40.00   M   10      1       2 18176  80.00   F   10      1       3 18177  30.00   F   10      1       2 18178  72.00   M    8      1       3 18179   8.00   M   10      1       1 18180   8.00   F   10      1       1 18181  58.00   M   10      1       2 18182  26.00   M   10      1       2 18183  15.00   F   10      1       1 18184  27.00   F   11      1       2 18185  45.00   F   10      1       2 18186  51.00   M   10      1       2 18187  15.00   M   10      1       1 18188  44.00   F   10      1       2 18189  65.00   F    6      1       3 18190  11.00   M   10      1       1 18191  35.00   F   10      1       2 18192  13.00   M   10      1       1 18193  24.00   F    5      1       2 18194   8.00   F    6      1       1 18195  36.00   F   12      1       2 18196  17.00   M   23      1       1 18197  26.00   F   23      1       2 18198  30.00   F   18      1       2 18199  44.00   M    8      1       2 18200  35.00   F    8      1       2 18201  40.00   M    5      1       2 18202  24.00   F    8      1       2 18203  27.00   M   14      1       2 18204  27.00   F    8      1       2 18205  63.00   M    8      1       3 18206  35.00   F    8      1       2 18207  19.00   M    8      1       2 18208  27.00   M    8      1       2 18209  25.00   F    8      1       2 18210  40.00   F   14      1       2 18211  15.00   M   14      1       1 18212  30.00   M    8      1       2 18213  25.00   F   10      1       2 18214  40.00   F    8      1       2 18215  32.00   M   10      1       2 18216  28.00   F   17      1       2 18217  28.00   F   12      1       2 18218  56.00   F   10      1       2 18219  16.00   F    8      1       1 18220  51.00   M   10      1       2 18221  30.00   M   10      1       2 18222  28.00   M   10      1       2 18223  35.00   F   10      1       2 18224  72.00   M   17      1       3 18225   3.00   M    7      1       1 18226  55.00   M   10      1       2 18227  36.00   M   10      1       2 18228  36.00   F   17      1       2 18229  22.00   M   11      1       2 18230  16.00   M   11      1       1 18231  37.00   F   11      1       2 18232  35.00   F   11      1       2 18233  22.00   F   11      1       2 18234   2.00   F   11      1       1 18235   2.00   F   12      1       1 18236  49.00   M   11      1       2 18237  43.00   F   11      1       2 18238  22.00   M   11      1       2 18239  48.00   M   11      1       2 18240  27.00   M   11      1       2 18241  26.00   M   11      1       2 18242  20.00   M   11      1       2 18243  35.00   M   17      1       2 18244   4.00   F   11      1       1 18245  28.00   M   15      1       2 18246  21.00   F   15      1       2 18247  21.00   F   12      1       2 18248  27.00   M   11      1       2 18249  33.00   M   11      1       2 18250  20.00   M   11      1       2 18251  55.00   F   11      1       2 18252   5.00   M   11      1       1 18253   8.00   F   11      1       1 18254   1.00   F   11      1       1 18255  25.00   F   11      1       2 18256  15.00   M   11      1       1 18257  43.00   F   11      1       2 18258  17.00   M   11      1       1 18259  36.00   F   11      1       2 18260  18.00   F   11      1       2 18261  42.00   M   10      1       2 18262  20.00   M   11      1       2 18263  44.00   M    8      1       2 18264  19.00   M    8      1       2 18265  21.00   M    8      1       2 18266  28.00   M   20      1       2 18267  40.00   M   11      1       2 18268  30.00   F   12      1       2 18269  46.00   M   13      1       2 18270   7.00   M    8      1       1 18271  12.00   F   10      1       1 18272  45.00   M   11      1       2 18273  40.00   F   11      1       2 18274  17.00   F   11      1       1 18275  15.00   M   11      1       1 18276  20.00   F    6      1       2 18277  30.00   F    6      1       2 18278   6.00   F    6      1       1 18279  29.00   M   19      1       2 18280  26.00   M   13      1       2 18281   4.00   F   16      1       1 18282  29.00   M   12      1       2 18283  21.00   M    4      1       2 18284  22.00   M   12      1       2 18285  14.00   F    6      1       1 18286  34.00   M    6      1       2 18287  38.00   M    6      1       2 18288  22.00   F    6      1       2 18289  34.00   M   12      1       2 18290  42.00   M    6      1       2 18291  35.00   F   12      1       2 18292  26.00   F    6      1       2 18293  37.00   M    4      1       2 18294  32.00   F    4      1       2 18295  46.00   F    6      1       2 18296  44.00   M    9      1       2 18297  36.00   M   10      1       2 18298  50.00   M    8      1       2 18299  53.00   M    8      1       2 18300  46.00   M    8      1       2 18301  68.00   F    9      1       3 18302  57.00   M   19      1       2 18303  69.00   M   18      1       3 18304  20.00   M   19      1       2 18305  57.00   M   20      1       2 18306  20.00   F   20      1       2 18307  36.00   F   18      1       2 18308  61.00   M   18      1       3 18309  18.00   F   18      1       2 18310  57.00   M   18      1       2 18311  60.00   M   19      1       3 18312  30.00   F   18      1       2 18313  64.00   F   18      1       3 18314  51.00   M    8      1       2 18315  35.00   F    9      1       2 18316  34.00   M   18      1       2 18317  40.00   M    8      1       2 18318  48.00   F   11      1       2 18319  26.00   M   11      1       2 18320  45.00   M   12      1       2 18321  50.00   M   12      1       2 18322  26.00   M   12      1       2 18323  33.00   M    7      1       2 18324  35.00   M    5      1       2 18325  33.00   M    9      1       2 18326   0.91   M    8      1       1 18327  47.00   M   18      1       2 18328  29.00   F   17      1       2 18329  30.00   M   17      1       2 18330  60.00   F   17      1       3 18331  27.00   M   18      1       2 18332  42.00   M   17      1       2 18333  20.00   M   17      1       2 18334  30.00   M   26      1       2 18335  29.00   M    9      1       2 18336  33.00   M   17      1       2 18337  78.00   M   17      1       3 18338  68.00   M   18      1       3 18339  64.00   M   21      1       3 18340  45.00   M    7      1       2 18341  62.00   M   17      1       3 18342  32.00   M   18      1       2 18343  38.00   M   17      1       2 18344  24.00   M    8      1       2 18345  60.00   M   19      1       3 18346  59.00   F    8      1       2 18347  38.00   M   18      1       2 18348  38.00   M   17      1       2 18349  34.00   M   13      1       2 18350  40.00   M   13      1       2 18351  25.00   F   13      1       2 18352  19.00   M    9      1       2 18353  25.00   F    9      1       2 18354  63.00   M   11      1       3 18355   3.00   M    9      1       1 18356  28.00   M    9      1       2 18357  78.00   F   11      1       3 18358  80.00   M   11      1       3 18359  35.00   M   15      1       2 18360  33.00   M    8      1       2 18361  38.00   F    8      1       2 18362  12.00   F    8      1       1 18363   8.00   M    8      1       1 18364  46.00   M    2      1       2 18365  17.00   F    9      1       1 18366  39.00   F    9      1       2 18367  18.00   F    9      1       2 18368  65.00   F    2      1       3 18369  44.00   F    9      1       2 18370  14.00   M    9      1       1 18371  17.00   M    9      1       1 18372  42.00   M   10      1       2 18373   8.00   M    8      1       1 18374  61.00   F    8      1       3 18375  65.00   M   10      1       3 18376  62.00   M   12      1       3 18377  63.00   M   10      1       3 18378   7.00   F   11      1       1 18379  24.00   M    5      1       2 18380  22.00   M    5      1       2 18381  22.00   M    5      1       2 18382  22.00   M    5      1       2 18383  23.00   M   10      1       2 18384  28.00   M   10      1       2 18385  26.00   M    5      1       2 18386  23.00   M   10      1       2 18387  24.00   M   10      1       2 18388  23.00   M    5      1       2 18389  25.00   M    9      1       2 18390  22.00   M    9      1       2 18391  46.00   M    9      1       2 18392  29.00   M    9      1       2 18393  26.00   M   17      1       2 18394  34.00   M    9      1       2 18395  35.00   M    5      1       2 18396  22.00   M    9      1       2 18397  25.00   F    5      1       2 18398   2.00   F    9      1       1 18399  26.00   F    9      1       2 18400  30.00   M    5      1       2 18401  55.00   F    9      1       2 18402  28.00   F    9      1       2 18403  25.00   M    6      1       2 18404  26.00   M    6      1       2 18405  26.00   M    9      1       2 18406  27.00   M    9      1       2 18407  25.00   M   17      1       2 18408  26.00   M    9      1       2 18409  28.00   M    9      1       2 18410  24.00   M   17      1       2 18411  25.00   M   17      1       2 18412  38.00   M   17      1       2 18413  38.00   M    9      1       2 18414  12.00   M    9      1       1 18415  36.00   M    6      1       2 18416  46.00   M    9      1       2 18417  34.00   M    9      1       2 18418  27.00   M   10      1       2 18419  27.00   M   10      1       2 18420  34.00   M   10      1       2 18421  40.00   M   10      1       2 18422  48.00   M    9      1       2 18423  39.00   M    9      1       2 18424  33.00   M    9      1       2 18425  40.00   M    6      1       2 18426  57.00   M    6      1       2 18427  47.00   M    6      1       2 18428  31.00   M    9      1       2 18429  36.00   M    9      1       2 18430  37.00   M    9      1       2 18431  42.00   M    9      1       2 18432  45.00   M    9      1       2 18433  32.00   M    9      1       2 18434  24.00   F    9      1       2 18435  25.00   M    9      1       2 18436  23.00   M   17      1       2 18437  20.00   M    9      1       2 18438  25.00   M   10      1       2 18439  31.00   M    9      1       2 18440  24.00   M    5      1       2 18441  25.00   M    9      1       2 18442  32.00   M    9      1       2 18443  29.00   M    6      1       2 18444  25.00   M    9      1       2 18445  27.00   M    9      1       2 18446  48.00   M    6      1       2 18447  23.00   M    9      1       2 18448  38.00   M    6      1       2 18449  32.00   M   10      1       2 18450  28.00   M    6      1       2 18451  31.00   M    9      1       2 18452  32.00   M    5      1       2 18453  44.00   M    9      1       2 18454  28.00   M    6      1       2 18455  26.00   M   10      1       2 18456  26.00   M    6      1       2 18457  33.00   M    9      1       2 18458  43.00   M    9      1       2 18459  30.00   M    9      1       2 18460  46.00   M    9      1       2 18461  49.00   F    9      1       2 18462  36.00   M    9      1       2 18463  12.00   M   10      1       1 18464  75.00   F   10      1       3 18465  83.00   M   10      1       3 18466  12.00   M   10      1       1 18467  57.00   M    8      1       2 18468  57.00   M   10      1       2 18469  49.00   M   10      1       2 18470  26.00   F   16      1       2 18471  11.00   F   16      1       1 18472   3.00   M   16      1       1 18473  21.00   F   18      1       2 18474  52.00   M    9      1       2 18475   6.00   M    8      1       1 18476  70.00   F    6      1       3 18477  55.00   F    5      1       2 18478  35.00   M    5      1       2 18479  34.00   M    5      1       2 18480  27.00   M    8      1       2 18481  36.00   M    7      1       2 18482  38.00   M   14      1       2 18483  21.00   M   10      1       2 18484  39.00   M    7      1       2 18485  25.00   F   12      1       2 18486  49.00   M    9      1       2 18487  31.00   M    7      1       2 18488  65.00   M   16      1       3 18489  32.00   M    9      1       2 18490  45.00   M    9      1       2 18491  35.00   F    9      1       2 18492  28.00   M   11      1       2 18493  23.00   F   10      1       2 18494  27.00   M    7      1       2 18495   7.00   M   10      1       1 18496  40.00   M   12      1       2 18497  31.00   M    9      1       2 18498  35.00   F   10      1       2 18499   5.00   F    7      1       1 18500  33.00   M    9      1       2 18501   3.00   F    7      1       1 18502  36.00   F   11      1       2 18503  27.00   M    9      1       2 18504  20.00   F    9      1       2 18505  38.00   F    9      1       2 18506  17.00   F    9      1       1 18507  28.00   F    9      1       2 18508  14.00   M    9      1       1 18509  40.00   F    9      1       2 18510  17.00   F    9      1       1 18511  23.00   M    9      1       2 18512  23.00   M    9      1       2 18513  22.00   F    9      1       2 18514  23.00   M    9      1       2 18515  54.00   F    9      1       2 18516  45.00   F    9      1       2 18517  21.00   F    9      1       2 18518  43.00   F   16      1       2 18519  21.00   M    7      1       2 18520  37.00   M    9      1       2 18521  23.00   M    9      1       2 18522  29.00   M    9      1       2 18523   5.00   F    7      1       1 18524  50.00   F    9      1       2 18525   9.00   F   10      1       1 18526  16.00   F    9      1       1 18527  27.00   M    9      1       2 18528  24.00   M    9      1       2 18529  30.00   F    9      1       2 18530  40.00   M    9      1       2 18531  21.00   F   11      1       2 18532  26.00   M    9      1       2 18533  25.00   M   16      1       2 18534  31.00   M    6      1       2 18535  30.00   F    6      1       2 18536  29.00   F    6      1       2 18537  41.00   M    8      1       2 18538   1.00   F    5      1       1 18539  12.00   M    5      1       1 18540  20.00   M    5      1       2 18541  21.00   F    5      1       2 18542  28.00   M    5      1       2 18543  35.00   F    5      1       2 18544  22.00   M   10      1       2 18545  25.00   M   14      1       2 18546  30.00   M   10      1       2 18547  45.00   M   10      1       2 18548  41.00   M   13      1       2 18549  24.00   F    7      1       2 18550  29.00   F   10      1       2 18551  22.00   M    7      1       2 18552  10.00   M    7      1       1 18553  31.00   M   11      1       2 18554  30.00   M   11      1       2 18555  25.00   F   14      1       2 18556   4.00   F   11      1       1 18557  46.00   M   16      1       2 18558  60.00   F   19      1       3 18559  23.00   F    9      1       2 18560  24.00   F   10      1       2 18561  35.00   F   11      1       2 18562  10.00   M   10      1       1 18563   8.00   F   10      1       1 18564   6.00   M   26      1       1 18565  29.00   F   26      1       2 18566  35.00   F   10      1       2 18567  40.00   M   10      1       2 18568  18.00   F   11      1       2 18569  27.00   M   11      1       2 18570   2.00   M    4      1       1 18571  28.00   M   10      1       2 18572  26.00   F   10      1       2 18573  24.00   F    4      1       2 18574  47.00   M   10      1       2 18575  42.00   M   12      1       2 18576  48.00   M    9      1       2 18577  37.00   F   11      1       2 18578  39.00   M   11      1       2 18579  39.00   M   11      1       2 18580  13.00   F    9      1       1 18581  45.00   M   26      1       2 18582  28.00   F   10      1       2 18583   6.00   M   26      1       1 18584  35.00   M   10      1       2 18585  58.00   F   10      1       2 18586  34.00   M   10      1       2 18587  25.00   F   10      1       2 18588  24.00   M   10      1       2 18589  36.00   F   11      1       2 18590  30.00   M   11      1       2 18591  29.00   M   10      1       2 18592  26.00   M   11      1       2 18593  30.00   F   11      1       2 18594  18.00   M   10      1       2 18595  40.00   F   10      1       2 18596  39.00   F   11      1       2 18597   5.00   F   23      1       1 18598  41.00   M   12      1       2 18599  36.00   M   19      1       2 18600  32.00   M   10      1       2 18601  15.00   M   10      1       1 18602  25.00   F   19      1       2 18603  37.00   M   16      1       2 18604  18.00   M   11      1       2 18605  30.00   F   10      1       2 18606  49.00   F   10      1       2 18607  35.00   M   15      1       2 18608  32.00   M   15      1       2 18609  17.00   M   15      1       1 18610  38.00   M    9      1       2 18611  27.00   F    9      1       2 18612  30.00   M   15      1       2 18613  33.00   M   18      1       2 18614  16.00   F   15      1       1 18615  57.00   M   18      1       2 18616  70.00   M   15      1       3 18617  50.00   M   18      1       2 18618  40.00   F   15      1       2 18619  29.00   M   15      1       2 18620  55.00   M    9      1       2 18621  59.00   M    9      1       2 18622  30.00   F    7      1       2 18623  10.00   F    9      1       1 18624  34.00   M    9      1       2 18625  33.00   M    9      1       2 18626  33.00   F   20      1       2 18627  35.00   M    9      1       2 18628  16.00   F    9      1       1 18629  47.00   M    9      1       2 18630  65.00   M   22      1       3 18631  52.00   F    8      1       2 18632  64.00   F   22      1       3 18633  35.00   M    8      1       2 18634  43.00   M    8      1       2 18635  30.00   F   16      1       2 18636  38.00   F   16      1       2 18637  12.00   F   16      1       1 18638  42.00   M    9      1       2 18639  35.00   M   16      1       2 18640  32.00   M    1      1       2 18641  45.00   M   10      1       2 18642  27.00   M    1      1       2 18643   9.00   F   13      1       1 18644  22.00   F   13      1       2 18645   9.00   M    7      1       1 18646  54.00   M    7      1       2 18647  16.00   M   10      1       1 18648  51.00   F   10      1       2 18649  24.00   F   10      1       2 18650   5.00   M    9      1       1 18651  45.00   M    9      1       2 18652  50.00   M   10      1       2 18653  20.00   F    7      1       2 18654  37.00   M   15      1       2 18655  56.00   F   10      1       2 18656  20.00   M   10      1       2 18657  25.00   F   10      1       2 18658  28.00   F    8      1       2 18659  24.00   F    8      1       2 18660  29.00   F    9      1       2 18661  55.00   M    9      1       2 18662  51.00   M   10      1       2 18663  20.00   F   10      1       2 18664   6.00   F   10      1       1 18665  10.00   M   10      1       1 18666  34.00   F   10      1       2 18667  68.00   F   10      1       3 18668  31.00   M    7      1       2 18669  23.00   F    8      1       2 18670  26.00   F    9      1       2 18671  56.00   F   18      1       2 18672  35.00   M   16      1       2 18673  63.00   F   20      1       3 18674  33.00   M   16      1       2 18675  22.00   F   16      1       2 18676  36.00   M   16      1       2 18677  33.00   F   17      1       2 18678  34.00   F   16      1       2 18679  19.00   F   16      1       2 18680  67.00   M   17      1       3 18681  35.00   M   18      1       2 18682  44.00   M   17      1       2 18683  49.00   M   16      1       2 18684  24.00   M   17      1       2 18685  46.00   M   17      1       2 18686  24.00   F   17      1       2 18687  36.00   M    7      1       2 18688  59.00   M   17      1       2 18689  57.00   M   17      1       2 18690  54.00   M   17      1       2 18691  18.00   F   16      1       2 18692  40.00   F   16      1       2 18693  32.00   F   16      1       2 18694   4.00   M   16      1       1 18695  35.00   M   18      1       2 18696  28.00   M   16      1       2 18697  15.00   F   16      1       1 18698  30.00   F   10      1       2 18699  13.00   M   10      1       1 18700  25.00   F   16      1       2 18701   5.00   M   11      1       1 18702  23.00   M   16      1       2 18703  30.00   M   14      1       2 18704  26.00   F   14      1       2 18705  14.00   F   14      1       1 18706  31.00   F   18      1       2 18707  37.00   M   14      1       2 18708  29.00   F   10      1       2 18709   6.00   F   10      1       1 18710  40.00   M   10      1       2 18711  10.00   F   10      1       1 18712  25.00   F   14      1       2 18713  11.00   F   18      1       1 18714  58.00   F   14      1       2 18715  18.00   M   14      1       2 18716  35.00   M   18      1       2 18717  45.00   F   10      1       2 18718  55.00   M   10      1       2 18719  25.00   F   10      1       2 18720   3.00   M   10      1       1 18721  15.00   F   25      1       1 18722   6.00   F   10      1       1 18723  26.00   M   14      1       2 18724  23.00   F   14      1       2 18725  35.00   M   25      1       2 18726   6.00   M   10      1       1 18727  20.00   M   14      1       2 18728  48.00   F    9      1       2 18729  19.00   M   18      1       2 18730   2.00   M    9      1       1 18731  37.00   M    8      1       2 18732  23.00   M    9      1       2 18733   9.00   M    9      1       1 18734  13.00   F    8      1       1 18735   4.00   M    8      1       1 18736  50.00   M    8      1       2 18737  23.00   F    8      1       2 18738  32.00   F    8      1       2 18739   4.00   M    8      1       1 18740   3.00   M    8      1       1 18741   2.00   F   15      1       1 18742  41.00   F   15      1       2 18743  45.00   F   15      1       2 18744   7.00   M   10      1       1 18745   9.00   F   10      1       1 18746   1.00   F    7      1       1 18747   9.00   M    7      1       1 18748  35.00   F    7      1       2 18749  30.00   M    7      1       2 18750  28.00   F    7      1       2 18751  31.00   F    7      1       2 18752  44.00   M    7      1       2 18753   4.00   M    7      1       1 18754  11.00   F    7      1       1 18755  10.00   M    7      1       1 18756  30.00   M    7      1       2 18757  25.00   M   13      1       2 18758  34.00   M    8      1       2 18759  36.00   F    8      1       2 18760  43.00   M    8      1       2 18761  10.00   F    9      1       1 18762  61.00   M   24      1       3 18763   5.00   F    6      1       1 18764  50.00   M   10      1       2 18765   7.00   F    6      1       1 18766  45.00   M   10      1       2 18767  49.00   M   13      1       2 18768  48.00   M    8      1       2 18769  14.00   F    8      1       1 18770  43.00   M    8      1       2 18771  40.00   F    8      1       2 18772  28.00   F    6      1       2 18773  52.00   M   13      1       2 18774   8.00   M    9      1       1 18775  10.00   F    9      1       1 18776   1.00   M    6      1       1 18777  27.00   M    9      1       2 18778  62.00   M   15      1       3 18779  29.00   F   13      1       2 18780   6.00   M    9      1       1 18781  43.00   M   10      1       2 18782  28.00   M   10      1       2 18783  49.00   M   24      1       2 18784  10.00   M   14      1       1 18785  34.00   M   14      1       2 18786  44.00   M   14      1       2 18787  13.00   M   14      1       1 18788  22.00   M   14      1       2 18789  35.00   F   14      1       2 18790  15.00   M   14      1       1 18791  63.00   M    9      1       3 18792   4.00   F   20      1       1 18793   3.00   F    9      1       1 18794  23.00   M   15      1       2 18795  50.00   M    8      1       2 18796  22.00   M   15      1       2 18797  58.00   M    8      1       2 18798  25.00   M   15      1       2 18799  34.00   M    8      1       2 18800  28.00   M    8      1       2 18801  23.00   M    8      1       2 18802  35.00   M    8      1       2 18803  30.00   M   15      1       2 18804  48.00   F    8      1       2 18805  44.00   F    8      1       2 18806  27.00   M    8      1       2 18807  24.00   M    8      1       2 18808  45.00   M    8      1       2 18809  38.00   M    8      1       2 18810  27.00   M   15      1       2 18811  36.00   M    8      1       2 18812  39.00   M    8      1       2 18813  25.00   M   15      1       2 18814  23.00   M    8      1       2 18815   5.00   F    7      1       1 18816  39.00   F    7      1       2 18817  21.00   F    7      1       2 18818  60.00   M    7      1       3 18819  69.00   M    7      1       3 18820  19.00   F    7      1       2 18821  18.00   M    7      1       2 18822  30.00   M    6      1       2 18823  52.00   M    9      1       2 18824  50.00   M    9      1       2 18825  15.00   F   11      1       1 18826  50.00   F   11      1       2 18827  21.00   M   11      1       2 18828  32.00   M   11      1       2 18829  26.00   M   11      1       2 18830  32.00   F   11      1       2 18831  20.00   M   11      1       2 18832  25.00   M   11      1       2 18833  55.00   M   21      1       2 18834  45.00   F   11      1       2 18835  43.00   M   11      1       2 18836  26.00   F   11      1       2 18837   3.00   F   11      1       1 18838  29.00   F   11      1       2 18839  72.00   F   13      1       3 18840  12.00   M   11      1       1 18841  40.00   M   11      1       2 18842  23.00   M   11      1       2 18843  25.00   F    8      1       2 18844  30.00   M    5      1       2 18845  32.00   F    5      1       2 18846  27.00   M    7      1       2 18847  25.00   M    8      1       2 18848  37.00   F    8      1       2 18849  53.00   F    8      1       2 18850  30.00   M   11      1       2 18851  56.00   M   10      1       2 18852   5.00   M    9      1       1 18853  28.00   M    9      1       2 18854  30.00   M   10      1       2 18855  30.00   F    7      1       2 18856   8.00   M    9      1       1 18857  37.00   M   10      1       2 18858  23.00   M   10      1       2 18859  40.00   M   13      1       2 18860  45.00   F   13      1       2 18861  23.00   M   13      1       2 18862  39.00   M   10      1       2 18863  28.00   F    8      1       2 18864  21.00   M    8      1       2 18865  42.00   F   14      1       2 18866  18.00   F   12      1       2 18867  35.00   F   12      1       2 18868  23.00   F    8      1       2 18869  37.00   M    9      1       2 18870  46.00   F    9      1       2 18871  52.00   M    9      1       2 18872  48.00   M    9      1       2 18873  30.00   M    9      1       2 18874  34.00   M    9      1       2 18875  30.00   M   12      1       2 18876  35.00   F    9      1       2 18877  40.00   F    9      1       2 18878  40.00   F    9      1       2 18879  15.00   M    9      1       1 18880  30.00   M    9      1       2 18881  60.00   M    9      1       3 18882  32.00   F   12      1       2 18883  41.00   F    8      1       2 18884  25.00   M    9      1       2 18885  32.00   M    9      1       2 18886  34.00   M    9      1       2 18887  52.00   M    9      1       2 18888  22.00   M   14      1       2 18889  42.00   M   23      1       2 18890  50.00   M   14      1       2 18891  31.00   M   12      1       2 18892  29.00   M   12      1       2 18893  40.00   M   12      1       2 18894  16.00   M   12      1       1 18895  38.00   F   12      1       2 18896  32.00   M   12      1       2 18897  27.00   M    9      1       2 18898  28.00   M   12      1       2 18899  22.00   F    9      1       2 18900   3.00   F   12      1       1 18901  64.00   M   12      1       3 18902  37.00   M   14      1       2 18903  48.00   F    9      1       2 18904  26.00   M   15      1       2 18905  22.00   M   14      1       2 18906  70.00   M   14      1       3 18907  30.00   F   14      1       2 18908   8.00   M   12      1       1 18909   7.00   F    9      1       1 18910  30.00   F   15      1       2 18911  45.00   F   16      1       2 18912  65.00   M   16      1       3 18913  17.00   M    9      1       1 18914  17.00   F    9      1       1 18915  31.00   F   11      1       2 18916  24.00   M   24      1       2 18917  30.00   M   10      1       2 18918  26.00   F   10      1       2 18919   7.00   M   10      1       1 18920  38.00   F   10      1       2 18921  36.00   F   10      1       2 18922  35.00   F    9      1       2 18923  30.00   M    7      1       2 18924  11.00   M    7      1       1 18925  12.00   M   17      1       1 18926  11.00   F    9      1       1 18927  54.00   M   24      1       2 18928  52.00   F   10      1       2 18929   1.00   F    7      1       1 18930  40.00   F   17      1       2 18931  54.00   M   17      1       2 18932  36.00   F    3      1       2 18933  26.00   M   10      1       2 18934  33.00   M   10      1       2 18935   3.00   F   10      1       1 18936  11.00   F   11      1       1 18937  40.00   F   11      1       2 18938  14.00   M   11      1       1 18939  13.00   F   11      1       1 18940  65.00   M   11      1       3 18941  22.00   F   11      1       2 18942  60.00   M   11      1       3 18943  75.00   F   11      1       3 18944  26.00   M   10      1       2 18945  23.00   F   10      1       2 18946  27.00   F   10      1       2 18947   1.00   F   21      1       1 18948  12.00   M   15      1       1 18949  48.00   M   24      1       2 18950  10.00   F   10      1       1 18951  15.00   F   10      1       1 18952   8.00   F   24      1       1 18953  41.00   M   10      1       2 18954  17.00   F   10      1       1 18955   4.00   M    9      1       1 18956  66.00   M   21      1       3 18957   7.00   F   10      1       1 18958  50.00   F   10      1       2 18959  22.00   M   10      1       2 18960  30.00   M    7      1       2 18961   6.00   F    7      1       1 18962   8.00   F    7      1       1 18963   7.00   M    9      1       1 18964  25.00   F    7      1       2 18965  42.00   M    7      1       2 18966  34.00   F    7      1       2 18967  40.00   M   10      1       2 18968  47.00   F   11      1       2 18969  42.00   M   22      1       2 18970  54.00   M    9      1       2 18971  51.00   M    9      1       2 18972  55.00   F   21      1       2 18973  55.00   F    7      1       2 18974   2.00   M    7      1       1 18975  25.00   F   16      1       2 18976  50.00   F    6      1       2 18977  31.00   M    6      1       2 18978  24.00   M    6      1       2 18979  49.00   M    6      1       2 18980  45.00   M    6      1       2 18981  30.00   M   11      1       2 18982  28.00   M   11      1       2 18983  35.00   F    7      1       2 18984  25.00   M   13      1       2 18985  30.00   M    8      1       2 18986  24.00   M    7      1       2 18987  37.00   M   11      1       2 18988  28.00   F    7      1       2 18989  40.00   M    9      1       2 18990  36.00   F   22      1       2 18991   5.00   F   11      1       1 18992  24.00   M   13      1       2 18993  28.00   F   11      1       2 18994   2.00   M    8      1       1 18995  28.00   M   13      1       2 18996  65.00   M   22      1       3 18997  38.00   M   11      1       2 18998  43.00   M    8      1       2 18999  56.00   M   13      1       2 19000  48.00   F   13      1       2 19001  29.00   M   13      1       2 19002  64.00   M   11      1       3 19003  25.00   M   22      1       2 19004  40.00   M    7      1       2 19005  30.00   M    7      1       2 19006  43.00   M   13      1       2 19007  49.00   M   11      1       2 19008  36.00   M    7      1       2 19009  41.00   F    7      1       2 19010  23.00   M    8      1       2 19011  18.00   M   13      1       2 19012  22.00   M    8      1       2 19013  26.00   M    8      1       2 19014  34.00   M    8      1       2 19015  25.00   M   13      1       2 19016  44.00   M    7      1       2 19017  57.00   M    8      1       2 19018  46.00   F   13      1       2 19019  33.00   M    8      1       2 19020  43.00   M   14      1       2 19021  22.00   M    7      1       2 19022  23.00   M    7      1       2 19023  22.00   M    7      1       2 19024  78.00   M   14      1       3 19025  22.00   M    9      1       2 19026  34.00   F    8      1       2 19027  24.00   F    8      1       2 19028  34.00   F    8      1       2 19029  27.00   F    8      1       2 19030  38.00   M    8      1       2 19031  21.00   F    8      1       2 19032  55.00   F    8      1       2 19033  50.00   M    8      1       2 19034  32.00   M    8      1       2 19035  82.00   F    6      1       3 19036  60.00   F    8      1       3 19037  30.00   F    8      1       2 19038  29.00   F    6      1       2 19039  35.00   M    8      1       2 19040  20.00   M    8      1       2 19041  40.00   M    8      1       2 19042   8.00   M    8      1       1 19043  41.00   M    8      1       2 19044   7.00   F    9      1       1 19045  32.00   F    8      1       2 19046  37.00   M    8      1       2 19047  63.00   M    9      1       3 19048  54.00   M    9      1       2 19049  27.00   F    6      1       2 19050  36.00   M    7      1       2 19051  36.00   F    8      1       2 19052  23.00   F   14      1       2 19053  49.00   M   14      1       2 19054  26.00   F   19      1       2 19055  30.00   F   14      1       2 19056  37.00   M   18      1       2 19057  41.00   M   18      1       2 19058  40.00   M   19      1       2 19059  32.00   F   14      1       2 19060   7.00   F   19      1       1 19061  10.00   M   14      1       1 19062  60.00   M   14      1       3 19063  33.00   F   18      1       2 19064  13.00   F   14      1       1 19065  10.00   M   19      1       1 19066  56.00   F   19      1       2 19067  35.00   F   14      1       2 19068  38.00   M   19      1       2 19069  13.00   F   14      1       1 19070   7.00   M   19      1       1 19071  42.00   M   12      1       2 19072  41.00   F   12      1       2 19073  17.00   F   12      1       1 19074  38.00   M   12      1       2 19075  65.00   M   12      1       3 19076  14.00   F    8      1       1 19077  26.00   F   11      1       2 19078  50.00   F   23      1       2 19079  20.00   F    9      1       2 19080  65.00   F    9      1       3 19081  42.00   M   10      1       2 19082  28.00   F    7      1       2 19083  18.00   F    7      1       2 19084  20.00   F    4      1       2 19085  22.00   F    4      1       2 19086  21.00   F    4      1       2 19087  23.00   F    4      1       2 19088  54.00   F    7      1       2 19089  12.00   M    7      1       1 19090  60.00   F    7      1       3 19091  24.00   F    7      1       2 19092  48.00   F    7      1       2 19093  65.00   M    7      1       3 19094  35.00   M   12      1       2 19095  55.00   M   12      1       2 19096  37.00   F   13      1       2 19097  16.00   F   13      1       1 19098  31.00   M   13      1       2 19099  59.00   M   13      1       2 19100  25.00   M    8      1       2 19101  15.00   M    8      1       1 19102  27.00   M    8      1       2 19103  24.00   F   14      1       2 19104  50.00   M   10      1       2 19105  49.00   M    8      1       2 19106  40.00   F    8      1       2 19107  55.00   F   18      1       2 19108  42.00   F   18      1       2 19109  26.00   M   18      1       2 19110  35.00   M   18      1       2 19111  41.00   M   18      1       2 19112  61.00   F   15      1       3 19113  50.00   F   15      1       2 19114  64.00   F   16      1       3 19115  53.00   M   15      1       2 19116  35.00   M   18      1       2 19117  36.00   M   15      1       2 19118  53.00   M   14      1       2 19119  32.00   F   14      1       2 19120  34.00   M   15      1       2 19121  55.00   M   17      1       2 19122  61.00   F   17      1       3 19123  40.00   M   14      1       2 19124  29.00   F   14      1       2 19125  59.00   F   14      1       2 19126  61.00   F   16      1       3 19127  32.00   M   16      1       2 19128  33.00   M   17      1       2 19129  24.00   M   14      1       2 19130  45.00   F   16      1       2 19131  65.00   M   16      1       3 19132  26.00   M   14      1       2 19133  54.00   M   16      1       2 19134  17.00   F   16      1       1 19135  13.00   F   16      1       1 19136  12.00   F   16      1       1 19137  16.00   F   16      1       1 19138  22.00   F   16      1       2 19139  42.00   F   16      1       2 19140  21.00   F   16      1       2 19141  45.00   F   16      1       2 19142  18.00   F   14      1       2 19143  15.00   M   14      1       1 19144  27.00   F   14      1       2 19145   7.00   F   16      1       1 19146  45.00   F   16      1       2 19147  26.00   M   14      1       2 19148  19.00   M   16      1       2 19149  33.00   M    7      1       2 19150  19.00   F    7      1       2 19151  62.00   M   14      1       3 19152  55.00   F    7      1       2 19153  25.00   M    7      1       2 19154  30.00   M   14      1       2 19155  31.00   F    7      1       2 19156  24.00   M    7      1       2 19157  45.00   F    7      1       2 19158  33.00   M    7      1       2 19159   1.00   F    7      1       1 19160  17.00   M    7      1       1 19161  15.00   M   16      1       1 19162  25.00   M    7      1       2 19163   5.00   F   16      1       1 19164  60.00   M   14      1       3 19165  70.00   M   16      1       3 19166  42.00   M   16      1       2 19167  35.00   M   16      1       2 19168  70.00   M   15      1       3 19169  69.00   M   16      1       3 19170  36.00   M   15      1       2 19171  53.00   M   14      1       2 19172   1.00   F   15      1       1 19173  22.00   F   14      1       2 19174  50.00   F   15      1       2 19175  46.00   F   15      1       2 19176  45.00   F   15      1       2 19177  47.00   M   14      1       2 19178  56.00   F   15      1       2 19179  25.00   F   14      1       2 19180  15.00   F   14      1       1 19181  46.00   F   16      1       2 19182  52.00   M   14      1       2 19183  48.00   M   14      1       2 19184  75.00   M   14      1       3 19185  72.00   F   14      1       3 19186  29.00   M   15      1       2 19187  53.00   F   14      1       2 19188  21.00   F   15      1       2 19189  31.00   F   14      1       2 19190  47.00   M   15      1       2 19191  28.00   M   15      1       2 19192  44.00   M   15      1       2 19193  49.00   F   15      1       2 19194  23.00   M   14      1       2 19195  33.00   F   16      1       2 19196  57.00   M   15      1       2 19197  22.00   M   15      1       2 19198  66.00   F   14      1       3 19199  99.00   F   15      1       3 19200  28.00   M   14      1       2 19201  70.00   F   14      1       3 19202  31.00   F   14      1       2 19203  74.00   M   15      1       3 19204  68.00   F   16      1       3 19205  51.00   M    8      1       2 19206  50.00   M    7      1       2 19207  63.00   M   11      1       3 19208  57.00   M   11      1       2 19209  33.00   M   11      1       2 19210  22.00   F   11      1       2 19211  27.00   F    9      1       2 19212  31.00   M    9      1       2 19213  32.00   F    6      1       2 19214  67.00   M   23      1       3 19215  37.00   F    9      1       2 19216  50.00   F   23      1       2 19217  26.00   M   10      1       2 19218  50.00   M    9      1       2 19219  20.00   F   10      1       2 19220  20.00   M    9      1       2 19221  19.00   F   10      1       2 19222  23.00   M    9      1       2 19223  62.00   F    9      1       3 19224  30.00   F   10      1       2 19225  11.00   M   10      1       1 19226  32.00   M   12      1       2 19227  29.00   M    9      1       2 19228  19.00   M    9      1       2 19229  35.00   F   17      1       2 19230  48.00   M    9      1       2 19231  40.00   F   23      1       2 19232  40.00   F    9      1       2 19233  20.00   F    9      1       2 19234  18.00   F    9      1       2 19235   8.00   M    9      1       1 19236  27.00   F    9      1       2 19237  22.00   F    9      1       2 19238  21.00   M   13      1       2 19239  26.00   F    9      1       2 19240  35.00   M    9      1       2 19241  48.00   M   14      1       2 19242  34.00   M    9      1       2 19243  39.00   M   11      1       2 19244  37.00   F   10      1       2 19245   9.00   M    9      1       1 19246  18.00   F    9      1       2 19247  25.00   F    9      1       2 19248   6.00   M    9      1       1 19249   0.50   M    9      1       1 19250  35.00   M    7      1       2 19251  35.00   M    9      1       2 19252  28.00   M   10      1       2 19253  24.00   F   17      1       2 19254  40.00   F   10      1       2 19255  26.00   F    9      1       2 19256  34.00   M    9      1       2 19257  20.00   F    9      1       2 19258  55.00   M   14      1       2 19259  68.00   F   10      1       3 19260  45.00   F   14      1       2 19261  28.00   M   11      1       2 19262  25.00   F   11      1       2 19263  69.00   F    4      1       3 19264  36.00   F    7      1       2 19265  24.00   M   16      1       2 19266  45.00   M    7      1       2 19267  26.00   F    7      1       2 19268  65.00   F   13      1       3 19269  43.00   M    8      1       2 19270  48.00   M    8      1       2 19271  40.00   F    8      1       2 19272  44.00   F   17      1       2 19273  48.00   M   19      1       2 19274  23.00   M   12      1       2 19275  16.00   F   12      1       1 19276  85.00   F   17      1       3 19277  81.00   M   11      1       3 19278  25.00   M   11      1       2 19279  18.00   M    8      1       2 19280  29.00   M   15      1       2 19281  50.00   F    6      1       2 19282  23.00   F    8      1       2 19283   4.00   F    8      1       1 19284  42.00   F    8      1       2 19285  59.00   M   10      1       2 19286  17.00   F    8      1       1 19287  36.00   M    8      1       2 19288  27.00   M    8      1       2 19289  35.00   F    8      1       2 19290  44.00   M    8      1       2 19291  38.00   M   17      1       2 19292  49.00   M   15      1       2 19293  17.00   M   15      1       1 19294  39.00   F   15      1       2 19295  18.00   M   15      1       2 19296  30.00   M    8      1       2 19297  57.00   M    8      1       2 19298  21.00   M    8      1       2 19299  21.00   F    7      1       2 19300  23.00   F    9      1       2 19301  38.00   M    9      1       2 19302  54.00   M    9      1       2 19303  31.00   M    9      1       2 19304  47.00   M    9      1       2 19305  18.00   M    9      1       2 19306  50.00   M    9      1       2 19307  25.00   M    9      1       2 19308  55.00   M   15      1       2 19309  60.00   M   17      1       3 19310  62.00   M    9      1       3 19311  32.00   M    9      1       2 19312  46.00   M    6      1       2 19313  54.00   M    9      1       2 19314   6.00   F   12      1       1 19315  65.00   M   12      1       3 19316   3.00   F    7      1       1 19317   2.00   M   12      1       1 19318  24.00   F   12      1       2 19319  45.00   F   12      1       2 19320  29.00   F   18      1       2 19321   4.00   F   18      1       1 19322  13.00   F   18      1       1 19323   8.00   M   18      1       1 19324  59.00   M   18      1       2 19325  37.00   M   10      1       2 19326  40.00   F   10      1       2 19327  16.00   F   18      1       1 19328  33.00   F   18      1       2 19329  31.00   M   18      1       2 19330  56.00   M   18      1       2 19331  50.00   F   18      1       2 19332  42.00   F   10      1       2 19333  16.00   M   18      1       1 19334  12.00   F   18      1       1 19335  28.00   M   10      1       2 19336  23.00   F   10      1       2 19337  23.00   M   12      1       2 19338  31.00   M    8      1       2 19339  27.00   M   10      1       2 19340  40.00   M    8      1       2 19341  42.00   M   10      1       2 19342  36.00   M    8      1       2 19343  26.00   M   21      1       2 19344  34.00   M   10      1       2 19345  34.00   M   10      1       2 19346  39.00   M   10      1       2 19347  43.00   M   10      1       2 19348  43.00   M    8      1       2 19349  31.00   M   12      1       2 19350  40.00   M   10      1       2 19351  36.00   M   12      1       2 19352  25.00   M   10      1       2 19353  50.00   F   10      1       2 19354  29.00   F   10      1       2 19355  21.00   F   10      1       2 19356  60.00   M   10      1       3 19357  24.00   F   10      1       2 19358  21.00   M   12      1       2 19359  19.00   F    6      1       2 19360  32.00   M   10      1       2 19361  24.00   M   12      1       2 19362  56.00   M   10      1       2 19363  28.00   M   10      1       2 19364  35.00   F   10      1       2 19365  39.00   F   12      1       2 19366  38.00   F   10      1       2 19367  60.00   M   10      1       3 19368  27.00   F   10      1       2 19369  30.00   M    8      1       2 19370   0.30   M    8      1       1 19371  42.00   M    8      1       2 19372  19.00   M    5      1       2 19373  28.00   F    5      1       2 19374  45.00   F    5      1       2 19375  47.00   M   10      1       2 19376  46.00   M   10      1       2 19377  65.00   F   10      1       3 19378  45.00   F   10      1       2 19379  44.00   F   10      1       2 19380  43.00   F    7      1       2 19381  25.00   F   10      1       2 19382  22.00   F    7      1       2 19383  20.00   F    7      1       2 19384  16.00   F    7      1       1 19385  13.00   M    7      1       1 19386  15.00   F    7      1       1 19387   8.00   M   10      1       1 19388  48.00   M   10      1       2 19389  23.00   M    7      1       2 19390  24.00   M    7      1       2 19391  16.00   M    7      1       1 19392  19.00   M    7      1       2 19393  47.00   M    7      1       2 19394  62.00   F    7      1       3 19395  48.00   M    7      1       2 19396  70.00   M    7      1       3 19397  70.00   F    7      1       3 19398  38.00   M    7      1       2 19399  42.00   M    7      1       2 19400  26.00   M    7      1       2 19401  68.00   F    7      1       3 19402  28.00   M    7      1       2 19403  24.00   M    7      1       2 19404  24.00   M    7      1       2 19405  19.00   F    7      1       2 19406  30.00   M    7      1       2 19407  60.00   F    7      1       3 19408  38.00   M    7      1       2 19409  26.00   M    7      1       2 19410  38.00   F    7      1       2 19411  10.00   M    7      1       1 19412  50.00   M    7      1       2 19413  52.00   M    7      1       2 19414  40.00   M    7      1       2 19415  20.00   F    7      1       2 19416   8.00   F    7      1       1 19417  75.00   M    7      1       3 19418  65.00   F    7      1       3 19419  75.00   F    7      1       3 19420  26.00   M    7      1       2 19421  38.00   F    7      1       2 19422  64.00   M   16      1       3 19423  62.00   F    7      1       3 19424  14.00   M    7      1       1 19425   6.00   M    7      1       1 19426  38.00   M    7      1       2 19427  13.00   F    7      1       1 19428  30.00   M    7      1       2 19429  30.00   F    7      1       2 19430  55.00   F    3      1       2 19431  45.00   F    7      1       2 19432  40.00   F    7      1       2 19433   4.00   F    7      1       1 19434  33.00   M    7      1       2 19435   7.00   M    7      1       1 19436  24.00   M    7      1       2 19437  21.00   F    7      1       2 19438  28.00   M    7      1       2 19439  30.00   M    7      1       2 19440  12.00   F    7      1       1 19441  20.00   F    7      1       2 19442   9.00   F    7      1       1 19443  31.00   M    8      1       2 19444  36.00   M    8      1       2 19445  24.00   M    8      1       2 19446  33.00   M    8      1       2 19447  30.00   M    8      1       2 19448  23.00   M    8      1       2 19449  22.00   M    8      1       2 19450  42.00   M    8      1       2 19451  54.00   F    5      1       2 19452  49.00   M    8      1       2 19453  53.00   M    8      1       2 19454  52.00   M    8      1       2 19455  22.00   M    8      1       2 19456  30.00   M    8      1       2 19457  24.00   M    8      1       2 19458  28.00   M    8      1       2 19459  23.00   M    8      1       2 19460  22.00   M    8      1       2 19461  25.00   M    8      1       2 19462  21.00   M    6      1       2 19463  49.00   F    5      1       2 19464  19.00   M    8      1       2 19465  38.00   M    9      1       2 19466  30.00   F    9      1       2 19467  54.00   F   14      1       2 19468  51.00   M    9      1       2 19469  51.00   M    9      1       2 19470  38.00   F   11      1       2 19471  42.00   M   11      1       2 19472  15.00   M   11      1       1 19473  27.00   M   11      1       2 19474  17.00   M   11      1       1 19475  50.00   F    9      1       2 19476  19.00   M    9      1       2 19477  54.00   M    9      1       2 19478  33.00   M   11      1       2 19479  36.00   M    9      1       2 19480  20.00   M    9      1       2 19481  21.00   F   19      1       2 19482  55.00   F    9      1       2 19483  31.00   F    9      1       2 19484  25.00   F    9      1       2 19485  32.00   F    9      1       2 19486   4.00   F    9      1       1 19487  70.00   F    9      1       3 19488  42.00   M    9      1       2 19489  20.00   M    9      1       2 19490  32.00   F    9      1       2 19491  52.00   M    9      1       2 19492  32.00   F    9      1       2 19493  21.00   F    9      1       2 19494  60.00   F    9      1       3 19495  23.00   M    9      1       2 19496  35.00   M    9      1       2 19497  19.00   M    9      1       2 19498  20.00   F    9      1       2 19499  22.00   F    9      1       2 19500  72.00   F    9      1       3 19501  22.00   F    9      1       2 19502  20.00   M    9      1       2 19503  40.00   F    9      1       2 19504  48.00   F    9      1       2 19505  45.00   F    9      1       2 19506  80.00   F    9      1       3 19507  24.00   F   19      1       2 19508  28.00   M    9      1       2 19509  42.00   M    9      1       2 19510  30.00   F   12      1       2 19511  60.00   F   14      1       3 19512  35.00   M   13      1       2 19513  12.00   F    6      1       1 19514  21.00   M   14      1       2 19515  32.00   M   21      1       2 19516  35.00   M   14      1       2 19517  45.00   F   13      1       2 19518  36.00   M   13      1       2 19519  60.00   F   12      1       3 19520  21.00   F   14      1       2 19521  30.00   F   15      1       2 19522  22.00   F   14      1       2 19523  62.00   M   13      1       3 19524  52.00   F   14      1       2 19525  60.00   M   15      1       3 19526  69.00   M   14      1       3 19527  45.00   F   12      1       2 19528  54.00   M   14      1       2 19529  24.00   F   15      1       2 19530  54.00   F   14      1       2 19531  53.00   M   12      1       2 19532  27.00   F   13      1       2 19533  68.00   M   22      1       3 19534  50.00   F   14      1       2 19535  36.00   M   14      1       2 19536  41.00   M   14      1       2 19537  61.00   M   14      1       3 19538  37.00   F   12      1       2 19539  34.00   M   13      1       2 19540  34.00   F   13      1       2 19541  44.00   F   13      1       2 19542  65.00   F   13      1       3 19543  15.00   F   14      1       1 19544  70.00   F   14      1       3 19545  32.00   F   13      1       2 19546  53.00   F   14      1       2 19547  54.00   M   10      1       2 19548  48.00   F   10      1       2 19549  45.00   F   12      1       2 19550  20.00   F   12      1       2 19551  15.00   F   12      1       1 19552   4.00   M   12      1       1 19553  60.00   M   12      1       3 19554  40.00   M   12      1       2 19555  30.00   M   14      1       2 19556  40.00   F   14      1       2 19557  16.00   M   14      1       1 19558  16.00   M   14      1       1 19559  30.00   F   14      1       2 19560  19.00   M    7      1       2 19561  28.00   M    7      1       2 19562  35.00   F   17      1       2 19563  25.00   M   14      1       2 19564  35.00   M   14      1       2 19565  30.00   F   14      1       2 19566  18.00   M   14      1       2 19567  25.00   F    7      1       2 19568   9.00   M    8      1       1 19569  40.00   F    9      1       2 19570  28.00   F   16      1       2 19571  62.00   M    9      1       3 19572  28.00   F   17      1       2 19573  10.00   F   16      1       1 19574  60.00   F   22      1       3 19575  19.00   M   14      1       2 19576  40.00   F   10      1       2 19577  40.00   F   10      1       2 19578  50.00   M   10      1       2 19579  22.00   F   10      1       2 19580  30.00   M    9      1       2 19581  53.00   F    8      1       2 19582  55.00   M    9      1       2 19583  27.00   F   10      1       2 19584  31.00   M   10      1       2 19585  30.00   M   10      1       2 19586  24.00   F   10      1       2 19587   0.75   F   10      1       1 19588   2.00   F   16      1       1 19589  40.00   F    9      1       2 19590   8.00   M   10      1       1 19591  10.00   M    9      1       1 19592  44.00   M    9      1       2 19593  27.00   M    9      1       2 19594  24.00   F   16      1       2 19595  15.00   M    9      1       1 19596   9.00   F    9      1       1 19597  28.00   M   10      1       2 19598  25.00   F    3      1       2 19599  45.00   F   12      1       2 19600  23.00   F   21      1       2 19601  38.00   F   12      1       2 19602   6.00   F   14      1       1 19603  36.00   F   14      1       2 19604  12.00   F    5      1       1 19605  55.00   F    5      1       2 19606  56.00   M    5      1       2 19607  14.00   M    5      1       1 19608  45.00   F    5      1       2 19609  50.00   F   14      1       2 19610  36.00   M    5      1       2 19611  25.00   F   10      1       2 19612  58.00   F   10      1       2 19613   0.41   F   10      1       1 19614  33.00   M   10      1       2 19615  17.00   M    6      1       1 19616  43.00   F    7      1       2 19617  69.00   F    6      1       3 19618  50.00   M    6      1       2 19619  42.00   F    7      1       2 19620  19.00   F    6      1       2 19621  45.00   M    6      1       2 19622  29.00   F   12      1       2 19623  35.00   M   12      1       2 19624  65.00   M   10      1       3 19625  75.00   F   12      1       3 19626  27.00   M   11      1       2 19627  28.00   F   11      1       2 19628  53.00   F   11      1       2 19629   6.00   M   11      1       1 19630  31.00   F   11      1       2 19631  55.00   M   13      1       2 19632  24.00   M   11      1       2 19633  21.00   F   12      1       2 19634  33.00   M   12      1       2 19635  68.00   F   12      1       3 19636   6.00   F   12      1       1 19637  31.00   M   12      1       2 19638  12.00   M   12      1       1 19639   5.00   M   12      1       1 19640  27.00   F    9      1       2 19641   8.00   F    8      1       1 19642  12.00   M    8      1       1 19643  34.00   F    8      1       2 19644  36.00   M    8      1       2 19645  25.00   M    8      1       2 19646  15.00   M    8      1       1 19647  30.00   F    8      1       2 19648  37.00   M    8      1       2 19649  66.00   M    7      1       3 19650  28.00   M    7      1       2 19651  50.00   F    7      1       2 19652  50.00   M    7      1       2 19653  60.00   M    7      1       3 19654  65.00   F    7      1       3 19655  60.00   F    7      1       3 19656  59.00   F    7      1       2 19657  30.00   F    7      1       2 19658  61.00   M   12      1       3 19659  65.00   F    7      1       3 19660  22.00   M    7      1       2 19661  45.00   M    7      1       2 19662  16.00   M    7      1       1 19663  62.00   M    7      1       3 19664  70.00   M    7      1       3 19665  10.00   F    7      1       1 19666   8.00   M    7      1       1 19667  48.00   F    7      1       2 19668  65.00   M    3      1       3 19669  25.00   M    7      1       2 19670  70.00   F    1      1       3 19671  30.00   F    7      1       2 19672  85.00   M    7      1       3 19673  29.00   M    7      1       2 19674  58.00   F    7      1       2 19675  31.00   F    7      1       2 19676  29.00   F    7      1       2 19677   4.00   F    7      1       1 19678   1.00   M    7      1       1 19679  34.00   F    7      1       2 19680  15.00   M    7      1       1 19681  23.00   M    7      1       2 19682  20.00   F    7      1       2 19683  26.00   F    7      1       2 19684  25.00   F    7      1       2 19685  59.00   M    9      1       2 19686  42.00   M    9      1       2 19687  58.00   F    9      1       2 19688  56.00   F   10      1       2 19689  65.00   F   10      1       3 19690  26.00   M   10      1       2 19691  25.00   M   10      1       2 19692  31.00   M    7      1       2 19693  39.00   M    7      1       2 19694  58.00   F    6      1       2 19695  14.00   M    6      1       1 19696  56.00   F    7      1       2 19697  60.00   F    7      1       3 19698  59.00   M    7      1       2 19699  47.00   M    7      1       2 19700  26.00   F   10      1       2 19701  18.00   M   13      1       2 19702  31.00   M    7      1       2 19703  71.00   F   10      1       3 19704  30.00   F   10      1       2 19705  24.00   F    3      1       2 19706  29.00   F    7      1       2 19707  65.00   M   18      1       3 19708  25.00   M    4      1       2 19709  35.00   M   11      1       2 19710  60.00   F   10      1       3 19711  38.00   F   10      1       2 19712  65.00   M    9      1       3 19713  21.00   F    9      1       2 19714  38.00   M   13      1       2 19715  37.00   M   14      1       2 19716  52.00   F   13      1       2 19717  23.00   M   13      1       2 19718  45.00   F   12      1       2 19719  32.00   M   12      1       2 19720  27.00   M   12      1       2 19721  62.00   F   15      1       3 19722  48.00   M   12      1       2 19723  26.00   M   13      1       2 19724  80.00   F   13      1       3 19725  44.00   M   12      1       2 19726  34.00   M   11      1       2 19727  58.00   M   11      1       2 19728  30.00   M   14      1       2 19729   6.00   F   13      1       1 19730  68.00   M   12      1       3 19731  23.00   F   14      1       2 19732  58.00   M   14      1       2 19733  56.00   M   13      1       2 19734  46.00   M   14      1       2 19735  47.00   M   12      1       2 19736  27.00   F   13      1       2 19737  35.00   F   12      1       2 19738  64.00   F   17      1       3 19739  52.00   F   17      1       2 19740  27.00   F   13      1       2 19741  30.00   F   12      1       2 19742  30.00   M   13      1       2 19743  36.00   M   13      1       2 19744  32.00   F   12      1       2 19745  25.00   F   11      1       2 19746  51.00   F   13      1       2 19747  55.00   F   13      1       2 19748  52.00   M   13      1       2 19749  36.00   F   13      1       2 19750  63.00   F   15      1       3 19751  59.00   M   13      1       2 19752  48.00   M   13      1       2 19753  27.00   M   13      1       2 19754  59.00   M   12      1       2 19755  81.00   M   13      1       3 19756  55.00   M   13      1       2 19757  55.00   M   12      1       2 19758  55.00   M   13      1       2 19759  16.00   M   13      1       1 19760  51.00   F   12      1       2 19761  23.00   M   11      1       2 19762  15.00   F   12      1       1 19763  49.00   F   11      1       2 19764  66.00   F   11      1       3 19765  24.00   F   11      1       2 19766  19.00   F   13      1       2 19767  16.00   F   11      1       1 19768   8.00   F   11      1       1 19769  37.00   F   11      1       2 19770  52.00   F   12      1       2 19771  70.00   M   13      1       3 19772  56.00   M   16      1       2 19773  55.00   M   12      1       2 19774   1.00   M   12      1       1 19775  27.00   F   12      1       2 19776  32.00   M   12      1       2 19777   4.00   F   12      1       1 19778  27.00   M   14      1       2 19779  73.00   M   14      1       3 19780  50.00   F   13      1       2 19781  23.00   F   14      1       2 19782  54.00   M   13      1       2 19783  11.00   M   13      1       1 19784  68.00   M   20      1       3 19785  38.00   M   13      1       2 19786  43.00   F   11      1       2 19787  30.00   M   12      1       2 19788  62.00   F   12      1       3 19789  50.00   M   12      1       2 19790  49.00   M   14      1       2 19791  57.00   F   13      1       2 19792  68.00   F   15      1       3 19793  23.00   F   11      1       2 19794  25.00   M   13      1       2 19795  35.00   M   13      1       2 19796  24.00   F   13      1       2 19797  65.00   M   12      1       3 19798  33.00   M   12      1       2 19799  49.00   F   13      1       2 19800  36.00   M   13      1       2 19801  57.00   M   14      1       2 19802  63.00   M   13      1       3 19803  48.00   M   13      1       2 19804  66.00   M   13      1       3 19805  85.00   M   13      1       3 19806  16.00   M   11      1       1 19807  42.00   M   13      1       2 19808  26.00   F   13      1       2 19809  27.00   M   13      1       2 19810  28.00   M   13      1       2 19811  75.00   M   13      1       3 19812  55.00   F   13      1       2 19813  50.00   M   13      1       2 19814  55.00   M   14      1       2 19815  24.00   M   13      1       2 19816  56.00   M   13      1       2 19817  54.00   F   13      1       2 19818  42.00   M   13      1       2 19819  56.00   F   12      1       2 19820  57.00   M   12      1       2 19821  27.00   M   17      1       2 19822  27.00   F   13      1       2 19823  57.00   M   15      1       2 19824  56.00   F   13      1       2 19825  55.00   F   13      1       2 19826  29.00   M   13      1       2 19827  28.00   M   13      1       2 19828  49.00   M   13      1       2 19829  65.00   M   14      1       3 19830  67.00   M   13      1       3 19831  31.00   F   13      1       2 19832  51.00   M    4      1       2 19833   4.00   M   13      1       1 19834  10.00   F   13      1       1 19835   8.00   M   13      1       1 19836  58.00   M   13      1       2 19837  59.00   M   13      1       2 19838  51.00   M   13      1       2 19839  59.00   M   14      1       2 19840  20.00   M   13      1       2 19841  27.00   M   14      1       2 19842  25.00   M   14      1       2 19843  17.00   M   14      1       1 19844  14.00   M   14      1       1 19845  14.00   F   14      1       1 19846  70.00   M    7      1       3 19847  26.00   F    7      1       2 19848  32.00   F    8      1       2 19849  59.00   M    8      1       2 19850  30.00   F    8      1       2 19851  38.00   M    8      1       2 19852  36.00   M    9      1       2 19853  42.00   M   12      1       2 19854  54.00   M    9      1       2 19855  43.00   M   17      1       2 19856  17.00   M   17      1       1 19857  32.00   M    9      1       2 19858  50.00   M    7      1       2 19859  42.00   M    7      1       2 19860  48.00   M    7      1       2 19861  22.00   M    7      1       2 19862  56.00   M    7      1       2 19863  38.00   M    7      1       2 19864   9.00   M    7      1       1 19865  13.00   F    7      1       1 19866  30.00   M    7      1       2 19867  31.00   F    7      1       2 19868  65.00   M   10      1       3 19869  62.00   M    9      1       3 19870  33.00   F    7      1       2 19871  64.00   M    7      1       3 19872  64.00   M    7      1       3 19873  30.00   M   11      1       2 19874  35.00   M    9      1       2 19875  56.00   M   11      1       2 19876  65.00   F    8      1       3 19877  25.00   M   17      1       2 19878  32.00   M    8      1       2 19879  23.00   F    8      1       2 19880  30.00   F    8      1       2 19881  30.00   F    8      1       2 19882  37.00   F    8      1       2 19883  30.00   F    8      1       2 19884  28.00   M    8      1       2 19885  36.00   F   15      1       2 19886  65.00   M   10      1       3 19887  50.00   F   11      1       2 19888  25.00   F   20      1       2 19889  28.00   M    7      1       2 19890  31.00   M    9      1       2 19891  29.00   M    7      1       2 19892  29.00   M    7      1       2 19893  30.00   M    9      1       2 19894  29.00   F    9      1       2 19895  58.00   M    9      1       2 19896  33.00   F    9      1       2 19897  38.00   F    9      1       2 19898  19.00   M    9      1       2 19899  14.00   F    9      1       1 19900  30.00   M    9      1       2 19901  13.00   F    9      1       1 19902  25.00   M    9      1       2 19903  15.00   F    9      1       1 19904  38.00   F   12      1       2 19905  24.00   M    9      1       2 19906  28.00   M    9      1       2 19907  41.00   F    9      1       2 19908  19.00   F    9      1       2 19909  19.00   F    9      1       2 19910  50.00   M    9      1       2 19911  24.00   M    9      1       2 19912  40.00   F    9      1       2 19913  32.00   M    9      1       2 19914   6.00   M    7      1       1 19915   3.00   M    7      1       1 19916  34.00   M    9      1       2 19917  37.00   M    9      1       2 19918  30.00   M   20      1       2 19919  31.00   M   20      1       2 19920  22.00   F    9      1       2 19921  77.00   M   11      1       3 19922  72.00   M   20      1       3 19923  32.00   M    9      1       2 19924  35.00   M   20      1       2 19925  50.00   F   20      1       2 19926  69.00   F    9      1       3 19927  42.00   F   21      1       2 19928  32.00   M    2      1       2 19929  25.00   M    8      1       2 19930  24.00   F    9      1       2 19931   3.00   M    9      1       1 19932  50.00   F    9      1       2 19933  25.00   M   10      1       2 19934  50.00   M    9      1       2 19935   7.00   F    9      1       1 19936  31.00   M    9      1       2 19937   3.00   M    9      1       1 19938  26.00   M    9      1       2 19939  45.00   F    9      1       2 19940   1.00   M    9      1       1 19941  30.00   F   12      1       2 19942  12.00   F   12      1       1 19943  40.00   M   13      1       2 19944  62.00   M    8      1       3 19945  39.00   M    8      1       2 19946  57.00   F    8      1       2 19947  30.00   F    9      1       2 19948   7.00   M    8      1       1 19949  30.00   F    9      1       2 19950  45.00   M    9      1       2 19951   2.00   F   15      1       1 19952  30.00   M   13      1       2 19953  18.00   F   13      1       2 19954   9.00   F   15      1       1 19955  29.00   M   10      1       2 19956  30.00   M   15      1       2 19957   8.00   M   16      1       1 19958  52.00   M   11      1       2 19959  12.00   M    9      1       1 19960  24.00   M    9      1       2 19961  24.00   M    9      1       2 19962  22.00   M    9      1       2 19963   1.00   M   14      1       1 19964  29.00   M    4      1       2 19965  40.00   F    9      1       2 19966  42.00   F   10      1       2 19967  28.00   F   13      1       2 19968  19.00   F   14      1       2 19969  21.00   M   14      1       2 19970  57.00   F   20      1       2 19971  54.00   M   13      1       2 19972  24.00   F    9      1       2 19973  25.00   M   13      1       2 19974  30.00   F    9      1       2 19975  64.00   F   11      1       3 19976  72.00   F    8      1       3 19977  43.00   M    8      1       2 19978  30.00   M    8      1       2 19979  35.00   F    8      1       2 19980  60.00   F    6      1       3 19981  75.00   M   12      1       3 19982  19.00   F   17      1       2 19983  13.00   M   17      1       1 19984  25.00   M   12      1       2 19985  50.00   M   17      1       2 19986  20.00   F    6      1       2 19987  20.00   F    6      1       2 19988  60.00   F    6      1       3 19989  20.00   F    6      1       2 19990  32.00   F    6      1       2 19991  28.00   F    6      1       2 19992  68.00   F    8      1       3 19993  20.00   F    6      1       2 19994  70.00   M    6      1       3 19995  30.00   M    6      1       2 19996  20.00   M    6      1       2 19997  52.00   F    6      1       2 19998  33.00   M    6      1       2 19999  56.00   M    9      1       2  [ reached 'max' / getOption(\"max.print\") -- omitted 6742 rows ]"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-preparing-the-data","dir":"Articles > Interactive_tte_tutorial","previous_headings":"The study and the data","what":"Preparing the data","title":"visR","text":"analysis, slight modifications need implemented data. Since visR developed goal speed clinical development process, follows cdisc ADaM standards. Additionally, functions data-type sensitive give informative summaries proper data-type present. following changes necessary: per ADaM guidelines, event indicated 0 censoring indicated >0. data use, 2 stands event 1 stands censoring. Therefore, censoring values adjusted. column Age_Cat parsed integer Sex character. Converting factor result informative output. Furthermore, Age_Cat encoded numerical value 1-3. adding respective names categories, output readable.","code":"# 1. Adjust censoring data$Status = abs(data$Status - 2)  # 2. Convert to factors and add the age categories data = data %>%    dplyr::mutate(      Age_Cat = recode_factor(Age_Cat,                              `1` = \"<18yr\", `2` =\"18yr-60yr\", `3` = \">60yr\"),      Sex     = as.factor(Sex))  data"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-live-demo","dir":"Articles > Interactive_tte_tutorial","previous_headings":"The study and the data","what":"Live Demo","title":"visR","text":"","code":"# Live demo"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-generation-of-summary-tables","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Generation of summary tables","title":"visR","text":"typical clinical report contains demography table, detailing distribution patients across different arms clinical trial. Commonly reported variables age, sex baseline disease characteristics. table sometimes referred Table 1. visR provides set functions generate render tables. rendering, visR enforces specification data source title. functions demonstrated demonstrated . default, table generated whole population stratifier can defined. Exercise 1: Generate table using Sex stratifier. Exercise 2: Generate table using Sex Age_Cat stratifier. Exercise 3: Add additional footnote table.","code":"# Define meta-data DATASOURCE = \"https://github.com/vntkumar8/covid-survival\" TITLE = \"Indian Covid Data\"  data %>% tableone(title = TITLE, datasource = DATASOURCE) # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-note","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Generation of summary tables","what":"Note","title":"visR","text":"notice tables contain NAs stratifier split . workaround first get table 1 data.frame, remove stratifier, render . examples demonstrates modular built compatibility visR. tableone command actually wrapper around functions get_tableone builds data.frame summary statistics render command displays nicely. (fixed upcoming release.)","code":"data %>%    get_tableone(strata = c(\"Sex\", \"Age_Cat\")) %>%    filter(variable != \"Sex\" & variable != \"Age_Cat\") %>%    render(title = TITLE, datasource = DATASOURCE)"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-fitting-survival-models","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Fitting survival models","title":"visR","text":"mentioned, visR defaults following CDISC ADaM standard. data set uses well, estimating plotting survival curves particularly straightforward.","code":"# The dataset that comes with visR is cdisc ADaM compliant  mod = visR::adtte %>% estimate_KM()   # When no strata is specified, the overall patient cohort is analysed mod  mod %>% visr()"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-survival-analysis-of-the-indian-covid-data","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Fitting survival models","what":"Survival analysis of the Indian COVID data","title":"visR","text":"Since ADaM standards utilized beyond clinical trial analysis reporting,, functions visR parameters allowing specification respective column names without modifying actual data. model estimated, different summary statistics easy get_ Exercise 1: Calculate quantiles model.","code":"mod = data %>% estimate_KM(strata = \"Sex\", CNSR = \"Status\", AVAL = \"Stay\") mod mod %>% get_pvalue() # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-plotting-survival-models","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Plotting survival models","title":"visR","text":"survival models can easily plotted using visR. visR function provides several convenient functions adjust aesthetics plot. Exercise 1: Change stratifier survival model Age_Cat. Exercise 2: Plot new model.","code":"mod %>% visr() mod %>%   visr(legend_position = \"top\",        x_label = \"\") mod %>%   visr(legend_position = \"top\",        x_units = \"years\") mod %>%   visr(legend_position = \"top\",        fun = \"log\") # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-improving-survival-plots","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Plotting survival models","what":"Improving survival plots","title":"visR","text":"top , visR provides several functions improve plots. , example, easy add confidence intervals censoring markers. Exercise 1: Add markers censoring. (Hint: functions adding statistics plot visR usually start add_). Also, function provided annotate plot additional information. Exercise 1: Add information statistical significance tests survival plot. (Hint: Combine add_annotation() get_pvalue()).","code":"mod %>%   visr() %>%   add_CI() # Have fun! mod %>%   visr() %>%   visR::add_annotation(label = \"Hello world\", xmin = 0, ymin = 0.5) # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-styling","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Plotting survival models","what":"Styling","title":"visR","text":"Particular attention paid stay compatible ggplot2 ecosystem. Therefore, created objects can example styled like ggplot. Exercise 1: Try recreate Figure 5 Indian Covid data publication close possible using learned now. Alternatively, can create visually pleasing variant.","code":"gg = mod %>% visr()     gg + ggplot2::theme_dark() # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-combining-plots-with-summary-statistics","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Combining plots with summary statistics","title":"visR","text":"Another good practice provide summary statistics accompany survival plot. purpose, visR provides handy function can appended normal piping framework. NOTE: breaks compatibility ggplot2 functions. Therefore, styling happen adding risktable. Analogous functions creating showing Table 1, risktables can also generated independently (using get_ function) can rendered. Exercise 1: Create render risktable survival model","code":"mod %>%   visr() %>%   add_risktable(statlist = c(\"n.risk\", \"n.event\", \"n.censor\")) # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-advanced-usage","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Advanced usage","title":"visR","text":"demonstrate advanced options might useful cases.","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-attrition","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Advanced usage","what":"Attrition","title":"visR","text":"Often subgroups population analysed complete data sets need filtered according specific criteria. function get_attrition allows list filter statements explanations, can visualized table (render) plotted (visr). function apply_attrition filters data.frame.","code":"data$Patient = 1:nrow(data)   condition = \"Age_Cat != '<18yr'\"  filters = data %>% get_attrition(subject_column_name = \"Patient\",                                   criteria_descriptions = \"18 and older\",                                   criteria_conditions = condition)   filters %>% visr()  filters %>% render(title = \"Attrition Table\", datasource = DATASOURCE)  data %>% apply_attrition(condition)"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-custom-summary-functions-for-the-table-1","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Advanced usage","what":"Custom summary functions for the Table 1","title":"visR","text":"tableone function accepts custom functions run columns. purpose, different possible data-type considered. simplicity, behaviour numeric columns changed example .","code":"# This is basically the default function but doesn't return the missing values. my_func = function(x) {      if (is.numeric(x)) {        dat = list(       `Mean (SD)` = paste0(format(mean(x, na.rm = TRUE), digits = 3),                             \" (\",                             format(sd(x, na.rm = TRUE), digits = 3),                             \")\"),       `Median (IQR)` = paste0(format(median(x, na.rm = TRUE), digits = 3),                                \" (\",                                format(quantile(x, probs=0.25, na.rm = TRUE), digits = 3),                               \"-\",                                format(quantile(x, probs=0.75, na.rm = TRUE), digits = 3),                                \")\"),       `Min-max` = paste0(format(min(x, na.rm = TRUE), digits = 3),                           \"-\", format(max(x, na.rm = TRUE), digits = 3))     )          list(dat)        } else {          visR::summarize_short(x)        } }  data %>%    get_tableone(strata = c(\"Sex\", \"Age_Cat\"), summary_function = my_func) %>%    filter(variable != \"Sex\" & variable != \"Age_Cat\") %>%    render(title = TITLE, datasource = DATASOURCE)"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-different-rendering-engines-for-table-output","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Advanced usage","what":"Different rendering engines for table output","title":"visR","text":"default, tables rendered gt. However, options like example kable dt also implemented.","code":"mod %>%    get_pvalue() %>%    render(title = TITLE,           datasource = DATASOURCE,           engine = \"kable\") # dt launches a child process which crashes on shinyapps.io?"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-possible-solution-to-the-exercise-for-recreating-figure-5","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Advanced usage","what":"Possible solution to the exercise for recreating figure 5","title":"visR","text":"library(\"learnr\") library(\"visR\") library(\"ggplot2\") library(\"dplyr\") tutorial_options(exercise.timelimit = 120)  # prep work one chunk well can always reference one  # \"exercise-setup\" chunk data = read.csv(\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\") data$Status = abs(data$Status - 2) data = data %>%    dplyr::mutate(      Age_Cat = recode_factor(Age_Cat,                              `1` = \"<18yr\", `2` =\"18yr-60yr\", `3` = \">60yr\"),      Sex     = .factor(Sex)) DATASOURCE = \"https://github.com/vntkumar8/covid-survival\" TITLE = \"Indian Covid Data\" mod = data %>% estimate_KM(strata = \"Sex\", CNSR = \"Status\", AVAL = \"Stay\")  assign(\"data\",       data,       envir = globalenv()) assign(\"DATASOURCE\", DATASOURCE, envir = globalenv()) assign(\"TITLE\",      TITLE,      envir = globalenv()) assign(\"mod\",        mod,        envir = globalenv())  theme_set(theme_bw())   learnr:::register_http_handlers(session, metadata = NULL)  learnr:::prepare_tutorial_state(session)  learnr:::i18n_observe_tutorial_language(input, session)  session$onSessionEnded(function() {         learnr:::event_trigger(session, \"session_stop\")       })  `tutorial-exercise-load-data-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-load-data-code-editor`)), session) output$`tutorial-exercise-load-data-output` <- renderUI({   `tutorial-exercise-load-data-result`() })  learnr:::store_exercise_cache(structure(list(label = \"load-data\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = NULL, chunks = list(list(label = \"load-data\",      code = \"\\n# First, set default ggplot2 theme\\ntheme_set(theme_bw())\\n\\n# , directly load data GitHub repository\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\n\\ndata\\n\",      opts = list(label = \"\\\"load-data\\\"\", exercise = \"TRUE\", exercise.eval = \"TRUE\",          exercise.startover = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = TRUE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"load-data\", exercise = TRUE,          exercise.eval = TRUE, exercise.startover = FALSE, code = c(\"\",          \"# First, set default ggplot2 theme\", \"theme_set(theme_bw())\",          \"\", \"# , directly load data GitHub repository\",          \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",          \"\", \"data\", \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"load-data, exercise = TRUE, exercise.eval = TRUE, exercise.startover = FALSE\",          fig.alt = NULL, fig.num = 0, exercise.df_print = \"default\",          exercise.checker = \"NULL\"), engine = \"r\", version = \"4\"), class = c(\"r\",  \"tutorial_exercise\")))  `tutorial-exercise-prepare-data-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-prepare-data-code-editor`)), session) output$`tutorial-exercise-prepare-data-output` <- renderUI({   `tutorial-exercise-prepare-data-result`() })  learnr:::store_exercise_cache(structure(list(label = \"prepare-data\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"\\n# First, set default ggplot2 theme\\ntheme_set(theme_bw())\\n\\n# , directly load data GitHub repository\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\n\\ndata\\n\",      chunks = list(list(label = \"load-data\", code = \"\\n# First, set default ggplot2 theme\\ntheme_set(theme_bw())\\n\\n# , directly load data GitHub repository\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\n\\ndata\\n\",          opts = list(label = \"\\\"load-data\\\"\", exercise = \"TRUE\",              exercise.eval = \"TRUE\", exercise.startover = \"FALSE\"),          engine = \"r\"), list(label = \"prepare-data\", code = \"\\n# 1. Adjust censoring\\ndata$Status = abs(data$Status - 2)\\n\\n# 2. Convert factors add age categories\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\n\\ndata\",          opts = list(label = \"\\\"prepare-data\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"load-data\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"prepare-data\", exercise = TRUE,          exercise.setup = \"load-data\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# 1. Adjust censoring\",          \"data$Status = abs(data$Status - 2)\", \"\", \"# 2. Convert factors add age categories\",          \"data = data %>%\", \"   dplyr::mutate(\", \"     Age_Cat = recode_factor(Age_Cat,\",          \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",          \"     Sex     = .factor(Sex))\", \"\", \"data\"), .width.px = 700,          .height.px = 432.632880098888, params.src = \"prepare-data, exercise = TRUE, exercise.setup = \\\"load-data\\\", exercise.startover = FALSE, exercise.eval = FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-live-demo-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-live-demo-code-editor`)), session) output$`tutorial-exercise-live-demo-output` <- renderUI({   `tutorial-exercise-live-demo-result`() })  learnr:::store_exercise_cache(structure(list(label = \"live-demo\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"live-demo\", code = \"# Live demo\",          opts = list(label = \"\\\"live-demo\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\", exercise.lines = \"30\", fig.width = \"9\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 9, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 864,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"live-demo\", exercise = TRUE,          exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, exercise.lines = 30, code = \"# Live demo\",          .width.px = 864, .height.px = 432.632880098888,          params.src = \"live-demo, exercise = TRUE, exercise.setup = \\\"setup\\\", exercise.startover = FALSE, exercise.eval = FALSE, exercise.lines = 30, fig.width=9\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-basic-tableone-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-basic-tableone-code-editor`)), session) output$`tutorial-exercise-basic-tableone-output` <- renderUI({   `tutorial-exercise-basic-tableone-result`() })  learnr:::store_exercise_cache(structure(list(label = \"basic-tableone\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"basic-tableone\", code = \"\\n# Define meta-data\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\n\\ndata %>% tableone(title = TITLE, datasource = DATASOURCE)\",          opts = list(label = \"\\\"basic-tableone\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"basic-tableone\", exercise = TRUE,          exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# Define meta-data\",          \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",          \"TITLE = \\\"Indian Covid Data\\\"\", \"\", \"data %>% tableone(title = TITLE, datasource = DATASOURCE)\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"basic-tableone, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-1-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-1-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-1-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-1-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-1\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-1\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-1\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-1\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-1, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-fixed-tableone-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-fixed-tableone-code-editor`)), session) output$`tutorial-exercise-fixed-tableone-output` <- renderUI({   `tutorial-exercise-fixed-tableone-result`() })  learnr:::store_exercise_cache(structure(list(label = \"fixed-tableone\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"fixed-tableone\", code = \"\\ndata %>% \\n  get_tableone(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\")) %>% \\n  filter(variable != \\\"Sex\\\" & variable != \\\"Age_Cat\\\") %>% \\n  render(title = TITLE, datasource = DATASOURCE)\",          opts = list(label = \"\\\"fixed-tableone\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"fixed-tableone\", exercise = TRUE,          exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"data %>% \", \"  get_tableone(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\")) %>% \",          \"  filter(variable != \\\"Sex\\\" & variable != \\\"Age_Cat\\\") %>% \",          \"  render(title = TITLE, datasource = DATASOURCE)\"),          .width.px = 700, .height.px = 432.632880098888,          params.src = \"fixed-tableone, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-cdisc-survival-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-cdisc-survival-code-editor`)), session) output$`tutorial-exercise-cdisc-survival-output` <- renderUI({   `tutorial-exercise-cdisc-survival-result`() })  learnr:::store_exercise_cache(structure(list(label = \"cdisc-survival\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"cdisc-survival\", code = \"\\n# dataset comes visR cdisc ADaM compliant \\nmod = visR::adtte %>% estimate_KM() \\n\\n# strata specified, overall patient cohort analysed\\nmod\\n\\nmod %>% visr()\\n\",          opts = list(label = \"\\\"cdisc-survival\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"cdisc-survival\", exercise = TRUE,          exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# dataset comes visR cdisc ADaM compliant \",          \"mod = visR::adtte %>% estimate_KM() \", \"\", \"# strata specified, overall patient cohort analysed\",          \"mod\", \"\", \"mod %>% visr()\", \"\"), .width.px = 700,          .height.px = 432.632880098888, params.src = \"cdisc-survival, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-indian-survival-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-indian-survival-code-editor`)), session) output$`tutorial-exercise-indian-survival-output` <- renderUI({   `tutorial-exercise-indian-survival-result`() })  learnr:::store_exercise_cache(structure(list(label = \"indian-survival\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"indian-survival\", code = \"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\nmod\",          opts = list(label = \"\\\"indian-survival\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"indian-survival\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",          \"mod\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"indian-survival, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-get_pvalue-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-get_pvalue-code-editor`)), session) output$`tutorial-exercise-get_pvalue-output` <- renderUI({   `tutorial-exercise-get_pvalue-result`() })  learnr:::store_exercise_cache(structure(list(label = \"get_pvalue\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"get_pvalue\", code = \"\\nmod %>% get_pvalue()\\n\",          opts = list(label = \"\\\"get_pvalue\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"get_pvalue\", exercise = TRUE,          exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>% get_pvalue()\",          \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"get_pvalue, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise_1-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise_1-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise_1-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise_1-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise_1\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise_1\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise_1\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise_1\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise_1, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-basic-survival-plot-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-basic-survival-plot-code-editor`)), session) output$`tutorial-exercise-basic-survival-plot-output` <- renderUI({   `tutorial-exercise-basic-survival-plot-result`() })  learnr:::store_exercise_cache(structure(list(label = \"basic-survival-plot\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"basic-survival-plot\", code = \"\\nmod %>% visr()\\n\",          opts = list(label = \"\\\"basic-survival-plot\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"basic-survival-plot\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>% visr()\",          \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"basic-survival-plot, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-basic-survival-plot-options-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-basic-survival-plot-options-code-editor`)), session) output$`tutorial-exercise-basic-survival-plot-options-output` <- renderUI({   `tutorial-exercise-basic-survival-plot-options-result`() })  learnr:::store_exercise_cache(structure(list(label = \"basic-survival-plot-options\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"basic-survival-plot-options\",          code = \"\\nmod %>%\\n  visr(legend_position = \\\"top\\\",\\n       x_label = \\\"\\\")\\nmod %>%\\n  visr(legend_position = \\\"top\\\",\\n       x_units = \\\"years\\\")\\nmod %>%\\n  visr(legend_position = \\\"top\\\",\\n       fun = \\\"log\\\")\\n\",          opts = list(label = \"\\\"basic-survival-plot-options\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"basic-survival-plot-options\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>%\", \"  visr(legend_position = \\\"top\\\",\",          \"       x_label = \\\"\\\")\", \"mod %>%\", \"  visr(legend_position = \\\"top\\\",\",          \"       x_units = \\\"years\\\")\", \"mod %>%\", \"  visr(legend_position = \\\"top\\\",\",          \"       fun = \\\"log\\\")\", \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"basic-survival-plot-options, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-3-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-3-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-3-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-3-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-3\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-3\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-3\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-3\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-3, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-add-x-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-add-x-code-editor`)), session) output$`tutorial-exercise-survival-plot-add-x-output` <- renderUI({   `tutorial-exercise-survival-plot-add-x-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-add-x\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-add-x\", code = \"\\nmod %>%\\n  visr() %>%\\n  add_CI()\\n\",          opts = list(label = \"\\\"survival-plot-add-x\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-add-x\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>%\", \"  visr() %>%\",          \"  add_CI()\", \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-add-x, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-4-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-4-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-4-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-4-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-4\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-4\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-4\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-4\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-4, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-annotate-basic-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-annotate-basic-code-editor`)), session) output$`tutorial-exercise-survival-plot-annotate-basic-output` <- renderUI({   `tutorial-exercise-survival-plot-annotate-basic-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-annotate-basic\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-annotate-basic\",          code = \"\\nmod %>%\\n  visr() %>%\\n  visR::add_annotation(label = \\\"Hello world\\\", xmin = 0, ymin = 0.5)\\n\",          opts = list(label = \"\\\"survival-plot-annotate-basic\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-annotate-basic\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>%\", \"  visr() %>%\",          \"  visR::add_annotation(label = \\\"Hello world\\\", xmin = 0, ymin = 0.5)\",          \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-annotate-basic, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-5-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-5-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-5-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-5-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-5\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-5\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-5\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-5\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-5, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-style-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-style-code-editor`)), session) output$`tutorial-exercise-survival-plot-style-output` <- renderUI({   `tutorial-exercise-survival-plot-style-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-style\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-style\", code = \"\\ngg = mod %>% visr() \\n  \\ngg + ggplot2::theme_dark()\\n\",          opts = list(label = \"\\\"survival-plot-style\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-style\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"gg = mod %>% visr() \",          \"  \", \"gg + ggplot2::theme_dark()\", \"\"), .width.px = 700,          .height.px = 432.632880098888, params.src = \"survival-plot-style, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-6-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-6-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-6-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-6-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-6\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-6\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-6\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-6\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-6, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-risktable-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-risktable-code-editor`)), session) output$`tutorial-exercise-survival-plot-risktable-output` <- renderUI({   `tutorial-exercise-survival-plot-risktable-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-risktable\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-risktable\",          code = \"\\nmod %>%\\n  visr() %>%\\n  add_risktable(statlist = c(\\\"n.risk\\\", \\\"n.event\\\", \\\"n.censor\\\"))\\n\",          opts = list(label = \"\\\"survival-plot-risktable\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\", fig.height = \"7\"), engine = \"r\")),      code_check = NULL, error_check = NULL, check = NULL, solution = NULL,      tests = NULL, options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 7,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-risktable\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>%\", \"  visr() %>%\",          \"  add_risktable(statlist = c(\\\"n.risk\\\", \\\"n.event\\\", \\\"n.censor\\\"))\",          \"\"), .width.px = 700, .height.px = 672, params.src = \"survival-plot-risktable, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE, fig.height = 7\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-7-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-7-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-7-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-7-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-7\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-7\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-7\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-7\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-7, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-basic-attrition-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-basic-attrition-code-editor`)), session) output$`tutorial-exercise-basic-attrition-output` <- renderUI({   `tutorial-exercise-basic-attrition-result`() })  learnr:::store_exercise_cache(structure(list(label = \"basic-attrition\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"basic-attrition\", code = \"\\ndata$Patient = 1:nrow(data) \\n\\ncondition = \\\"Age_Cat != '<18yr'\\\"\\n\\nfilters = data %>% get_attrition(subject_column_name = \\\"Patient\\\", \\n                                 criteria_descriptions = \\\"18 older\\\", \\n                                 criteria_conditions = condition) \\n\\nfilters %>% visr()\\n\\nfilters %>% render(title = \\\"Attrition Table\\\", datasource = DATASOURCE)\\n\\ndata %>% apply_attrition(condition)\\n\\n\",          opts = list(label = \"\\\"basic-attrition\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"basic-attrition\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"data$Patient = 1:nrow(data) \",          \"\", \"condition = \\\"Age_Cat != '<18yr'\\\"\", \"\", \"filters = data %>% get_attrition(subject_column_name = \\\"Patient\\\", \",          \"                                 criteria_descriptions = \\\"18 older\\\", \",          \"                                 criteria_conditions = condition) \",          \"\", \"filters %>% visr()\", \"\", \"filters %>% render(title = \\\"Attrition Table\\\", datasource = DATASOURCE)\",          \"\", \"data %>% apply_attrition(condition)\", \"\", \"\"), .width.px = 700,          .height.px = 432.632880098888, params.src = \"basic-attrition, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-custom-func-tableone-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-custom-func-tableone-code-editor`)), session) output$`tutorial-exercise-custom-func-tableone-output` <- renderUI({   `tutorial-exercise-custom-func-tableone-result`() })  learnr:::store_exercise_cache(structure(list(label = \"custom-func-tableone\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"custom-func-tableone\", code = \"\\n# basically default function return missing values.\\nmy_func = function(x) {\\n  \\n  (.numeric(x)) {\\n  \\n    dat = list(\\n      `Mean (SD)` = paste0(format(mean(x, na.rm = TRUE), digits = 3), \\n                           \\\" (\\\", \\n                           format(sd(x, na.rm = TRUE), digits = 3), \\n                           \\\")\\\"),\\n      `Median (IQR)` = paste0(format(median(x, na.rm = TRUE), digits = 3), \\n                              \\\" (\\\", \\n                              format(quantile(x, probs=0.25, na.rm = TRUE), digits = 3),\\n                              \\\"-\\\", \\n                              format(quantile(x, probs=0.75, na.rm = TRUE), digits = 3), \\n                              \\\")\\\"),\\n      `Min-max` = paste0(format(min(x, na.rm = TRUE), digits = 3), \\n                         \\\"-\\\", format(max(x, na.rm = TRUE), digits = 3))\\n    )\\n    \\n    list(dat)\\n    \\n  } else {\\n    \\n    visR::summarize_short(x)\\n    \\n  }\\n}\\n\\ndata %>% \\n  get_tableone(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\"), summary_function = my_func) %>% \\n  filter(variable != \\\"Sex\\\" & variable != \\\"Age_Cat\\\") %>% \\n  render(title = TITLE, datasource = DATASOURCE)\",          opts = list(label = \"\\\"custom-func-tableone\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\", exercise.lines = \"35\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"custom-func-tableone\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, exercise.lines = 35, code = c(\"\",          \"# basically default function return missing values.\",          \"my_func = function(x) {\", \"  \", \"  (.numeric(x)) {\",          \"  \", \"    dat = list(\", \"      `Mean (SD)` = paste0(format(mean(x, na.rm = TRUE), digits = 3), \",          \"                           \\\" (\\\", \", \"                           format(sd(x, na.rm = TRUE), digits = 3), \",          \"                           \\\")\\\"),\", \"      `Median (IQR)` = paste0(format(median(x, na.rm = TRUE), digits = 3), \",          \"                              \\\" (\\\", \", \"                              format(quantile(x, probs=0.25, na.rm = TRUE), digits = 3),\",          \"                              \\\"-\\\", \", \"                              format(quantile(x, probs=0.75, na.rm = TRUE), digits = 3), \",          \"                              \\\")\\\"),\", \"      `Min-max` = paste0(format(min(x, na.rm = TRUE), digits = 3), \",          \"                         \\\"-\\\", format(max(x, na.rm = TRUE), digits = 3))\",          \"    )\", \"    \", \"    list(dat)\", \"    \", \"  } else {\",          \"    \", \"    visR::summarize_short(x)\", \"    \", \"  }\",          \"}\", \"\", \"data %>% \", \"  get_tableone(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\"), summary_function = my_func) %>% \",          \"  filter(variable != \\\"Sex\\\" & variable != \\\"Age_Cat\\\") %>% \",          \"  render(title = TITLE, datasource = DATASOURCE)\"),          .width.px = 700, .height.px = 432.632880098888,          params.src = \"custom-func-tableone, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE, exercise.lines=35\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-rendering-engines-dt-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-rendering-engines-dt-code-editor`)), session) output$`tutorial-exercise-rendering-engines-dt-output` <- renderUI({   `tutorial-exercise-rendering-engines-dt-result`() })  learnr:::store_exercise_cache(structure(list(label = \"rendering-engines-dt\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"rendering-engines-dt\", code = \"\\nmod %>% \\n  get_pvalue() %>% \\n  render(title = TITLE, \\n         datasource = DATASOURCE, \\n         engine = \\\"kable\\\") # dt launches child process crashes shinyapps.io? \\n\",          opts = list(label = \"\\\"rendering-engines-dt\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"rendering-engines-dt\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>% \", \"  get_pvalue() %>% \",          \"  render(title = TITLE, \", \"         datasource = DATASOURCE, \",          \"         engine = \\\"kable\\\") # dt launches child process crashes shinyapps.io? \",          \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"rendering-engines-dt, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-solution-figure5-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-solution-figure5-code-editor`)), session) output$`tutorial-exercise-solution-figure5-output` <- renderUI({   `tutorial-exercise-solution-figure5-result`() })  learnr:::store_exercise_cache(structure(list(label = \"solution-figure5\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"solution-figure5\", code = \"\\nm = data %>% estimate_KM(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\"),\\n                         CNSR = \\\"Status\\\", \\n                         AVAL = \\\"Stay\\\")\\n\\nhalf_surv_df = m %>% \\n  get_quantile(probs = 0.5) %>% \\n  dplyr::filter(quantity == \\\"quantile\\\") %>%\\n  dplyr::mutate(pos1 = `50`, pos2 = 0.5) %>%\\n  dplyr::select(-c(quantity, `50`))\\n  \\nhalf_surv_df_horizontal = half_surv_df %>% dplyr::mutate(pos1 = 0)\\n  \\nhalf_surv_df_vertical = half_surv_df %>% dplyr::mutate(pos2 = 0)\\n  \\nhalf_surv_df = rbind(half_surv_df, half_surv_df_horizontal, half_surv_df_vertical)  \\n  \\nm %>%\\n  visr() %>%\\n  add_CI() %>%\\n  add_CNSR() +\\n  ggplot2::geom_line(data = half_surv_df, ggplot2::aes(pos1, pos2, group = strata), linetype = \\\"dashed\\\")\\n\",          opts = list(label = \"\\\"solution-figure5\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e311ead4cc/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"solution-figure5\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"m = data %>% estimate_KM(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\"),\",          \"                         CNSR = \\\"Status\\\", \", \"                         AVAL = \\\"Stay\\\")\",          \"\", \"half_surv_df = m %>% \", \"  get_quantile(probs = 0.5) %>% \",          \"  dplyr::filter(quantity == \\\"quantile\\\") %>%\", \"  dplyr::mutate(pos1 = `50`, pos2 = 0.5) %>%\",          \"  dplyr::select(-c(quantity, `50`))\", \"  \", \"half_surv_df_horizontal = half_surv_df %>% dplyr::mutate(pos1 = 0)\",          \"  \", \"half_surv_df_vertical = half_surv_df %>% dplyr::mutate(pos2 = 0)\",          \"  \", \"half_surv_df = rbind(half_surv_df, half_surv_df_horizontal, half_surv_df_vertical)  \",          \"  \", \"m %>%\", \"  visr() %>%\", \"  add_CI() %>%\", \"  add_CNSR() +\",          \"  ggplot2::geom_line(data = half_surv_df, ggplot2::aes(pos1, pos2, group = strata), linetype = \\\"dashed\\\")\",          \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"solution-figure5, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))","code":"m = data %>% estimate_KM(strata = c(\"Sex\", \"Age_Cat\"),                          CNSR = \"Status\",                           AVAL = \"Stay\")  half_surv_df = m %>%    get_quantile(probs = 0.5) %>%    dplyr::filter(quantity == \"quantile\") %>%   dplyr::mutate(pos1 = `50`, pos2 = 0.5) %>%   dplyr::select(-c(quantity, `50`))    half_surv_df_horizontal = half_surv_df %>% dplyr::mutate(pos1 = 0)    half_surv_df_vertical = half_surv_df %>% dplyr::mutate(pos2 = 0)    half_surv_df = rbind(half_surv_df, half_surv_df_horizontal, half_surv_df_vertical)      m %>%   visr() %>%   add_CI() %>%   add_CNSR() +   ggplot2::geom_line(data = half_surv_df, ggplot2::aes(pos1, pos2, group = strata), linetype = \"dashed\")"},{"path":"https://openpharma.github.io/visR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Mark Baillie. Author, maintainer, copyright holder. Diego Saldana. Author. Charlotta Fruechtenicht. Author. Marc Vandemeulebroecke. Author. Thanos Siadimas. Author. Pawel Kawski. Author. Steven Haesendonckx. Author. James Black. Author. Pelagia Alexandra Papadopoulou. Author. Tim Treis. Author. Rebecca Albrecht. Author. Ardalan Mirshani. Contributor. Daniel D. Sjoberg. Author.","code":""},{"path":"https://openpharma.github.io/visR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Baillie M, Saldana D, Fruechtenicht C, Vandemeulebroecke M, Siadimas T, Kawski P, Haesendonckx S, Black J, Alexandra Papadopoulou P, Treis T, Albrecht R, Sjoberg D (2023). visR: Clinical Graphs Tables Adhering Graphical Principles. R package version 0.4.0, https://github.com/openpharma/visR.","code":"@Manual{,   title = {visR: Clinical Graphs and Tables Adhering to Graphical Principles},   author = {Mark Baillie and Diego Saldana and Charlotta Fruechtenicht and Marc Vandemeulebroecke and Thanos Siadimas and Pawel Kawski and Steven Haesendonckx and James Black and Pelagia {Alexandra Papadopoulou} and Tim Treis and Rebecca Albrecht and Daniel D. Sjoberg},   year = {2023},   note = {R package version 0.4.0},   url = {https://github.com/openpharma/visR}, }"},{"path":"https://openpharma.github.io/visR/contributing.html","id":null,"dir":"","previous_headings":"","what":"Contributing to visR","title":"Contributing to visR","text":"detailed info contributing, please see development contributing guide.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to visR","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to visR","text":"want make bigger contribution, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to visR","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"openpharma/visR\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/contributing.html","id":"general-coding-principles","dir":"","previous_headings":"Code style","what":"General coding principles","title":"Contributing to visR","text":"new functions preferably built using widely adapted tidyverse (please see namespace allowed package dependencies). Dependencies kept minimum. Please don’t restyle code nothing PR. Use roxygen2, Markdown syntax, updating creating documentation. Use testthat unit tests. Contributions accompanied extensive user acceptance testing. Please reach team need help. CRAN requires us use TRUE/FALSE, don’t use T/F. Subset using ‘[[’ rather ‘$’ avoid surprises partial matching","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"data-manipulation","dir":"","previous_headings":"Code style","what":"Data Manipulation","title":"Contributing to visR","text":"Data expected present tidy form (one row per observation) unless otherwise noted. Dataframes, rather tibbles returned functions. magrittr pipe (%>%) can used consecutive operations data. models broom package easily converts key information models tidy data tibbles subsequent wrangling visualisation. Note implemented methods lazy extract key information, used common downstream processing. visR custom tidiers extract informtation model objects dataframe users information required downstream processing.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"figures","dir":"","previous_headings":"Code style","what":"Figures","title":"Contributing to visR","text":"Plotting implemented using ggplot2 unless possible. Interactivity may provided using ggplotly - needed plotly packages.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"tables","dir":"","previous_headings":"Code style","what":"Tables","title":"Contributing to visR","text":"Tables always also available raw dataframes.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"testing","dir":"","previous_headings":"","what":"Testing","title":"Contributing to visR","text":"Write tests soon function somewhat usable state. Improving easier starting. Add new test every issue/bug identified. Use vdiffr comparison anything plotted library manually curated plots. Educate break: Rather just stopping parameters provided don’t make sense, like shape = NULL, warn user required argument set reasonable default.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"package-maintenance","dir":"","previous_headings":"","what":"Package maintenance","title":"Contributing to visR","text":"integrated several “watchdogs” testing routine ensure adherence certain standards ’ve set. routines scan codebase possible style violations:","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"cran-watchdog","dir":"","previous_headings":"Package maintenance","what":"CRAN watchdog","title":"Contributing to visR","text":"successful submission CRAN, certain rules set place CRAN team, example usage TRUE/FALSE T/F. continuously ensure compliance , CRAN watchdog scans respective files violations .","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"validation-watchdog-in-pr","dir":"","previous_headings":"Package maintenance","what":"Validation watchdog (in PR)","title":"Contributing to visR","text":"want make easy use package stringent environment might require packages validated. Based discussions similar projects decided implement traceability testing files. Therefore, tests well last change , automatically written files potential scraping. details last change gathered usage git log table contents unit test specifications generated based strings actual tests.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to visR","text":"Please note visR project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://openpharma.github.io/visR/index.html","id":"visr-","dir":"","previous_headings":"","what":"Clinical Graphs and Tables Adhering to Graphical Principles","title":"Clinical Graphs and Tables Adhering to Graphical Principles","text":"goal visR enable fit--purpose, reusable clinical medical research focused visualizations tables sensible defaults based sound graphical principles. Package documentation","code":""},{"path":"https://openpharma.github.io/visR/index.html","id":"motivation","dir":"","previous_headings":"","what":"Motivation","title":"Clinical Graphs and Tables Adhering to Graphical Principles","text":"using common package visualising data analysis results clinical development process, want positive influence choice visualisation making easy explore different visualisation use impactful visualisations fit--purpose effective visual communication making easy implement best practices judging visualisation chose research question, want facilitate support good practice. can read philosophy architecture repo wiki.","code":""},{"path":"https://openpharma.github.io/visR/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Clinical Graphs and Tables Adhering to Graphical Principles","text":"easiest way get visR install CRAN: Install development version GitHub :","code":"install.packages(\"visR\") # defaults to main branch devtools::install_github(\"openpharma/visR\")"},{"path":"https://openpharma.github.io/visR/index.html","id":"cite-visr","dir":"","previous_headings":"","what":"Cite visR","title":"Clinical Graphs and Tables Adhering to Graphical Principles","text":"","code":"> citation(\"visR\")"},{"path":"https://openpharma.github.io/visR/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Clinical Graphs and Tables Adhering to Graphical Principles","text":"Please note visR project released Contributor Code Conduct. contributing project, agree abide terms. Thank contributors: @AlexandraP-21, @ardeeshany, @bailliem, @cschaerfe, @ddsjoberg, @diego-s, @epijim, @galachad, @gdario, @ginberg, @jameshunterbr, @jinjooshim, @joanacmbarros, @Jonnie-Bevan, @kawap, @kawap93, @kentm4, @krystian8207, @kzalocusky, @lcomm, @lesniewa, @prabhushanmup, @rebecca-albrecht, @SHAESEN2, @thanos-siadimas, @therneau, @thomas-neitmann, @timtreis, @yonicd","code":""},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Survival Object from CDISC Data — Surv_CNSR","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"aim Surv_CNSR() map inconsistency convention survival package CDISC ADaM ADTTE data model. function creates survival object (e.g. survival::Surv()) uses CDISC ADaM ADTTE coding conventions converts arguments status/event variable convention used survival package. AVAL CNSR arguments passed survival::Surv(time = AVAL, event = 1 - CNSR, type = \"right\", origin = 0).","code":""},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"","code":"Surv_CNSR(AVAL, CNSR)"},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"AVAL follow-time. follow-time assumed originate zero. argument passed, default value column/vector named AVAL. CNSR censoring indicator 1=censored 0=death/event. argument passed, default value column/vector named CNSR.","code":""},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"Object class 'Surv'","code":""},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"Surv_CNSR() function creates survival object utilizing expected data structure CDISC ADaM ADTTE data model, mapping CDISC ADaM ADTTE coding conventions expected status/event variable convention used survival package---specifically, coding convention used status/event indicator. survival package expects status/event indicator following format: 0=alive, 1=dead. accepted choices TRUE/FALSE (TRUE = death) 1/2 (2=death). final risky option omit indicator variable, case subjects assumed event. CDISC ADaM ADTTE data model adopts different coding convention event/status indicator. Using convention, event/status variable named 'CNSR' uses following coding: censor = 1, status/event = 0.","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"","code":"# Use the `Surv_CNSR()` function with visR functions adtte %>%   visR::estimate_KM(formula = visR::Surv_CNSR() ~ SEX) #> Call: ~survival::survfit(formula = visR::Surv_CNSR() ~ SEX, data = data) #>  #>         n events median 0.95LCL 0.95UCL #> SEX=F 143     80     64      47      96 #> SEX=M 111     72     41      30      57  # Use the `Surv_CNSR()` function with functions from other packages as well survival::survfit(visR::Surv_CNSR() ~ SEX, data = adtte) #> Call: survfit(formula = visR::Surv_CNSR() ~ SEX, data = adtte) #>  #>         n events median 0.95LCL 0.95UCL #> SEX=F 143     80     64      47      96 #> SEX=M 111     72     41      30      57 survival::survreg(visR::Surv_CNSR() ~ SEX + AGE, data = adtte) %>%   broom::tidy() #> # A tibble: 4 × 5 #>   term        estimate std.error statistic    p.value #>   <chr>          <dbl>     <dbl>     <dbl>      <dbl> #> 1 (Intercept)   3.97      0.993      4.00  0.0000645  #> 2 SEXM         -0.412     0.226     -1.82  0.0689     #> 3 AGE           0.0131    0.0131     0.997 0.319      #> 4 Log(scale)    0.326     0.0676     4.83  0.00000139"},{"path":"https://openpharma.github.io/visR/reference/add_CI.html","id":null,"dir":"Reference","previous_headings":"","what":"Add confidence interval (CI) to visR object — add_CI","title":"Add confidence interval (CI) to visR object — add_CI","text":"Method add pointwise confidence intervals object created visR S3 method. method set use pipe %>%. two options display CI's, \"ribbon\" \"step\" lines. default method available moment.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add confidence interval (CI) to visR object — add_CI","text":"","code":"add_CI(gg, ...)  # S3 method for ggsurvfit add_CI(gg, alpha = 0.1, style = \"ribbon\", linetype, ...)  # S3 method for ggtidycuminc add_CI(gg, alpha = 0.1, style = \"ribbon\", linetype, ...)"},{"path":"https://openpharma.github.io/visR/reference/add_CI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add confidence interval (CI) to visR object — add_CI","text":"gg ggplot created visR ... arguments passed method modify geom_ribbon alpha aesthetic ggplot2 geom_ribbon. Default 0.1. style aesthetic ggplot2 geom_ribbon. Default \"ribbon\". alternative option \"step\" uses line display interval bounds. linetype aesthetic ggplot2 geom_ribbon.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add confidence interval (CI) to visR object — add_CI","text":"Pointwise confidence interval overlayed visR ggplot","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add confidence interval (CI) to visR object — add_CI","text":"","code":"library(visR)  # Estimate KM curves by treatment group survfit_object <- survival::survfit(data = adtte, survival::Surv(AVAL, 1 - CNSR) ~ TRTP)  ## plot without confidence intervals (CI) p <- visR::visr(survfit_object) p   # add CI to plot with default settings p %>% add_CI()   # change transparency of CI ribbon p %>% add_CI(alpha = 0.9, style = \"ribbon\")   # plot CI as a step line instead of ribbon p %>% add_CI(alpha = 0.1, style = \"step\")   # change linetype of CI p %>% add_CI(style = \"step\", linetype = 1)"},{"path":"https://openpharma.github.io/visR/reference/add_CNSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Add censoring symbols to a visR object — add_CNSR","title":"Add censoring symbols to a visR object — add_CNSR","text":"Add censoring symbols visR ggplot S3 method. S3 method adding censoring symbols visR ggplot. method set use pipe %>%. default method available moment.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CNSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add censoring symbols to a visR object — add_CNSR","text":"","code":"add_CNSR(gg, ...)  # S3 method for ggsurvfit add_CNSR(gg, shape = 3, size = 2, ...)  # S3 method for ggtidycuminc add_CNSR(gg, shape = 3, size = 2, ...)"},{"path":"https://openpharma.github.io/visR/reference/add_CNSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add censoring symbols to a visR object — add_CNSR","text":"gg ggplot created visR ... arguments passed method modify geom_point shape aesthetic ggplot2 geom_point. Default 3. size aesthetic ggplot2 geom_point. Default 2.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CNSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add censoring symbols to a visR object — add_CNSR","text":"Censoring symbols overlayed visR ggplot","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CNSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add censoring symbols to a visR object — add_CNSR","text":"","code":"library(visR)  # Estimate KM curves by treatment group survfit_object <- survival::survfit(data = adtte, survival::Surv(AVAL, 1 - CNSR) ~ TRTP)  ## plot without confidence intervals p <- visR::visr(survfit_object) p   # add censoring to plot p %>% visR::add_CNSR()   # change censor symbol shape p %>% visR::add_CNSR(shape = 1)   # change size and shape p %>% visR::add_CNSR(size = 4, shape = 2)"},{"path":"https://openpharma.github.io/visR/reference/add_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Add annotations to a visR object — add_annotation","title":"Add annotations to a visR object — add_annotation","text":"Wrapper around ggplot2::annotation_custom simplified annotation ggplot2 plots. function accepts string, dataframe, data.table, tibble customized objects class gtable places specified location ggplot. layout fixed: bold column headers plain body. font size type can chosen. initial plot individual annotation stored attribute component final object.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add annotations to a visR object — add_annotation","text":"","code":"add_annotation(   gg = NULL,   label = NULL,   base_family = \"sans\",   base_size = 11,   xmin = -Inf,   xmax = Inf,   ymin = -Inf,   ymax = Inf )"},{"path":"https://openpharma.github.io/visR/reference/add_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add annotations to a visR object — add_annotation","text":"gg Object class ggplot. label String, dataframe, data.table, tibble used annotate ggplot. base_family character. Base font family base_size numeric. Base font size pt xmin x coordinates giving horizontal location raster fit annotation. xmax x coordinates giving horizontal location raster fit annotation. ymin y coordinates giving vertical location raster fit annotation. ymax y coordinates giving vertical location raster fit annotation.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add annotations to a visR object — add_annotation","text":"Object class ggplot added annotation object class gtable.","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/add_annotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add annotations to a visR object — add_annotation","text":"","code":"## Estimate survival surv_object <- visR::estimate_KM(data = adtte, strata = \"TRTP\")  ## We want to annotate the survival KM plot with a simple string comment visR::visr(surv_object) %>%   visR::add_annotation(     label = \"My simple comment\",     base_family = \"sans\",     base_size = 15,     xmin = 110,     xmax = 180,     ymin = 0.80   )   ## Currently, care needs to be taken on the x-y values relative ## to the plot data area. Here we are plotting outside of the data area. visR::visr(surv_object) %>%   visR::add_annotation(     label = \"My simple comment\",     base_family = \"sans\",     base_size = 15,     xmin = 210,     xmax = 380,     ymin = 1.0   )    ## We may also want to annotate a KM plot with information ## from additional tests or estimates. This example we annotate ## with p-values contained in a tibble  ## we calculate p-values for \"Equality across strata\" lbl <- visR::get_pvalue(surv_object,   statlist = c(\"test\", \"pvalue\"),   type = \"All\" )  ## display p-values lbl #>   Equality across strata p-value #> 1               Log-Rank  <0.001 #> 2               Wilcoxon  <0.001 #> 3            Tarone-Ware  <0.001  ## Now annotate survival KM plot with the p-values visR::visr(surv_object) %>%   visR::add_annotation(     label = lbl,     base_family = \"sans\",     base_size = 9,     xmin = 100,     xmax = 180,     ymin = 0.80   )"},{"path":"https://openpharma.github.io/visR/reference/add_highlight.html","id":null,"dir":"Reference","previous_headings":"","what":"Highlight a specific strata — add_highlight","title":"Highlight a specific strata — add_highlight","text":"S3 method highlighting specific strata lowering opacity strata.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_highlight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Highlight a specific strata — add_highlight","text":"","code":"add_highlight(gg, ...)  # S3 method for ggsurvfit add_highlight(gg = NULL, strata = NULL, bg_alpha = 0.2, ...)"},{"path":"https://openpharma.github.io/visR/reference/add_highlight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Highlight a specific strata — add_highlight","text":"gg ggplot created visR ... arguments passed method strata String representing name value strata highlighted shown legend. bg_alpha numerical value 0 1 used decrease opacity strata chosen highlighted strata. strata's existing alpha values multiplied bg_alpha decrease opacity, highlighting target strata. works colour fill properties, example present applying visR::add_CI().","code":""},{"path":"https://openpharma.github.io/visR/reference/add_highlight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Highlight a specific strata — add_highlight","text":"input ggsurvfit object adjusted alpha values","code":""},{"path":"https://openpharma.github.io/visR/reference/add_highlight.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Highlight a specific strata — add_highlight","text":"","code":"adtte %>%   visR::estimate_KM(strata = \"SEX\") %>%   visR::visr() %>%   visR::add_CI(alpha = 0.4) %>%   visR::add_highlight(strata = \"M\", bg_alpha = 0.2)   strata <- c(\"Placebo\", \"Xanomeline Low Dose\")  adtte %>%   visR::estimate_KM(strata = \"TRTP\") %>%   visR::visr() %>%   visR::add_CI(alpha = 0.4) %>%   visR::add_highlight(strata = strata, bg_alpha = 0.2)"},{"path":"https://openpharma.github.io/visR/reference/add_quantiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Add quantile indicators to visR plot — add_quantiles","title":"Add quantile indicators to visR plot — add_quantiles","text":"Method add quantile lines plot.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_quantiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add quantile indicators to visR plot — add_quantiles","text":"","code":"add_quantiles(gg, ...)  # S3 method for ggsurvfit add_quantiles(   gg,   quantiles = 0.5,   linetype = \"dashed\",   linecolour = \"grey50\",   alpha = 1,   ... )"},{"path":"https://openpharma.github.io/visR/reference/add_quantiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add quantile indicators to visR plot — add_quantiles","text":"gg ggplot created visR ... arguments passed method modify geom_line quantiles vector quantiles displayed probability scale, default: 0.5 linetype string indicating linetype described aesthetics ggplot2 geom_line, default: dashed (also supports \"mixed\" -> horizontal lines solid, vertical ones dashed) linecolour string indicating linetype described aesthetics ggplot2 geom_line, default: grey, (also supports \"strata\" -> horizontal lines grey50, vertical ones colour respective strata) alpha numeric value 0 1 described aesthetics ggplot2 geom_line, default: 1","code":""},{"path":"https://openpharma.github.io/visR/reference/add_quantiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add quantile indicators to visR plot — add_quantiles","text":"Lines indicating quantiles overlayed visR ggplot","code":""},{"path":"https://openpharma.github.io/visR/reference/add_quantiles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add quantile indicators to visR plot — add_quantiles","text":"","code":"library(visR)  adtte %>%   estimate_KM(\"SEX\") %>%   visr() %>%   add_quantiles() #> Warning: no non-missing arguments to min; returning Inf   adtte %>%   estimate_KM(\"SEX\") %>%   visr() %>%   add_quantiles(quantiles = c(0.25, 0.50)) #> Warning: no non-missing arguments to min; returning Inf   adtte %>%   estimate_KM(\"SEX\") %>%   visr() %>%   add_quantiles(     quantiles = c(0.25, 0.50),     linetype = \"solid\",     linecolour = \"grey\"   ) #> Warning: no non-missing arguments to min; returning Inf   adtte %>%   estimate_KM(\"SEX\") %>%   visr() %>%   add_quantiles(     quantiles = c(0.25, 0.50),     linetype = \"mixed\",     linecolour = \"strata\"   ) #> Warning: no non-missing arguments to min; returning Inf"},{"path":"https://openpharma.github.io/visR/reference/add_risktable.html","id":null,"dir":"Reference","previous_headings":"","what":"Add risk tables to visR plots through an S3 method — add_risktable","title":"Add risk tables to visR plots through an S3 method — add_risktable","text":"S3 method adding risk tables visR plots. function following workflow: risktables calculated using get_risktable risktables placed underneath visR plots using plot_grid initial visR plot individual risktables stored attribute component final object allow post-modification individual plots desired","code":""},{"path":"https://openpharma.github.io/visR/reference/add_risktable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add risk tables to visR plots through an S3 method — add_risktable","text":"","code":"add_risktable(gg, ...)  # S3 method for ggsurvfit add_risktable(   gg,   times = NULL,   statlist = \"n.risk\",   label = NULL,   group = \"strata\",   collapse = FALSE,   rowgutter = 0.16,   ... )  # S3 method for ggtidycuminc add_risktable(   gg,   times = NULL,   statlist = \"n.risk\",   label = NULL,   group = \"strata\",   collapse = FALSE,   rowgutter = 0.16,   ... )"},{"path":"https://openpharma.github.io/visR/reference/add_risktable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add risk tables to visR plots through an S3 method — add_risktable","text":"gg visR plot class ggsurvfit ggtidycmprsk ... arguments passed method add_risktable times Numeric vector indicating times risk set, censored subjects, events calculated. statlist Character vector indicating summary data present. Current choices \"n.risk\" \"n.event\" \"n.censor\", \"cum.event\", \"cum.censor\". Default \"n.risk\". label Character vector labels statlist. Default matches \"n.risk\" \"risk\", \"n.event\" \"Events\", \"n.censor\" \"Censored\", \"cum.event\" \"Cum. Event\", \"cum.censor\" \"Cum. Censor\". group String indicating grouping variable risk tables. Current options : \"strata\": groups risk tables per stratum. label specifies label within risk table. strata levels used titles risk tables. default \"statlist\": groups risk tables per statlist. label specifies title risk table. strata levels used labeling within risk table. Default \"strata\". collapse Boolean, indicates whether present data overall. Default FALSE. rowgutter numeric relative value 0 1 indicates height used table versus height used plot, described cowplot::plot_grid(rel_heights=). default 0.16.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_risktable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add risk tables to visR plots through an S3 method — add_risktable","text":"Object class ggplot added risk table.","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/add_risktable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add risk tables to visR plots through an S3 method — add_risktable","text":"","code":"## Display 2 risk tables, 1 per statlist adtte %>%   visR::estimate_KM(strata = \"TRTP\") %>%   visR::visr() %>%   visR::add_risktable(     label = c(\"Subjects at Risk\", \"Censored\"),     statlist = c(\"n.risk\", \"n.censor\", \"n.event\"),     group = \"statlist\"   )   ## Display overall risk table at selected times adtte %>%   visR::estimate_KM(strata = \"TRTP\") %>%   visR::visr() %>%   visR::add_risktable(     label = c(\"Subjects at Risk\", \"Censored\"),     statlist = c(\"n.risk\", \"n.censor\"),     collapse = TRUE,     times = c(0, 20, 40, 60)   )   ## Add risk set as specified times adtte %>%   visR::estimate_KM(strata = \"TRTP\") %>%   visR::visr() %>%   visR::add_risktable(times = c(0, 20, 40, 100, 111, 200))"},{"path":"https://openpharma.github.io/visR/reference/adtte.html","id":null,"dir":"Reference","previous_headings":"","what":"adtte - CDISC ADaM compliant time to event data set — adtte","title":"adtte - CDISC ADaM compliant time to event data set — adtte","text":"ADTTE data copied 2013 CDISC Pilot","code":""},{"path":"https://openpharma.github.io/visR/reference/adtte.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"adtte - CDISC ADaM compliant time to event data set — adtte","text":"","code":"adtte"},{"path":"https://openpharma.github.io/visR/reference/adtte.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"adtte - CDISC ADaM compliant time to event data set — adtte","text":"data frame 254 rows 26 variables: STUDYID Study Identifier SITEID Study Site Identifier USUBJID Unique Subject Identifier AGE Age AGEGR1 Pooled Age Group 1 AGEGR1N Pooled Age Group 1 (N) RACE Race RACEN Race (N) SEX Sex TRTSDT Date First Exposure Treatment TRTEDT Date Last Exposure Treatment TRTDUR Duration treatment (days) TRTP Planned Treatment TRTA Actual Treatment TRTAN Actual Treatment (N) PARAM Parameter Description PARAMCD Parameter Code AVAL Analysis Value STARTDT Time Event Origin Date Subject ADT Analysis Date CNSR Censor EVNTDESC Event Censoring Description SRCDOM Source Domain SRCVAR Source Variable SRCSEQ Source Sequence Number SAFFL Safety Population Flag","code":""},{"path":"https://openpharma.github.io/visR/reference/adtte.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"adtte - CDISC ADaM compliant time to event data set — adtte","text":"CDISC SDTM/ADAM Pilot Project. https://github.com/phuse-org/phuse-scripts/tree/master/data","code":""},{"path":"https://openpharma.github.io/visR/reference/adtte.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"adtte - CDISC ADaM compliant time to event data set — adtte","text":"","code":"data(\"adtte\")"},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Align multiple ggplot graphs, taking into account the legend — align_plots","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"function aligns multiple ggplot graphs making width taking account legend width.","code":""},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"","code":"align_plots(pltlist)"},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"pltlist list plots","code":""},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"List ggplot equal width.","code":""},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"https://stackoverflow.com/questions/26159495","code":""},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"","code":"# \\donttest{  ## create 2 graphs p1 <- ggplot2::ggplot(adtte, ggplot2::aes(x = as.numeric(AGE), fill = \"Age\")) +   ggplot2::geom_histogram(bins = 15)  p2 <- ggplot2::ggplot(adtte, ggplot2::aes(x = as.numeric(AGE))) +   ggplot2::geom_histogram(bins = 15)  ## default alignment does not take into account legend size cowplot::plot_grid(   plotlist = list(p1, p2),   align = \"none\",   nrow = 2 )   ## align_plots() takes into account legend width cowplot::plot_grid(   plotlist = visR::align_plots(pltlist = list(p1, p2)),   align = \"none\",   nrow = 2 )  # }"},{"path":"https://openpharma.github.io/visR/reference/apply_attrition.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","title":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","text":"Apply list inclusion/exclusion criteria patient-level dataframe","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_attrition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","text":"","code":"apply_attrition(data, criteria_conditions)"},{"path":"https://openpharma.github.io/visR/reference/apply_attrition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","text":"data data.frame. Data set filtered criteria_conditions character dplyr-filter compatible conditions filtering criteria. conditions applied filter input data set obtain final analysis data set","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_attrition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","text":"Filtered data frame","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_attrition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","text":"","code":"adtte_filtered <- visR::apply_attrition(adtte,   criteria_conditions = c(     \"TRTP=='Placebo'\", \"AGE>=75\",     \"RACE=='WHITE'\", \"SITEID==709\"   ) )"},{"path":"https://openpharma.github.io/visR/reference/apply_theme.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a theme to a ggplot object. — apply_theme","title":"Applies a theme to a ggplot object. — apply_theme","text":"Takes styling options defined visR::define_theme applies plot.","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_theme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a theme to a ggplot object. — apply_theme","text":"","code":"apply_theme(gg, visR_theme_dict = NULL)"},{"path":"https://openpharma.github.io/visR/reference/apply_theme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a theme to a ggplot object. — apply_theme","text":"gg object class ggplot visR_theme_dict nested list containing possible font options","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_theme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies a theme to a ggplot object. — apply_theme","text":"object class ggplot","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_theme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a theme to a ggplot object. — apply_theme","text":"","code":"library(visR)  theme <- visR::define_theme(   strata = list(     \"SEX\" = list(       \"F\" = \"red\",       \"M\" = \"blue\"     ),     \"TRTA\" = list(       \"Placebo\" = \"cyan\",       \"Xanomeline High Dose\" = \"purple\",       \"Xanomeline Low Dose\" = \"brown\"     )   ),   fontsizes = list(     \"axis\" = 12,     \"ticks\" = 10,     \"legend_title\" = 10,     \"legend_text\" = 8   ),   fontfamily = \"Helvetica\",   grid = FALSE,   bg = \"transparent\",   legend_position = \"top\" )   gg <- adtte %>%   visR::estimate_KM(strata = \"SEX\") %>%   visR::visr() %>%   visR::apply_theme(theme) gg"},{"path":"https://openpharma.github.io/visR/reference/brca_cohort.html","id":null,"dir":"Reference","previous_headings":"","what":"Cancer survival data — brca_cohort","title":"Cancer survival data — brca_cohort","text":"Creation script data-raw","code":""},{"path":"https://openpharma.github.io/visR/reference/brca_cohort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cancer survival data — brca_cohort","text":"","code":"brca_cohort"},{"path":"https://openpharma.github.io/visR/reference/brca_cohort.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cancer survival data — brca_cohort","text":"object class data.frame 1098 rows 10 columns.","code":""},{"path":"https://openpharma.github.io/visR/reference/define_theme.html","id":null,"dir":"Reference","previous_headings":"","what":"Provides a simple wrapper for themes — define_theme","title":"Provides a simple wrapper for themes — define_theme","text":"function collects several lists present. absent, reasonable defaults used. strata defined theme, default grey50 presented legend.","code":""},{"path":"https://openpharma.github.io/visR/reference/define_theme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provides a simple wrapper for themes — define_theme","text":"","code":"define_theme(   strata = NULL,   fontsizes = NULL,   fontfamily = \"Helvetica\",   grid = FALSE,   bg = \"transparent\",   legend_position = NULL )"},{"path":"https://openpharma.github.io/visR/reference/define_theme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provides a simple wrapper for themes — define_theme","text":"strata named list containing different strata name:colour value pairs fontsizes named list containing font sizes different options fontfamily string name supported font grid boolean specifies whether major minor grid drawn. drawing major minor gridlines can manipulated separately using boolean indicator named list elements major minor. bg string defining colour background plot legend_position string defining legend position. Valid options NULL, 'top' 'bottom' 'right' 'left'","code":""},{"path":"https://openpharma.github.io/visR/reference/define_theme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Provides a simple wrapper for themes — define_theme","text":"Nested list styling preferences ggplot object","code":""},{"path":"https://openpharma.github.io/visR/reference/define_theme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provides a simple wrapper for themes — define_theme","text":"","code":"theme <- visR::define_theme(   strata = list(\"SEX\" = list(     \"F\" = \"red\",     \"M\" = \"blue\"   )),   fontsizes = list(     \"axis\" = 12,     \"ticks\" = 10,     \"legend_title\" = 10,     \"legend_text\" = 8   ),   fontfamily = \"Helvetica\",   grid = list(     \"major\" = FALSE,     \"minor\" = FALSE   ),   bg = \"transparent\",   legend_position = \"top\" )"},{"path":"https://openpharma.github.io/visR/reference/dot-convert_alpha.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts an alpha value between its numeric and its hex-encoded form. — .convert_alpha","title":"Converts an alpha value between its numeric and its hex-encoded form. — .convert_alpha","text":"function accepts numeric (NULL/NA) two-character hex encoded alpha representation returns respective representation.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-convert_alpha.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts an alpha value between its numeric and its hex-encoded form. — .convert_alpha","text":"","code":".convert_alpha(numeric_alpha = NULL, hex_alpha = NULL)"},{"path":"https://openpharma.github.io/visR/reference/dot-convert_alpha.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts an alpha value between its numeric and its hex-encoded form. — .convert_alpha","text":"numeric_alpha numerical value 0 1. hex_alpha two-letter character string.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-convert_alpha.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts an alpha value between its numeric and its hex-encoded form. — .convert_alpha","text":"numeric_alpha specified, two-letter representation returned. hex_alpha specified, numeric representation returned.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_alpha_from_hex_colour.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the numerical alpha representation of #RRGGBBAA colour — .get_alpha_from_hex_colour","title":"Extract the numerical alpha representation of #RRGGBBAA colour — .get_alpha_from_hex_colour","text":"RGB colours can encoded hexadecimal values, example internally used ggplot2. , numerical RGB values mapped 0-255 value range two-character hex-values. yields string form '#RRGGBB'. Additionally, fourth optional block can present encoding alpha transparency colour. extends string '#RRGGBBAA'. function takes string input hex_colour, extracts 'AA' part returns numerical representation .","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_alpha_from_hex_colour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the numerical alpha representation of #RRGGBBAA colour — .get_alpha_from_hex_colour","text":"","code":".get_alpha_from_hex_colour(hex_colour = NULL)"},{"path":"https://openpharma.github.io/visR/reference/dot-get_alpha_from_hex_colour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the numerical alpha representation of #RRGGBBAA colour — .get_alpha_from_hex_colour","text":"hex_colour string format '#RRGGBBAA'","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_alpha_from_hex_colour.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the numerical alpha representation of #RRGGBBAA colour — .get_alpha_from_hex_colour","text":"numeric representation colors' alpha value, rounded 2 digits.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_strata.html","id":null,"dir":"Reference","previous_headings":"","what":"Get strata level combinations — .get_strata","title":"Get strata level combinations — .get_strata","text":"Extracts strata level combinations survival objects without specified strata.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_strata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get strata level combinations — .get_strata","text":"","code":".get_strata(strata)"},{"path":"https://openpharma.github.io/visR/reference/dot-get_strata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get strata level combinations — .get_strata","text":"strata strata survival object","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_strata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get strata level combinations — .get_strata","text":"strata level combinations survival objects without specified strata.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-replace_hex_alpha.html","id":null,"dir":"Reference","previous_headings":"","what":"Replaces the AA part of a #RRGGBBAA hex-colour. — .replace_hex_alpha","title":"Replaces the AA part of a #RRGGBBAA hex-colour. — .replace_hex_alpha","text":"RGB colours can encoded hexadecimal values, example internally used ggplot2. , numerical RGB values mapped 0-255 value range two-character hex-values. yields string form '#RRGGBB'. Additionally, fourth optional block can present encoding alpha transparency colour. extends string '#RRGGBBAA'. function takes '#RRGGBBAA' string input colour two-character hex-representation alpha value input new_alpha, replaces 'AA' part colour new_alpha returns new colour.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-replace_hex_alpha.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replaces the AA part of a #RRGGBBAA hex-colour. — .replace_hex_alpha","text":"","code":".replace_hex_alpha(colour, new_alpha)"},{"path":"https://openpharma.github.io/visR/reference/dot-replace_hex_alpha.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replaces the AA part of a #RRGGBBAA hex-colour. — .replace_hex_alpha","text":"colour character string format #RRGGBBAA. new_alpha two-character string new alpha value.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-replace_hex_alpha.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replaces the AA part of a #RRGGBBAA hex-colour. — .replace_hex_alpha","text":"hex-encoded RGBA colour.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"function wrapper around survival::survfit.formula() perform Kaplan-Meier analysis, assuming right-censored data. result object class survfit can used downstream functions methods rely survfit class. function can leverage conventions controlled vocabulary CDISC ADaM ADTTE data model, also works standard, non-CDISC datasets formula argument.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"","code":"estimate_KM(   data = NULL,   strata = NULL,   CNSR = \"CNSR\",   AVAL = \"AVAL\",   formula = NULL,   ... )"},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"data data frame. dataset expected one record per subject per analysis parameter. Rows missing observations included analysis removed. AVAL, CNSR, strata arguments used construct formula passed survival::survfit(formula=Surv(AVAL, 1-CNSR)~strata). arguments' default values follow naming conventions CDISC. AVAL Analysis value Time--Event analysis. Default \"AVAL\", per CDISC ADaM guiding principles. CNSR Censor Time--Event analysis. Default \"CNSR\", per CDISC ADaM guiding principles. expected CNSR = 1 censoring CNSR = 0 event interest. strata Character vector, representing strata Time--Event analysis. NULL, overall analysis performed. Default NULL. formula formula Surv() RHS stratifying variables LHS. Use ~1 LHS unstratified estimates. argument passed survival::survfit(formula=). argument used, arguments AVAL, CNSR, strata ignored. ... additional arguments passed ellipsis call survival::survfit.formula(...). Use ?survival::survfit.formula ?survival::survfitCI information.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"survfit object ready downstream processing estimation visualization functions methods.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"estimation-of-survfit-object","dir":"Reference","previous_headings":"","what":"Estimation of 'survfit' object","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"estimate_KM() function utilizes defaults survival::survfit(): Kaplan Meier estimate estimated directly (stype = 1). cumulative hazard estimated using Nelson-Aalen estimator (ctype = 1): H.tilde = cumsum(x$n.event/x$n.risk). MLE (H.hat(t) = -log(S.hat(t))) requested. two-sided pointwise 0.95 confidence interval estimated using log transformation (conf.type = \"log\"). strata present, returned survfit object supplemented named list stratum associated label. support full traceability, data set name captured named list call captured within corresponding environment.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"param-paramcd-and-cdisc","dir":"Reference","previous_headings":"","what":"PARAM/PARAMCD and CDISC","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"data frame includes columns PARAM/PARAMCD (part CDISC format), function expects data filtered parameter interest.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"https://github.com/therneau/survival","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"","code":"## No stratification visR::estimate_KM(data = adtte) #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     1, data = data) #>  #>        n events median 0.95LCL 0.95UCL #> [1,] 254    152     51      44      70  ## Stratified Kaplan-Meier analysis by `TRTP` visR::estimate_KM(data = adtte, strata = \"TRTP\") #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     TRTP, data = data) #>  #>                            n events median 0.95LCL 0.95UCL #> TRTP=Placebo              86     29     NA      NA      NA #> TRTP=Xanomeline High Dose 84     61     36      25      47 #> TRTP=Xanomeline Low Dose  84     62     33      28      51  ## Stratified Kaplan-Meier analysis by `TRTP` and `SEX` visR::estimate_KM(data = adtte, strata = c(\"TRTP\", \"SEX\")) #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     TRTP + SEX, data = data) #>  #>                                   n events median 0.95LCL 0.95UCL #> TRTP=Placebo, SEX=F              53     19     NA      90      NA #> TRTP=Placebo, SEX=M              33     10     NA      NA      NA #> TRTP=Xanomeline High Dose, SEX=F 40     27     46      30      70 #> TRTP=Xanomeline High Dose, SEX=M 44     34     25      20      46 #> TRTP=Xanomeline Low Dose, SEX=F  50     34     43      29     100 #> TRTP=Xanomeline Low Dose, SEX=M  34     28     27      21      51  ## Stratification with one level visR::estimate_KM(data = adtte, strata = \"PARAMCD\") #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     PARAMCD, data = data) #>  #>        n events median 0.95LCL 0.95UCL #> [1,] 254    152     51      44      70  ## Analysis on subset of adtte visR::estimate_KM(data = adtte[adtte$SEX == \"F\", ]) #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     1, data = data) #>  #>        n events median 0.95LCL 0.95UCL #> [1,] 143     80     64      47      96  ## Modify the default analysis by using the ellipsis visR::estimate_KM(   data = adtte, strata = NULL,   type = \"kaplan-meier\", conf.int = FALSE, timefix = TRUE ) #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     1, data = data, timefix = TRUE, type = \"kaplan-meier\", conf.int = FALSE) #>  #>        n events median #> [1,] 254    152     51  ## Example working with non CDISC data head(survival::veteran[c(\"time\", \"status\", \"trt\")]) #>   time status trt #> 1   72      1   1 #> 2  411      1   1 #> 3  228      1   1 #> 4  126      1   1 #> 5  118      1   1 #> 6   10      1   1  # Using non-CDSIC data visR::estimate_KM(data = survival::veteran, formula = Surv(time, status) ~ trt) #> Call: ~survival::survfit(formula = Surv(time, status) ~ trt, data = data) #>  #>        n events median 0.95LCL 0.95UCL #> trt=1 69     64  103.0      59     132 #> trt=2 68     64   52.5      44      95"},{"path":"https://openpharma.github.io/visR/reference/estimate_cuminc.html","id":null,"dir":"Reference","previous_headings":"","what":"Competing Events Cumulative Incidence — estimate_cuminc","title":"Competing Events Cumulative Incidence — estimate_cuminc","text":"Function creates cumulative incidence object using tidycmprsk::cuminc() function.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_cuminc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Competing Events Cumulative Incidence — estimate_cuminc","text":"","code":"estimate_cuminc(   data = NULL,   strata = NULL,   CNSR = \"CNSR\",   AVAL = \"AVAL\",   conf.int = 0.95,   ... )"},{"path":"https://openpharma.github.io/visR/reference/estimate_cuminc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Competing Events Cumulative Incidence — estimate_cuminc","text":"data data frame. dataset expected one record per subject per analysis parameter. Rows missing observations included analysis removed. AVAL, CNSR, strata arguments used construct formula passed tidycmprsk::cuminc(formula=). AVAL Analysis value Time--Event analysis. Default \"AVAL\", per CDISC ADaM guiding principles. CNSR Column name indicating outcome censoring statuses. Column must factor first level indicates censoring, next level outcome interest, remaining levels competing events. Default \"CNSR\" strata Character vector, representing strata Time--Event analysis. NULL, overall analysis performed. Default NULL. conf.int Confidence internal level. Default 0.95. Parameter passed tidycmprsk::cuminc(conf.level=) ... Additional argument passed tidycmprsk::cuminc()","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_cuminc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Competing Events Cumulative Incidence — estimate_cuminc","text":"cumulative incidence object explained https://mskcc-epi-bio.github.io/tidycmprsk/reference/cuminc.html","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_cuminc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Competing Events Cumulative Incidence — estimate_cuminc","text":"","code":"cuminc <-   visR::estimate_cuminc(     data = tidycmprsk::trial,     strata = \"trt\",     CNSR = \"death_cr\",     AVAL = \"ttdeath\"   ) cuminc #>  #> ── cuminc() ──────────────────────────────────────────────────────────────────── #>  #> • Failure type \"death from cancer\" #> strata   time   n.risk   estimate   std.error   95% CI           #> Drug A   5.00   97       0.000      0.000       NA, NA           #> Drug A   10.0   94       0.020      0.014       0.004, 0.065     #> Drug A   15.0   83       0.071      0.026       0.031, 0.134     #> Drug A   20.0   61       0.173      0.039       0.106, 0.255     #> Drug B   5.00   102      0.000      0.000       NA, NA           #> Drug B   10.0   95       0.039      0.019       0.013, 0.090     #> Drug B   15.0   75       0.167      0.037       0.102, 0.246     #> Drug B   20.0   55       0.255      0.043       0.175, 0.343     #>  #> • Failure type \"death other causes\" #> strata   time   n.risk   estimate   std.error   95% CI           #> Drug A   5.00   97       0.010      0.010       0.001, 0.050     #> Drug A   10.0   94       0.020      0.014       0.004, 0.065     #> Drug A   15.0   83       0.082      0.028       0.038, 0.147     #> Drug A   20.0   61       0.204      0.041       0.131, 0.289     #> Drug B   5.00   102      0.000      0.000       NA, NA           #> Drug B   10.0   95       0.029      0.017       0.008, 0.077     #> Drug B   15.0   75       0.098      0.030       0.050, 0.165     #> Drug B   20.0   55       0.206      0.040       0.133, 0.289     #>  #> • Tests #> outcome              statistic   df     p.value     #> death from cancer    1.99        1.00   0.16        #> death other causes   0.089       1.00   0.77         cuminc %>%   visR::visr() %>%   visR::add_CI() %>%   visR::add_risktable(statlist = c(\"n.risk\", \"cum.event\"))"},{"path":"https://openpharma.github.io/visR/reference/get_COX_HR.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","title":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","text":"S3 method extracting information regarding Hazard Ratios. function allows survival object's formula updated. default method available moment.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_COX_HR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","text":"","code":"get_COX_HR(x, ...)  # S3 method for survfit get_COX_HR(x, update_formula = NULL, ...)"},{"path":"https://openpharma.github.io/visR/reference/get_COX_HR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","text":"x object class survfit ... arguments passed method survival::coxph update_formula Template specifies update formula survfit object update.formula","code":""},{"path":"https://openpharma.github.io/visR/reference/get_COX_HR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","text":"tidied object class coxph containing Hazard Ratios","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/get_COX_HR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","text":"","code":"## treatment effect survfit_object_trt <- visR::estimate_KM(data = adtte, strata = c(\"TRTP\")) visR::get_COX_HR(survfit_object_trt) #> tidyme S3 default method (broom::tidy) used. #>                       term estimate std.error statistic      p.value #> 1 TRTPXanomeline High Dose 1.614618 0.2332605  6.921953 4.454580e-12 #> 2  TRTPXanomeline Low Dose 1.422555 0.2295098  6.198232 5.710099e-10  ## treatment and gender effect survfit_object_trt_sex <- visR::estimate_KM(data = adtte, strata = c(\"TRTP\", \"SEX\")) visR::get_COX_HR(survfit_object_trt_sex) #> tidyme S3 default method (broom::tidy) used. #>                       term  estimate std.error statistic      p.value #> 1 TRTPXanomeline High Dose 1.6159986 0.2339757  6.906695 4.960732e-12 #> 2  TRTPXanomeline Low Dose 1.4755033 0.2317331  6.367252 1.924446e-10 #> 3                     SEXM 0.3745729 0.1651854  2.267591 2.335412e-02  ## update formula of KM estimates by treatment to include \"SEX\" for HR estimation visR::get_COX_HR(survfit_object_trt, update_formula = \". ~ . + SEX\") #> tidyme S3 default method (broom::tidy) used. #>                       term  estimate std.error statistic      p.value #> 1 TRTPXanomeline High Dose 1.6159986 0.2339757  6.906695 4.960732e-12 #> 2  TRTPXanomeline Low Dose 1.4755033 0.2317331  6.367252 1.924446e-10 #> 3                     SEXM 0.3745729 0.1651854  2.267591 2.335412e-02  ## update formula of KM estimates by treatment to include \"AGE\" for ## HR estimation with ties considered via the efron method visR::get_COX_HR(survfit_object_trt,   update_formula = \". ~ . + survival::strata(AGE)\", ties = \"efron\" ) #> Warning: Loglik converged before variable  3,7,18,36,37 ; coefficient may be infinite.  #> tidyme S3 default method (broom::tidy) used. #>                           term    estimate    std.error     statistic #> 1     TRTPXanomeline High Dose   1.5676387 2.555278e-01  6.1349054151 #> 2      TRTPXanomeline Low Dose   1.3216405 2.530283e-01  5.2232906436 #> 3  survival::strata(AGE)AGE=52 -18.1640265 5.298844e+03 -0.0034279227 #> 4  survival::strata(AGE)AGE=54  -0.2090228 1.415797e+00 -0.1476361992 #> 5  survival::strata(AGE)AGE=56  -1.6206873 1.097690e+00 -1.4764526121 #> 6  survival::strata(AGE)AGE=57  -1.1398148 1.166518e+00 -0.9771082854 #> 7  survival::strata(AGE)AGE=59 -18.1578569 3.767502e+03 -0.0048196009 #> 8  survival::strata(AGE)AGE=60  -1.3242727 1.241054e+00 -1.0670547450 #> 9  survival::strata(AGE)AGE=61  -1.4515246 1.133242e+00 -1.2808598926 #> 10 survival::strata(AGE)AGE=62  -1.4077265 1.428274e+00 -0.9856138035 #> 11 survival::strata(AGE)AGE=63  -1.7199741 1.245995e+00 -1.3804019425 #> 12 survival::strata(AGE)AGE=64  -1.4654615 1.256398e+00 -1.1663993068 #> 13 survival::strata(AGE)AGE=65  -1.8714042 1.236610e+00 -1.5133340637 #> 14 survival::strata(AGE)AGE=66  -0.7392248 1.443026e+00 -0.5122740113 #> 15 survival::strata(AGE)AGE=67  -1.7065320 1.132933e+00 -1.5062959259 #> 16 survival::strata(AGE)AGE=68  -1.5389556 1.104752e+00 -1.3930331338 #> 17 survival::strata(AGE)AGE=69  -1.1214615 1.136235e+00 -0.9869979981 #> 18 survival::strata(AGE)AGE=70 -18.2495027 2.607234e+03 -0.0069995634 #> 19 survival::strata(AGE)AGE=71  -1.3835479 1.085266e+00 -1.2748469333 #> 20 survival::strata(AGE)AGE=72  -1.5861528 1.109620e+00 -1.4294562988 #> 21 survival::strata(AGE)AGE=73  -1.3311583 1.078529e+00 -1.2342349648 #> 22 survival::strata(AGE)AGE=74  -1.8664225 1.086626e+00 -1.7176312590 #> 23 survival::strata(AGE)AGE=75  -1.0701874 1.094268e+00 -0.9779939016 #> 24 survival::strata(AGE)AGE=76  -1.5673346 1.094736e+00 -1.4317009758 #> 25 survival::strata(AGE)AGE=77  -1.6854602 1.064611e+00 -1.5831705057 #> 26 survival::strata(AGE)AGE=78  -1.3770729 1.070742e+00 -1.2860927604 #> 27 survival::strata(AGE)AGE=79  -1.4209295 1.057330e+00 -1.3438841443 #> 28 survival::strata(AGE)AGE=80  -1.0569092 1.073982e+00 -0.9841035589 #> 29 survival::strata(AGE)AGE=81  -2.4638433 1.083968e+00 -2.2729862787 #> 30 survival::strata(AGE)AGE=82  -1.9384962 1.095070e+00 -1.7702034739 #> 31 survival::strata(AGE)AGE=83  -0.9201888 1.089830e+00 -0.8443416072 #> 32 survival::strata(AGE)AGE=84  -1.4853330 1.059185e+00 -1.4023361544 #> 33 survival::strata(AGE)AGE=85  -1.5477944 1.140473e+00 -1.3571508894 #> 34 survival::strata(AGE)AGE=86  -2.2322079 1.172337e+00 -1.9040670529 #> 35 survival::strata(AGE)AGE=87  -2.6816908 1.423263e+00 -1.8841844010 #> 36 survival::strata(AGE)AGE=88 -18.7682740 2.704813e+03 -0.0069388434 #> 37 survival::strata(AGE)AGE=89 -18.1983764 1.889752e+04 -0.0009630034 #>         p.value #> 1  8.520988e-10 #> 2  1.757712e-07 #> 3  9.972649e-01 #> 4  8.826299e-01 #> 5  1.398224e-01 #> 6  3.285155e-01 #> 7  9.961545e-01 #> 8  2.859471e-01 #> 9  2.002429e-01 #> 10 3.243227e-01 #> 11 1.674629e-01 #> 12 2.434530e-01 #> 13 1.301948e-01 #> 14 6.084593e-01 #> 15 1.319912e-01 #> 16 1.636098e-01 #> 17 3.236436e-01 #> 18 9.944152e-01 #> 19 2.023634e-01 #> 20 1.528731e-01 #> 21 2.171154e-01 #> 22 8.586389e-02 #> 23 3.280773e-01 #> 24 1.522294e-01 #> 25 1.133826e-01 #> 26 1.984107e-01 #> 27 1.789859e-01 #> 28 3.250646e-01 #> 29 2.302701e-02 #> 30 7.669325e-02 #> 31 3.984785e-01 #> 32 1.608149e-01 #> 33 1.747333e-01 #> 34 5.690145e-02 #> 35 5.954004e-02 #> 36 9.944636e-01 #> 37 9.992316e-01"},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate cohort attrition table — get_attrition","title":"Generate cohort attrition table — get_attrition","text":"experimental function may developed time. function calculates subjects counts excluded included step cohort selection process.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate cohort attrition table — get_attrition","text":"","code":"get_attrition(data, criteria_descriptions, criteria_conditions, subject_column_name)"},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate cohort attrition table — get_attrition","text":"data Dataframe. used input data count subjects meets criteria interest criteria_descriptions character contains descriptions inclusion/exclusion criteria. element vector corresponds description criterion. criteria_conditions character contains corresponding conditions criteria. conditions used table compute counts subjects. subject_column_name character column name table contains subject id.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate cohort attrition table — get_attrition","text":"counts percentages remaining excluded subjects step cohort selection table format.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate cohort attrition table — get_attrition","text":"criteria_descriptions criteria_conditions need length","code":""},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate cohort attrition table — get_attrition","text":"","code":"visR::get_attrition(adtte,   criteria_descriptions =     c(       \"1. Placebo Group\", \"2. Be 75 years of age or older.\",       \"3. White\", \"4. Site 709\"     ),   criteria_conditions = c(     \"TRTP=='Placebo'\", \"AGE>=75\",     \"RACE=='WHITE'\", \"SITEID==709\"   ),   subject_column_name = \"USUBJID\" ) #> # A tibble: 5 × 6 #>   Criteria       Condition `Remaining N` `Remaining %` `Excluded N` `Excluded %` #>   <chr>          <chr>             <int>         <dbl>        <int>        <dbl> #> 1 Total cohort … none                254        100               0         0    #> 2 1. Placebo Gr… TRTP=='P…            86         33.9           168        66.1  #> 3 2. Be 75 year… AGE>=75              48         18.9            38        15.0  #> 4 3. White       RACE=='W…            45         17.7             3         1.18 #> 5 4. Site 709    SITEID==…             3          1.18           42        16.5"},{"path":"https://openpharma.github.io/visR/reference/get_pvalue.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","title":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","text":"Wrapper around survival::survdiff tests null hypothesis equality across strata.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_pvalue.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","text":"","code":"get_pvalue(   survfit_object,   ptype = \"All\",   rho = NULL,   statlist = c(\"test\", \"Chisq\", \"df\", \"pvalue\"),   ... )"},{"path":"https://openpharma.github.io/visR/reference/get_pvalue.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","text":"survfit_object object class survfit ptype Character vector containing type p-value desired. Current options \"Log-Rank\" \"Wilcoxon\" \"Tarone-Ware\" \"Custom\" \"\". \"Custom\" allows user specify weights Kaplan-Meier estimates using argument rho. default \"\" displaying types possible. rho specified context \"\", also custom p-value displayed. rho scalar parameter controls type test. statlist Character vector containing desired information displayed. order arguments determines order displayed final result. Default test name (\"test\"), Chi-squared test statistic (\"Chisq\"), degrees freedom (\"df\") p-value (\"pvalue\"). ... arguments passed method","code":""},{"path":"https://openpharma.github.io/visR/reference/get_pvalue.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","text":"data frame summary measures Test Equality Across Strata","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/get_pvalue.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","text":"","code":"## general examples survfit_object <- visR::estimate_KM(data = adtte, strata = \"TRTP\") visR::get_pvalue(survfit_object) #>   Equality across strata  Chisq df p-value #> 1               Log-Rank 60.270  2  <0.001 #> 2               Wilcoxon 48.023  2  <0.001 #> 3            Tarone-Ware 41.850  2  <0.001 visR::get_pvalue(survfit_object, ptype = \"All\") #>   Equality across strata  Chisq df p-value #> 1               Log-Rank 60.270  2  <0.001 #> 2               Wilcoxon 48.023  2  <0.001 #> 3            Tarone-Ware 41.850  2  <0.001  ## examples to obtain specific tests visR::get_pvalue(survfit_object, ptype = \"Log-Rank\") #>   Equality across strata  Chisq df p-value #> 1               Log-Rank 60.270  2  <0.001 visR::get_pvalue(survfit_object, ptype = \"Wilcoxon\") #>   Equality across strata  Chisq df p-value #> 1               Wilcoxon 48.023  2  <0.001 visR::get_pvalue(survfit_object, ptype = \"Tarone-Ware\") #>   Equality across strata  Chisq df p-value #> 1            Tarone-Ware 41.850  2  <0.001  ## Custom example - obtain Harrington and Fleming test visR::get_pvalue(survfit_object, ptype = \"Custom\", rho = 1) #>                  Equality across strata  Chisq df p-value #> 1 Harrington and Fleming test (rho = 1) 48.023  2  <0.001  ## Get specific information and statistics visR::get_pvalue(survfit_object, ptype = \"Log-Rank\", statlist = c(\"test\", \"Chisq\", \"df\", \"pvalue\")) #>   Equality across strata  Chisq df p-value #> 1               Log-Rank 60.270  2  <0.001 visR::get_pvalue(survfit_object, ptype = \"Wilcoxon\", statlist = c(\"pvalue\")) #>   p-value #> 1  <0.001"},{"path":"https://openpharma.github.io/visR/reference/get_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper around quantile methods — get_quantile","title":"Wrapper around quantile methods — get_quantile","text":"S3 method extracting quantiles. default method available moment.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper around quantile methods — get_quantile","text":"","code":"get_quantile(x, ...)  # S3 method for survfit get_quantile(   x,   ...,   probs = c(0.25, 0.5, 0.75),   conf.int = TRUE,   tolerance = sqrt(.Machine$double.eps) )"},{"path":"https://openpharma.github.io/visR/reference/get_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper around quantile methods — get_quantile","text":"x object class survfit ... arguments passed method probs probabilities Default = c(0.25,0.50,0.75) conf.int lower upper confidence limits returned? tolerance tolerance checking survival curve exactly     equals one quantiles","code":""},{"path":"https://openpharma.github.io/visR/reference/get_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper around quantile methods — get_quantile","text":"data frame quantiles object","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/get_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrapper around quantile methods — get_quantile","text":"","code":"## Kaplan-Meier estimates survfit_object <- visR::estimate_KM(data = adtte, strata = c(\"TRTP\"))  ## visR quantiles visR::get_quantile(survfit_object) #>                      strata quantity  25 50  75 #> 4              TRTP=Placebo    lower  35 NA  NA #> 1              TRTP=Placebo quantile  70 NA  NA #> 7              TRTP=Placebo    upper 177 NA  NA #> 5 TRTP=Xanomeline High Dose    lower   5 25  50 #> 2 TRTP=Xanomeline High Dose quantile  14 36  58 #> 8 TRTP=Xanomeline High Dose    upper  22 47  94 #> 6  TRTP=Xanomeline Low Dose    lower  15 28  57 #> 3  TRTP=Xanomeline Low Dose quantile  19 33  80 #> 9  TRTP=Xanomeline Low Dose    upper  27 51 126  ## survival quantiles quantile(survfit_object) #> $quantile #>                           25 50 75 #> TRTP=Placebo              70 NA NA #> TRTP=Xanomeline High Dose 14 36 58 #> TRTP=Xanomeline Low Dose  19 33 80 #>  #> $lower #>                           25 50 75 #> TRTP=Placebo              35 NA NA #> TRTP=Xanomeline High Dose  5 25 50 #> TRTP=Xanomeline Low Dose  15 28 57 #>  #> $upper #>                            25 50  75 #> TRTP=Placebo              177 NA  NA #> TRTP=Xanomeline High Dose  22 47  94 #> TRTP=Xanomeline Low Dose   27 51 126 #>"},{"path":"https://openpharma.github.io/visR/reference/get_risktable.html","id":null,"dir":"Reference","previous_headings":"","what":"Obtain risk tables for tables and plots — get_risktable","title":"Obtain risk tables for tables and plots — get_risktable","text":"Create risk table object using S3 method. Currently, default method defined.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_risktable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Obtain risk tables for tables and plots — get_risktable","text":"","code":"get_risktable(x, ...)  # S3 method for survfit get_risktable(   x,   times = NULL,   statlist = \"n.risk\",   label = NULL,   group = c(\"strata\", \"statlist\"),   collapse = FALSE,   ... )  # S3 method for tidycuminc get_risktable(   x,   times = pretty(x$tidy$time, 10),   statlist = \"n.risk\",   label = NULL,   group = c(\"strata\", \"statlist\"),   collapse = FALSE,   ... )"},{"path":"https://openpharma.github.io/visR/reference/get_risktable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Obtain risk tables for tables and plots — get_risktable","text":"x object class survfit tidycuminc ... arguments passed method times Numeric vector indicating times risk set, censored subjects, events calculated. statlist Character vector indicating summary data present. Current choices \"n.risk\" \"n.event\" \"n.censor\", \"cum.event\", \"cum.censor\". Default \"n.risk\". label Character vector labels statlist. Default matches \"n.risk\" \"risk\", \"n.event\" \"Events\", \"n.censor\" \"Censored\", \"cum.event\" \"Cum. Event\", \"cum.censor\" \"Cum. Censor\". group String indicating grouping variable risk tables. Current options : \"strata\": groups risk tables per stratum. label specifies label within risk table. strata levels used titles risk tables. default \"statlist\": groups risk tables per statlist. label specifies title risk table. strata levels used labeling within risk table. Default \"strata\". collapse Boolean, indicates whether present data overall. Default FALSE.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_risktable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Obtain risk tables for tables and plots — get_risktable","text":"return list attributes form risk table .e. number patients risk per strata","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/get_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","title":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","text":"S3 method extracting descriptive statistics across strata. default method available moment.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","text":"","code":"get_summary(x, ...)  # S3 method for survfit get_summary(   x,   statlist = c(\"strata\", \"records\", \"events\", \"median\", \"LCL\", \"UCL\", \"CI\"),   ... )"},{"path":"https://openpharma.github.io/visR/reference/get_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","text":"x object class survfit ... arguments passed method statlist Character vector containing desired information displayed. order arguments determines order displayed final result. Default strata (\"strata\"), number subjects (\"records\"), number events (\"events\"), median survival time (\"median\"), Confidence Interval (\"CI\"), Lower Confidence Limit (\"UCL\") Upper Confidence Limit (\"UCL\").","code":""},{"path":"https://openpharma.github.io/visR/reference/get_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","text":"list summary statistics survfit object data frame summary measures survfit object","code":""},{"path":"https://openpharma.github.io/visR/reference/get_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","text":"","code":"survfit_object <- survival::survfit(data = adtte, survival::Surv(AVAL, 1 - CNSR) ~ TRTP) get_summary(survfit_object) #>                                              strata No. of subjects #> TRTP=Placebo                           TRTP=Placebo              86 #> TRTP=Xanomeline High Dose TRTP=Xanomeline High Dose              84 #> TRTP=Xanomeline Low Dose   TRTP=Xanomeline Low Dose              84 #>                           No. of events Median(surv.time) 0.95LCL 0.95UCL #> TRTP=Placebo                         29                NA      NA      NA #> TRTP=Xanomeline High Dose            61                36      25      47 #> TRTP=Xanomeline Low Dose             62                33      28      51 #>                            0.95CI #> TRTP=Placebo              (NA;NA) #> TRTP=Xanomeline High Dose (25;47) #> TRTP=Xanomeline Low Dose  (28;51)"},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate summary statistics — get_tableone","title":"Calculate summary statistics — get_tableone","text":"S3 method creating table summary statistics. summary statistics can used presentation tables table one baseline demography tables. summary statistics estimated conditional variable type: continuous, binary, categorical, etc. default following summary stats calculated: Numeric variables: mean, min, 25th-percentile, median, 75th-percentile, maximum, standard deviation Factor variables: proportion factor level overall dataset Default: number unique values number missing values","code":""},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate summary statistics — get_tableone","text":"","code":"get_tableone(   data,   strata = NULL,   overall = TRUE,   summary_function = summarize_short )  # S3 method for default get_tableone(   data,   strata = NULL,   overall = TRUE,   summary_function = summarize_short )"},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate summary statistics — get_tableone","text":"data dataset summarize dataframe tibble strata Stratifying/Grouping variable name(s) character vector. NULL, overall results returned overall TRUE, summary statistics overall dataset also calculated summary_function function defining summary statistics numeric categorical values","code":""},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate summary statistics — get_tableone","text":"object class tableone. list data specified summaries input variables.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate summary statistics — get_tableone","text":"possible provide summary function. Please loot summary inspiration.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate summary statistics — get_tableone","text":"columns table summarized. columns shall used, please select variables prior creating summary table using dplyr::select()","code":""},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate summary statistics — get_tableone","text":"","code":"# Example using the ovarian data set  survival::ovarian %>%   dplyr::select(-fustat) %>%   dplyr::mutate(     age_group = factor(       dplyr::case_when(         age <= 50 ~ \"<= 50 years\",         age <= 60 ~ \"<= 60 years\",         age <= 70 ~ \"<= 70 years\",         TRUE ~ \"> 70 years\"       )     ),     rx = factor(rx),     ecog.ps = factor(ecog.ps)   ) %>%   dplyr::select(age, age_group, everything()) %>%   visR::get_tableone() #> # A tibble: 21 × 3 #>    variable  statistic    Total            #>    <chr>     <chr>        <chr>            #>  1 Sample    N            26               #>  2 age       Mean (SD)    56.2 (10.1)      #>  3 age       Median (IQR) 56.8 (50.2-62.4) #>  4 age       Min-max      38.9-74.5        #>  5 age       Missing      0 (0%)           #>  6 age_group <= 50 years  6 (23.1%)        #>  7 age_group <= 60 years  13 (50.0%)       #>  8 age_group <= 70 years  4 (15.4%)        #>  9 age_group > 70 years   3 (11.5%)        #> 10 futime    Mean (SD)    600 (340)        #> # ℹ 11 more rows  # Examples using ADaM data  # display patients in an analysis set adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(TRTA) %>%   visR::get_tableone() #> # A tibble: 4 × 3 #>   variable statistic            Total      #>   <chr>    <chr>                <chr>      #> 1 Sample   N                    254        #> 2 TRTA     Placebo              86 (33.9%) #> 3 TRTA     Xanomeline High Dose 84 (33.1%) #> 4 TRTA     Xanomeline Low Dose  84 (33.1%)  ## display overall summaries for demog adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, RACE) %>%   visR::get_tableone() #> # A tibble: 13 × 3 #>    variable statistic                        Total         #>    <chr>    <chr>                            <chr>         #>  1 Sample   N                                254           #>  2 AGE      Mean (SD)                        75.1 (8.25)   #>  3 AGE      Median (IQR)                     77 (70-81)    #>  4 AGE      Min-max                          51-89         #>  5 AGE      Missing                          0 (0%)        #>  6 AGEGR1   <65                              33 (13.0%)    #>  7 AGEGR1   >80                              77 (30.3%)    #>  8 AGEGR1   65-80                            144 (56.7%)   #>  9 SEX      F                                143 (56.3%)   #> 10 SEX      M                                111 (43.7%)   #> 11 RACE     AMERICAN INDIAN OR ALASKA NATIVE 1 (0.394%)    #> 12 RACE     BLACK OR AFRICAN AMERICAN        23 (9.055%)   #> 13 RACE     WHITE                            230 (90.551%)  ## By actual treatment adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, RACE, TRTA) %>%   visR::get_tableone(strata = \"TRTA\") #> # A tibble: 13 × 6 #>    variable statistic Total Placebo `Xanomeline High Dose` `Xanomeline Low Dose` #>    <chr>    <chr>     <chr> <chr>   <chr>                  <chr>                 #>  1 Sample   N         254   86      84                     84                    #>  2 AGE      Mean (SD) 75.1… 75.2 (… 74.4 (7.89)            75.7 (8.29)           #>  3 AGE      Median (… 77 (… 76 (69… 76 (70.8-80)           77.5 (71-82)          #>  4 AGE      Min-max   51-89 52-89   56-88                  51-88                 #>  5 AGE      Missing   0 (0… 0 (0%)  0 (0%)                 0 (0%)                #>  6 AGEGR1   <65       33 (… 14 (16… 11 (13.1%)             8 (9.52%)             #>  7 AGEGR1   >80       77 (… 30 (34… 18 (21.4%)             29 (34.52%)           #>  8 AGEGR1   65-80     144 … 42 (48… 55 (65.5%)             47 (55.95%)           #>  9 SEX      F         143 … 53 (61… 40 (47.6%)             50 (59.5%)            #> 10 SEX      M         111 … 33 (38… 44 (52.4%)             34 (40.5%)            #> 11 RACE     AMERICAN… 1 (0… NA      1 (1.19%)              NA                    #> 12 RACE     BLACK OR… 23 (… 8 (9.3… 9 (10.71%)             6 (7.14%)             #> 13 RACE     WHITE     230 … 78 (90… 74 (88.10%)            78 (92.86%)            ## By actual treatment, without overall adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, EVNTDESC, TRTA) %>%   visR::get_tableone(strata = \"TRTA\", overall = FALSE) #> # A tibble: 12 × 5 #>    variable statistic       Placebo `Xanomeline High Dose` `Xanomeline Low Dose` #>    <chr>    <chr>           <chr>   <chr>                  <chr>                 #>  1 Sample   N               86      84                     84                    #>  2 AGE      Mean (SD)       75.2 (… 74.4 (7.89)            75.7 (8.29)           #>  3 AGE      Median (IQR)    76 (69… 76 (70.8-80)           77.5 (71-82)          #>  4 AGE      Min-max         52-89   56-88                  51-88                 #>  5 AGE      Missing         0 (0%)  0 (0%)                 0 (0%)                #>  6 AGEGR1   <65             14 (16… 11 (13.1%)             8 (9.52%)             #>  7 AGEGR1   >80             30 (34… 18 (21.4%)             29 (34.52%)           #>  8 AGEGR1   65-80           42 (48… 55 (65.5%)             47 (55.95%)           #>  9 SEX      F               53 (61… 40 (47.6%)             50 (59.5%)            #> 10 SEX      M               33 (38… 44 (52.4%)             34 (40.5%)            #> 11 EVNTDESC Dematologic Ev… 29 (33… 61 (72.6%)             62 (73.8%)            #> 12 EVNTDESC Study Completi… 57 (66… 23 (27.4%)             22 (26.2%)"},{"path":"https://openpharma.github.io/visR/reference/legendopts.html","id":null,"dir":"Reference","previous_headings":"","what":"Translates options for legend into a list that can be passed to ggplot2 — legendopts","title":"Translates options for legend into a list that can be passed to ggplot2 — legendopts","text":"function takes legend position orientation, defined user puts list ggplot2.","code":""},{"path":"https://openpharma.github.io/visR/reference/legendopts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translates options for legend into a list that can be passed to ggplot2 — legendopts","text":"","code":"legendopts(legend_position = \"right\", legend_orientation = NULL)"},{"path":"https://openpharma.github.io/visR/reference/legendopts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translates options for legend into a list that can be passed to ggplot2 — legendopts","text":"legend_position Default = \"right\". legend_orientation Default = NULL.","code":""},{"path":"https://openpharma.github.io/visR/reference/legendopts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translates options for legend into a list that can be passed to ggplot2 — legendopts","text":"List legend options ggplot2.","code":""},{"path":"https://openpharma.github.io/visR/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. dplyr %>% survival Surv","code":""},{"path":"https://openpharma.github.io/visR/reference/render.html","id":null,"dir":"Reference","previous_headings":"","what":"Render a data.frame, risktable, or tableone object as a table — render","title":"Render a data.frame, risktable, or tableone object as a table — render","text":"Render previously created data.frame, tibble tableone object html, rtf latex","code":""},{"path":"https://openpharma.github.io/visR/reference/render.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Render a data.frame, risktable, or tableone object as a table — render","text":"","code":"render(   data,   title = \"\",   datasource,   footnote = \"\",   output_format = \"html\",   engine = \"gt\",   download_format = c(\"copy\", \"csv\", \"excel\") )"},{"path":"https://openpharma.github.io/visR/reference/render.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Render a data.frame, risktable, or tableone object as a table — render","text":"data Input data.frame tibble visualize title Specify title text string displayed rendered table. Default title. datasource String specifying data source underlying data set. Default title. footnote String specifying additional information displayed footnote alongside data source specifications statistical tests. output_format Type output returned, can \"html\" \"latex\". Default \"html\". engine \"html\" selected output_format, one can chose using kable, gt DT engine render output table. Default \"gt\". download_format Options formats generated downloading data. Default list \"c('copy', 'csv', 'excel')\".","code":""},{"path":"https://openpharma.github.io/visR/reference/render.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Render a data.frame, risktable, or tableone object as a table — render","text":"table data structure possible interactive functionality depending choice engine.","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":null,"dir":"Reference","previous_headings":"","what":"Step ribbon statistic — stat_stepribbon","title":"Step ribbon statistic — stat_stepribbon","text":"Provides stair-step values ribbon plots, often using conjunction ggplot2::geom_step(). step ribbon can added stat_stepribbon() identically ggplot2::geom_ribbon(stat = \"stepribbon\")","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Step ribbon statistic — stat_stepribbon","text":"","code":"stat_stepribbon(   mapping = NULL,   data = NULL,   geom = \"ribbon\",   position = \"identity\",   na.rm = FALSE,   show.legend = NA,   inherit.aes = TRUE,   direction = \"hv\",   ... )  StatStepribbon"},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Step ribbon statistic — stat_stepribbon","text":"object class StatStepRibbon (inherits Stat, ggproto, gg) length 3.","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Step ribbon statistic — stat_stepribbon","text":"mapping Set aesthetic mappings created aes(). specified inherit.aes = TRUE (default), combined default mapping top level plot. must supply mapping plot mapping. data data displayed layer. three options: NULL, default, data inherited plot data specified call ggplot(). data.frame, object, override plot data. objects fortified produce data frame. See fortify() variables created. function called single argument, plot data. return value must data.frame, used layer data. function can created formula (e.g. ~ head(.x, 10)). geom geom use; defaults \"ribbon\" position Position adjustment, either string naming adjustment (e.g. \"jitter\" use position_jitter), result call position adjustment function. Use latter need change settings adjustment. na.rm FALSE, default, missing values removed warning. TRUE, missing values silently removed. show.legend logical. layer included legends? NA, default, includes aesthetics mapped. FALSE never includes, TRUE always includes. can also named logical vector finely select aesthetics display. inherit.aes FALSE, overrides default aesthetics, rather combining . useful helper functions define data aesthetics inherit behaviour default plot specification, e.g. borders(). direction hv horizontal-vertical steps, vh vertical-horizontal steps ... arguments passed layer(). often aesthetics, used set aesthetic fixed value, like colour = \"red\" size = 3. may also parameters paired geom/stat.","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Step ribbon statistic — stat_stepribbon","text":"ggplot","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Step ribbon statistic — stat_stepribbon","text":"https://groups.google.com/forum/?fromgroups=#!topic/ggplot2/9cFWHaH1CPs","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Step ribbon statistic — stat_stepribbon","text":"","code":"# using ggplot2::geom_ribbon() survival::survfit(survival::Surv(time, status) ~ 1, data = survival::lung) %>%   survival::survfit0() %>%   broom::tidy() %>%   ggplot2::ggplot(ggplot2::aes(x = time, y = estimate, ymin = conf.low, ymax = conf.high)) +   ggplot2::geom_step() +   ggplot2::geom_ribbon(stat = \"stepribbon\", alpha = 0.2)   # using stat_stepribbon() with the same result survival::survfit(survival::Surv(time, status) ~ 1, data = survival::lung) %>%   survival::survfit0() %>%   broom::tidy() %>%   ggplot2::ggplot(ggplot2::aes(x = time, y = estimate, ymin = conf.low, ymax = conf.high)) +   ggplot2::geom_step() +   visR::stat_stepribbon(alpha = 0.2)"},{"path":"https://openpharma.github.io/visR/reference/summarize_long.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate summary statistics from a vector — summarize_long","title":"Calculate summary statistics from a vector — summarize_long","text":"Calculates several summary statistics. summary statistics depend vector class","code":""},{"path":"https://openpharma.github.io/visR/reference/summarize_long.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate summary statistics from a vector — summarize_long","text":"","code":"summarize_long(x)  # S3 method for factor summarize_long(x)  # S3 method for integer summarize_long(x)  # S3 method for numeric summarize_long(x)  # S3 method for default summarize_long(x)"},{"path":"https://openpharma.github.io/visR/reference/summarize_long.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate summary statistics from a vector — summarize_long","text":"x object","code":""},{"path":"https://openpharma.github.io/visR/reference/summarize_long.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate summary statistics from a vector — summarize_long","text":"summarized version input.","code":""},{"path":"https://openpharma.github.io/visR/reference/summarize_short.html","id":null,"dir":"Reference","previous_headings":"","what":"Create abbreviated variable summary for table1 — summarize_short","title":"Create abbreviated variable summary for table1 — summarize_short","text":"function creates summaries combines multiple summary measures single formatted string. Create variable summary numeric variables. Calculates mean (standard deviation), median (IQR), min-max range N/% missing elements numeric vector. Create variable summary integer variables Calculates mean (standard deviation), median (IQR), min-max range N/% missing elements integer vector.","code":""},{"path":"https://openpharma.github.io/visR/reference/summarize_short.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create abbreviated variable summary for table1 — summarize_short","text":"","code":"summarize_short(x)  # S3 method for factor summarize_short(x)  # S3 method for numeric summarize_short(x)  # S3 method for integer summarize_short(x)  # S3 method for default summarize_short(x)"},{"path":"https://openpharma.github.io/visR/reference/summarize_short.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create abbreviated variable summary for table1 — summarize_short","text":"x vector summarized","code":""},{"path":"https://openpharma.github.io/visR/reference/summarize_short.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create abbreviated variable summary for table1 — summarize_short","text":"summarized less detailed version input.","code":""},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":null,"dir":"Reference","previous_headings":"","what":"Display a summary Table (i.e. table one) — tableone","title":"Display a summary Table (i.e. table one) — tableone","text":"Wrapper function produce summary table (.e. Table One). Create render summary table dataset. typical example summary table \"table one\", first table applied medical research manuscript. Calculate summary statistics present formatted table","code":""},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display a summary Table (i.e. table one) — tableone","text":"","code":"tableone(   data,   title,   datasource,   footnote = \"\",   strata = NULL,   overall = TRUE,   summary_function = summarize_short,   ... )"},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display a summary Table (i.e. table one) — tableone","text":"data dataframe tibble visualize title Table title include rendered table. Input text string. datasource String specifying datasource underlying data set footnote Table footnote include rendered table. Input text string. strata Character vector column names use stratification summary table. Default: NULL , indicates stratification. overall TRUE, summary statistics overall dataset also calculated summary_function function defining summary statistics numeric categorical values Pre-implemented functions summarize_long summarize_short ... Pass options render_table","code":""},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display a summary Table (i.e. table one) — tableone","text":"table-like data structure, possibly interactive depending choice engine","code":""},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":"example-output","dir":"Reference","previous_headings":"","what":"Example Output","title":"Display a summary Table (i.e. table one) — tableone","text":"tableone(engine = \"gt\")  tableone(engine = \"DT\")  tableone(engine = \"kable\")  tableone(engine = \"kable\", output_format = \"latex\")","code":""},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display a summary Table (i.e. table one) — tableone","text":"","code":"# metadata for table t1_title <- \"Cohort Summary\" t1_ds <- \"ADaM Interim Dataset for Time-to-Event Analysis\" t1_fn <- \"My table one footnote\"   ## table by treatment - without overall and render with GT tbl_gt <-   adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, EVNTDESC, TRTA) %>%   visR::tableone(     strata = \"TRTA\",     overall = FALSE,     title = t1_title,     datasource = t1_ds,     footnote = t1_fn,     engine = \"gt\"   )  ## table by treatment - without overall and render with DT tbl_DT <-   adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, EVNTDESC, TRTA) %>%   visR::tableone(     strata = \"TRTA\",     overall = FALSE,     title = t1_title,     datasource = t1_ds,     footnote = t1_fn,     engine = \"DT\"   )  ## table by treatment - without overall and render with kable tbl_kable_html <-   adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, EVNTDESC, TRTA) %>%   visR::tableone(     strata = \"TRTA\",     overall = FALSE,     title = t1_title,     datasource = t1_ds,     footnote = t1_fn,     engine = \"kable\"   )  ## table by treatment - without overall and render with kable as ## a latex table format rather than html tbl_kable_latex <-   adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, EVNTDESC, TRTA) %>%   visR::tableone(     strata = \"TRTA\",     overall = FALSE,     title = t1_title,     datasource = t1_ds,     footnote = t1_fn,     output_format = \"latex\",     engine = \"kable\"   )"},{"path":"https://openpharma.github.io/visR/reference/the_lhs.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the ","title":"Find the ","text":"function finds left-hand sided symbol magrittr pipe returns character.","code":""},{"path":"https://openpharma.github.io/visR/reference/the_lhs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the ","text":"","code":"the_lhs()"},{"path":"https://openpharma.github.io/visR/reference/the_lhs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the ","text":"Left-hand sided symbol string magrittr pipe.","code":""},{"path":"https://openpharma.github.io/visR/reference/the_lhs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Find the ","text":"https://github.com/tidyverse/magrittr/issues/115#issuecomment-173894787","code":""},{"path":"https://openpharma.github.io/visR/reference/the_lhs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find the ","text":"","code":"blah <- function(x) the_lhs() adtte %>%   blah() #> [1] \"adtte\""},{"path":"https://openpharma.github.io/visR/reference/tidyme.html","id":null,"dir":"Reference","previous_headings":"","what":"Extended tidy cleaning of selected objects using S3 method — tidyme","title":"Extended tidy cleaning of selected objects using S3 method — tidyme","text":"S3 method extended tidying selected model outputs. Note visR method retains original nomenclature objects, adds one broom::tidy ensure compatibility tidy workflows. default method relies broom::tidy return tidied object","code":""},{"path":"https://openpharma.github.io/visR/reference/tidyme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extended tidy cleaning of selected objects using S3 method — tidyme","text":"","code":"tidyme(x, ...)  # S3 method for default tidyme(x, ...)  # S3 method for survfit tidyme(x, ...)"},{"path":"https://openpharma.github.io/visR/reference/tidyme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extended tidy cleaning of selected objects using S3 method — tidyme","text":"x S3 object ... arguments passed method","code":""},{"path":"https://openpharma.github.io/visR/reference/tidyme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extended tidy cleaning of selected objects using S3 method — tidyme","text":"Data frame containing list elements S3 object columns. column 'strata' factor ensure strata sorted agreement order survfit object","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/tidyme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extended tidy cleaning of selected objects using S3 method — tidyme","text":"","code":"## Extended tidying for a survfit object surv_object <- visR::estimate_KM(data = adtte, strata = \"TRTA\") tidied <- visR::tidyme(surv_object)  ## Tidyme for non-included classes data <- cars lm_object <- stats::lm(data = cars, speed ~ dist) lm_tidied <- visR::tidyme(lm_object) #> tidyme S3 default method (broom::tidy) used. lm_tidied #>          term  estimate  std.error statistic      p.value #> 1 (Intercept) 8.2839056 0.87438449  9.473985 1.440974e-12 #> 2        dist 0.1655676 0.01749448  9.463990 1.489836e-12"},{"path":"https://openpharma.github.io/visR/reference/visR-Global.html","id":null,"dir":"Reference","previous_headings":"","what":"visR package — visR Global","title":"visR package — visR Global","text":"Set global variables","code":""},{"path":"https://openpharma.github.io/visR/reference/visR-package.html","id":null,"dir":"Reference","previous_headings":"","what":"visR: Clinical Graphs and Tables Adhering to Graphical Principles — visR-package","title":"visR: Clinical Graphs and Tables Adhering to Graphical Principles — visR-package","text":"enable fit--purpose, reusable clinical medical research focused visualizations tables sensible defaults based graphical principles described : \"Vandemeulebroecke et al. (2018)\" doi:10.1002/pst.1912 , \"Vandemeulebroecke et al. (2019)\" doi:10.1002/psp4.12455 , \"Morris et al. (2019)\" doi:10.1136/bmjopen-2019-030215 .","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/visR-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"visR: Clinical Graphs and Tables Adhering to Graphical Principles — visR-package","text":"Maintainer: Mark Baillie bailliem@gmail.com [copyright holder] Authors: Diego Saldana diego.saldana@roche.com Charlotta Fruechtenicht charlotta.fruechtenicht@roche.com Marc Vandemeulebroecke marc.vandemeulebroecke@novartis.com Thanos Siadimas thanos.siadimas@roche.com Pawel Kawski pawel.kawski@contractors.roche.com Steven Haesendonckx shaesen2@.jnj.com James Black james.black.jb2@roche.com Pelagia Alexandra Papadopoulou PPapadop@.jnj.com Tim Treis tim.treis@outlook.de Rebecca Albrecht rebecca.albrecht.dietsch@gmail.com Daniel D. Sjoberg danield.sjoberg@gmail.com (ORCID) contributors: Ardalan Mirshani ardeeshany@gmail.com [contributor]","code":""},{"path":"https://openpharma.github.io/visR/reference/visr.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a supported S3 object — visr","title":"Plot a supported S3 object — visr","text":"S3 method creating plots directly objects using ggplot2, similar base R plot() function. Methods visr.survfit() visr.tidycuminc() deprecated favor ggsurvfit::ggsurvfit() ggsurvfit::ggcuminc(), respectively. visr.attrition() function draw Consort flow diagram chart currently questioned.","code":""},{"path":"https://openpharma.github.io/visR/reference/visr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a supported S3 object — visr","text":"","code":"visr(x, ...)  # S3 method for default visr(x, ...)  # S3 method for survfit visr(   x = NULL,   x_label = NULL,   y_label = NULL,   x_units = NULL,   x_ticks = NULL,   y_ticks = NULL,   fun = \"surv\",   legend_position = \"right\",   ... )  # S3 method for attrition visr(   x,   description_column_name = \"Criteria\",   value_column_name = \"Remaining N\",   complement_column_name = \"\",   box_width = 50,   font_size = 12,   fill = \"white\",   border = \"black\",   ... )  # S3 method for tidycuminc visr(   x = NULL,   x_label = \"Time\",   y_label = \"Cumulative Incidence\",   x_units = NULL,   x_ticks = pretty(x$tidy$time, 10),   y_ticks = pretty(c(0, 1), 5),   legend_position = \"right\",   ... )"},{"path":"https://openpharma.github.io/visR/reference/visr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a supported S3 object — visr","text":"x Object class attritiontable ... arguments passed method x_label character Label x-axis. specified, function look \"PARAM\" \"PARAMCD\" information original data set (CDISC standards). \"PARAM\"/\"PARAMCD\" information available, default x-axis label \"Time\". y_label character Label y-axis. specified, default proposal, depending fun argument. x_units Unit added x_label (x_label (x_unit)). Default NULL. x_ticks Ticks x-axis. specified, default proposal. y_ticks Ticks y-axis. specified, default proposal based fun argument. fun Function represents scale estimate. current options : legend_position Specifies legend position plot. Character values allowed \"top\" \"left\" \"bottom\" \"right\". Numeric coordinates also allowed. Default \"right\". description_column_name character Name column containing inclusion descriptions value_column_name character Name column containing remaining sample counts complement_column_name character Optional: Name column containing exclusion descriptions box_width character box width box flow chart font_size character fontsize pt fill color (string hexcode) use fill boxes flowchart border color (string hexcode) use borders boxes flowchart","code":""},{"path":"https://openpharma.github.io/visR/reference/visr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a supported S3 object — visr","text":"Object class ggplot ggsurvplot survfit objects.","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/visr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a supported S3 object — visr","text":"","code":"# fit KM km_fit <- survival::survfit(survival::Surv(AVAL, 1 - CNSR) ~ TRTP, data = adtte)  # plot curves using survival plot function plot(km_fit)   # plot same curves using visR::visr plotting function visR::visr(km_fit)   # estimate KM using visR wrapper survfit_object <- visR::estimate_KM(data = adtte, strata = \"TRTP\")  # Plot survival probability visR::visr(survfit_object, fun = \"surv\")   # Plot survival percentage visR::visr(survfit_object, fun = \"pct\")   # Plot cumulative hazard visR::visr(survfit_object, fun = \"cloglog\") #> Warning: NAs introduced by y-axis transformation.    ## Create attrition attrition <- visR::get_attrition(adtte,   criteria_descriptions = c(     \"1. Not in Placebo Group\",     \"2. Be 75 years of age or older.\",     \"3. White\",     \"4. Female\"   ),   criteria_conditions = c(     \"TRTP != 'Placebo'\",     \"AGE >= 75\",     \"RACE=='WHITE'\",     \"SEX=='F'\"   ),   subject_column_name = \"USUBJID\" )  ## Draw a CONSORT attrition chart without specifying extra text for the complement attrition %>%   visr(\"Criteria\", \"Remaining N\")   ## Add detailed complement descriptions to the \"exclusion\" part of the CONSORT diagram # Step 1. Add new column to attrition dataframe attrition$Complement <- c(   \"NA\",   \"Placebo Group\",   \"Younger than 75 years\",   \"Non-White\",   \"Male\" )  # Step 2. Define the name of the column in the call to the plotting function attrition %>%   visr(\"Criteria\", \"Remaining N\", \"Complement\")   ## Styling the CONSORT flowchart # Change the fill and outline of the boxes in the flowchart attrition %>%   visr(\"Criteria\", \"Remaining N\", \"Complement\", fill = \"lightblue\", border = \"grey\")   ## Adjust the font size in the boxes attrition %>%   visr(\"Criteria\", \"Remaining N\", font_size = 10)"},{"path":"https://openpharma.github.io/visR/news/index.html","id":"visr-040","dir":"Changelog","previous_headings":"","what":"visR 0.4.0","title":"visR 0.4.0","text":"CRAN release: 2023-11-20 Functions estimate_KM() visr.survfit() deprecated favor ggsurvfit::ggsurvfit().","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"visr-031","dir":"Changelog","previous_headings":"","what":"visR 0.3.1","title":"visR 0.3.1","text":"CRAN release: 2022-08-17 now zoom figure (e.g. Kaplan-Meier figure) ggplot2::coord_cartesian() instead using scale_x_continuous(limits=) scale_y_continuous(limits=). latter first removes data outside limits, constructs line. Zooming constructs full line, zooms limits. useful risktable often reports estimates near end KM figure, line cutoff shown last time point. (#402) README update contributor listing. (#435)","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"visr-030","dir":"Changelog","previous_headings":"","what":"visR 0.3.0","title":"visR 0.3.0","text":"CRAN release: 2022-06-24","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"new-functions-0-3-0","dir":"Changelog","previous_headings":"","what":"New functions","title":"visR 0.3.0","text":"Highlight specific strata survival plots using add_highlight(). Indicate quantiles survival plots using add_quantiles(). Estimation cumulative incidence presence competing risks now possible estimate_cuminc(). risk estimates can plotted similarly estimates estimate_KM() using visr() function. Function Surv_CNSR() used CDISC ADTTE conventions default values time status indicators time--event analyses. note, status variable must coded 0/1 1 indicating observation censored. (#391)","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"breaking-changes-0-3-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"visR 0.3.0","text":"AlignPlots() renamed align_plots().","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"visR 0.3.0","text":"Fix get_pvalue() estimate_KM() objects data piped modifications (e.g. dplyr::filter(), dplyr::mutate()). Fix add_risktable() estimates assigned incorrect label. Review unit testing estimate_KM(formula=) argument. (#399) Fixed bug define_theme(strata=). (#388) Fix discrepancy issue saving survfit() call estimate_KM(). (#365) Added check class(x)==. (#358) Fix documentation S3 method visr(). (#301)","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"other-updates-0-3-0","dir":"Changelog","previous_headings":"","what":"Other Updates","title":"visR 0.3.0","text":"Stratifying variable names removed legend visr.survfit() figures, legend title now describes stratifying variable(s). (#343) estimate_KM() function gains experimental formula= argument. argument used, AVAL, CNSR, strata arguments typically used construct formula ignored. (#379) add_risktable rowgutter argument allow spacing plot risktables strata variable now removed body tableone() results. (#254) visr.survfit() function longer warns x-axis label PARAM column found original data set. (#378) call saved estimate_KM() object updated quosure–ensuring original function call can always recalled. define_theme()updated ensure strata present theme displayed. (#388) Removed external dependency easyalluvial package. (#383) Reduced number exported functions. (#381) Improved documentation visr() generic functions. (#301) (#357) Unit testing updated relevant 100% code coverage function requirements tested. README page updated additional examples. (#425) lifecycle badges added function level indicate functions still experimental questioning phases. (#398)","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"visr-020","dir":"Changelog","previous_headings":"","what":"visR 0.2.0","title":"visR 0.2.0","text":"CRAN release: 2021-06-14 Initial CRAN release.","code":""}]
+[{"path":"https://openpharma.github.io/visR/CODE_OF_CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behavior participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behavior may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://www.contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/.","code":""},{"path":"https://openpharma.github.io/visR/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022 visR authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://openpharma.github.io/visR/articles/CDISC_ADaM.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Survival Analysis with visR using CDISC ADaM Time-To-Event Analysis Dataset (ADTTE)","text":"tutorial illustrates standard time--event analysis can done efficiently data set adheres CDISC ADaM standard. detailed time--event analysis broad overview visR’s functionality presented another vignette.","code":"library(ggplot2) library(visR)"},{"path":"https://openpharma.github.io/visR/articles/CDISC_ADaM.html","id":"global-document-setup","dir":"Articles","previous_headings":"","what":"Global Document Setup","title":"Survival Analysis with visR using CDISC ADaM Time-To-Event Analysis Dataset (ADTTE)","text":"","code":"# Metadata Title DATASET <- paste0(\"Analyis Data Time-To-Event (ADTTE)\")  # Save original options() old <- options()    # Global formatting options options(digits = 3)  # Global ggplot settings theme_set(theme_bw())  # Global table settings  options(DT.options = list(pageLength = 10,                            language = list(search = 'Filter:'),                            scrollX = TRUE))  # load ADTTE from CDISC pilot  data(adtte)  # Restore original options() options(old)"},{"path":"https://openpharma.github.io/visR/articles/CDISC_ADaM.html","id":"time-to-event-analysis","dir":"Articles","previous_headings":"","what":"Time-to-event analysis","title":"Survival Analysis with visR using CDISC ADaM Time-To-Event Analysis Dataset (ADTTE)","text":"visR includes wrapper function easily display summary tables (e.g. tableone) wrapper function estimate Kaplan-Meier curve compatible %>% purrr::map functions without losing traceability dataset name inside call object. data set adheres CDISC ADaM standards, stratifier needs specified. Given survival object, visR includes several functions quickly extract additional information survival object (e.g. test statistics p-values) general function display table (render). survival object can plotted using visR function visr. Additional information like confidence intervals risktable can added plot.","code":"# Display a summary table (e.g. tableone) visR::tableone(adtte[,c(\"TRTP\", \"AGE\")],          title = \"Demographic summary\" , datasource = DATASET) #> Warning: There was 1 warning in `summarise()`. #> ℹ In argument: `TRTP = (function (x) ...`. #> ℹ In group 1: `all = \"Total\"`. #> Caused by warning: #> ! `fct_explicit_na()` was deprecated in forcats 1.0.0. #> ℹ Please use `fct_na_value_to_level()` instead. #> ℹ The deprecated feature was likely used in the visR package. #>   Please report the issue at <https://github.com/openpharma/visR/issues>. # Estimate a survival object survfit_object <-  adtte %>%   visR::estimate_KM(data = ., strata = \"TRTP\") #> Warning: `estimate_KM()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated. survfit_object #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     TRTP, data = data) #>  #>                            n events median 0.95LCL 0.95UCL #> TRTP=Placebo              86     29     NA      NA      NA #> TRTP=Xanomeline High Dose 84     61     36      25      47 #> TRTP=Xanomeline Low Dose  84     62     33      28      51 # Display test statistics associated with the survival estimate  visR::render(survfit_object %>% get_pvalue(), title = \"P-values\", datasource = DATASET) # Create and display a Kaplan-Meier from the survival object and add a risktable visr(survfit_object) %>%    visR::add_CI() %>%   visR::add_risktable() #> Warning: `visr.survfit()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"data-preparation","dir":"Articles","previous_headings":"Attrition example","what":"Data preparation","title":"Creating consort flow diagram with visR","text":"Prepare data using attrition function.","code":"attrition <- visR::get_attrition(adtte,    criteria_descriptions = c(\"1. Not in Placebo Group\",                              \"2. Be 75 years of age or older.\",                              \"3. White\",                              \"4. Female\"),    criteria_conditions   = c(\"TRTP != 'Placebo'\",                              \"AGE >= 75\",                              \"RACE=='WHITE'\",                              \"SEX=='F'\"),    subject_column_name   = \"USUBJID\")"},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"render-chart","dir":"Articles","previous_headings":"Attrition example","what":"Render chart","title":"Creating consort flow diagram with visR","text":"Draw CONSORT attrition chart without specifying extra text complement","code":"attrition %>%   visR::visr(\"Criteria\", \"Remaining N\")"},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"adding-more-detail","dir":"Articles","previous_headings":"Attrition example","what":"Adding more detail","title":"Creating consort flow diagram with visR","text":"Adding detailed complement descriptions “exclusion” part CONSORT diagram","code":""},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"add-the-control-group","dir":"Articles","previous_headings":"Attrition example > Adding more detail","what":"Add the control group","title":"Creating consort flow diagram with visR","text":"Step 1. Add new column attrition dataframe","code":"attrition$Complement <- c(\"NA\", \"Placebo Group\", \"Younger than 75 years\", \"Non-White\", \"Male\")"},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"define-metadata","dir":"Articles","previous_headings":"Attrition example > Adding more detail","what":"Define metadata","title":"Creating consort flow diagram with visR","text":"Step 2. Define name column call plotting function","code":"attrition %>%   visR::visr(\"Criteria\", \"Remaining N\", \"Complement\")"},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"styling-the-consort-flowchart-","dir":"Articles","previous_headings":"Additional features","what":"Styling the CONSORT flowchart.","title":"Creating consort flow diagram with visR","text":"Change fill outline boxes flowchart","code":"attrition %>%   visR::visr(\"Criteria\", \"Remaining N\", \"Complement\", fill = \"lightblue\", border=\"grey\")"},{"path":"https://openpharma.github.io/visR/articles/Consort_flow_diagram.html","id":"adjusting-size","dir":"Articles","previous_headings":"Additional features","what":"Adjusting size","title":"Creating consort flow diagram with visR","text":"Adjust font size boxes","code":"attrition %>%  visR::visr(\"Criteria\", \"Remaining N\", font_size = 10)"},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Styling survival plots","text":"tutorial illustrates usage styling function visR provides. default, visR::visr() apply form visual changes generated survival plots. Therefore, default output looks like expect standard ggplot2::ggplot() plot. examples visualize results estimate_KM(), principles apply competing risks cumulative incidence objects created estimate_cuminc() well.","code":"library(visR)"},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"preparation-of-the-data","dir":"Articles","previous_headings":"Introduction","what":"Preparation of the data","title":"Styling survival plots","text":"example, work patient data NCCTG Lung Cancer dataset part survival package. data also used demonstrate functions visR another vignette. However, particular one, used demonstrate adjustments aesthetics.","code":""},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"generation-of-a-survfit-object","dir":"Articles","previous_headings":"Introduction","what":"Generation of a survfit object","title":"Styling survival plots","text":"","code":"lung_cohort <- survival::lung  lung_cohort <- lung_cohort %>%     dplyr::mutate(sex = as.factor(ifelse(sex == 1, \"Male\", \"Female\")))  %>%     dplyr::mutate(status = status - 1) %>%   dplyr::rename(Age = \"age\", Sex = \"sex\", Status = \"status\", Days = \"time\")   lung_suvival_object <- lung_cohort %>%   visR::estimate_KM(strata = \"Sex\", CNSR = \"Status\", AVAL = \"Days\") #> Warning: `estimate_KM()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"plotting-the-generated-survfit-object-without-adjustments","dir":"Articles","previous_headings":"Styling","what":"Plotting the generated survfit object without adjustments","title":"Styling survival plots","text":"can, plot shows default ggplot2::theme_grey() style grey background, visible grid default ggplot2 colours.","code":"p <- lung_suvival_object %>%   visR::visr() #> Warning: `visr.survfit()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated. p"},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"using-ggplot2-to-style-the-plot","dir":"Articles","previous_headings":"Styling","what":"Using ggplot2 to style the plot","title":"Styling survival plots","text":"Since visR::visr() also generates valid ggplot object output, can use conventional styling logic options ggplot2 provides, shown .  However, visR also provides functions adjust common aesthetics easily less code.","code":"p +   ggplot2::theme_bw() +   ggplot2::theme(legend.position = \"top\") +   ggplot2::scale_color_manual(values = c(\"red\", \"blue\"))"},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"using-visr-to-style-the-plot","dir":"Articles","previous_headings":"Styling","what":"Using visR to style the plot","title":"Styling survival plots","text":"direct option style plots generated visR::visr() using parameters function provides. Internally, parameters like y-axis label automatically deducted used function. following example demonstrates options exposed.  However, rather minimal adjustments usually don’t cover things user wants modify. Therefore, provide two additional functions adjust aesthetics: visR::define_theme() visR::apply_theme(). first one provides easy wrapper create nested list list styling options applied plot second function.","code":"lung_suvival_object %>%   visR::visr(x_label = \"Time\",              y_label = NULL, # NULL (default) causes the label to be deducted from the used function              x_ticks = seq(0, 1200, 200),              y_ticks = seq(0, 100, 20),              fun = \"pct\",              legend_position = \"top\")"},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"defining-a-visr_theme-using-visrdefine_theme","dir":"Articles","previous_headings":"New themes","what":"Defining a visR_theme using visR::define_theme()","title":"Styling survival plots","text":"options provided visR::define_theme(), nonetheless returns minimal list reasonable defaults. However, function also takes several styling options. currently usable ones displayed . One particular use mind writing function , wanted option define different colours strata worry present.","code":"visR::define_theme()  #> $fontfamily #> [1] \"Helvetica\" #>  #> $grid #> [1] FALSE #>  #> $bg #> [1] \"transparent\" #>  #> attr(,\"class\") #> [1] \"visR_theme\" \"list\" theme <-   visR::define_theme(     strata = list(       \"Sex\" = list(\"Female\" = \"red\",                    \"Male\" = \"blue\"),       \"ph.ecog\" = list(\"0\" = \"cyan\",                        \"1\" = \"purple\",                        \"2\" = \"brown\")     ),     fontsizes = list(       \"axis\" = 12,       \"ticks\" = 10,       \"legend_title\" = 10,       \"legend_text\" = 8     ),     fontfamily = \"Helvetica\",     grid = list(\"major\" = FALSE,                 \"minor\" = FALSE),     #grid = TRUE/FALSE # <- can also be used instead of the named list above     bg = \"transparent\",     legend_position = \"top\"   )"},{"path":"https://openpharma.github.io/visR/articles/Styling_KM_plots.html","id":"apply-styling-using-visrapply_theme","dir":"Articles","previous_headings":"New themes","what":"Apply styling using visR::apply_theme()","title":"Styling survival plots","text":"visR::apply_theme() function exposes user two ways style plot. direct one just apply function plot without specifying options. applies several reasonable defaults plot.  second one apply nested list lists , ideally generated visR::define_theme() plot. serves purpose generate detailed visR_theme object apply one several plots single line. lists also easily saved shared. usage theme generated shown .","code":"lung_suvival_object %>%   visR::visr() %>%   visR::apply_theme() lung_suvival_object %>%   visR::visr() %>%   visR::apply_theme(theme)"},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Survival Analysis with visR","text":"tutorial illustrates typical use case clinical development - analysis time certain event (e.g., death) different groups. Typically, data obtained randomized clinical trials (RCT) can used estimate overall survival patients one group (e.g., treated drug X) vs another group (e.g., treated drug Y) thus determine treatment difference. thorough introduction Survival Analysis, recommend following tutorial. example, work patient data NCCTG Lung Cancer dataset part survival package. Another vignette presents example using data set following CDISC ADaM standard.","code":"library(ggplot2) library(visR)"},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"global-document-setup","dir":"Articles","previous_headings":"","what":"Global Document Setup","title":"Survival Analysis with visR","text":"","code":"# Metadata Title DATASET <- paste0(\"NCCTG Lung Cancer Dataset (from survival package \",                    packageVersion(\"survival\"), \")\")  # Save original options() old <- options()    # Global formatting options options(digits = 3)  # Global ggplot settings theme_set(theme_bw())  # Global table settings  options(DT.options = list(pageLength = 10,                            language = list(search = 'Filter:'),                            scrollX = TRUE))  lung_cohort <- survival::lung  # Change gender to be a factor and rename some variables to make output look nicer lung_cohort <- lung_cohort %>%     dplyr::mutate(sex = as.factor(ifelse(sex == 1, \"Male\", \"Female\")))  %>%     dplyr::rename(Age = \"age\", Sex = \"sex\", Status = \"status\", Days = \"time\")  # Restore original options() options(old)"},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"cohort-overview-table-one","dir":"Articles","previous_headings":"","what":"Cohort Overview (Table one)","title":"Survival Analysis with visR","text":"Visualizing tables, like table one risk tables, two-step process visR . First, data.frame (tibble) created get_XXX() function (e.g. get_tableone()). Secondly, data.frame can displayed calling function render(). advantage process data summaries can created, used adjusted throughout analysis, every step data summaries can displayed even downloaded. Populations usually displayed -called table one. Function get_tableone creates tibble includes populations summaries. Function render nicely displays tableone. Additionally, visR includes wrapper function create display tableone one function call. Creating visualizing tableone default settings simple can done one line code. However, customization options. get wrapper functions, stratifier can defined column displaying total information can removed. visR’s render supports three different rendering engines flexible possible. default, render uses gt. Additional engines datatable (dt) include easy downloading options… …kable flexible displaying various output formats (html default, latex supported). Overview Lung Cancer patients Called html output format, html view displayed; called latex string containing latex code printed.","code":"# Select variables of interest and change names to look nicer lung_cohort_tab1 <- lung_cohort %>%     dplyr::select(Age, Sex)   # Create a table one tab1 <- visR::get_tableone(lung_cohort_tab1) #> Warning: There was 1 warning in `summarise()`. #> ℹ In argument: `Sex = (function (x) ...`. #> ℹ In group 1: `all = \"Total\"`. #> Caused by warning: #> ! `fct_explicit_na()` was deprecated in forcats 1.0.0. #> ℹ Please use `fct_na_value_to_level()` instead. #> ℹ The deprecated feature was likely used in the visR package. #>   Please report the issue at <https://github.com/openpharma/visR/issues>.  # Render the tableone visR::render(tab1, title = \"Overview over Lung Cancer patients\", datasource = DATASET) # Use wrapper functionality to create and display a tableone visR::tableone(lung_cohort_tab1, title = \"Overview over Lung Cancer patients\", datasource = DATASET) # Create and render a tableone with a stratifier and without displaying the total visR::tableone(lung_cohort_tab1, strata = \"Sex\", overall = FALSE,                title = \"Overview over Lung Cancer patients\", datasource = DATASET) # Create and render a tableone with with dt as an engine visR::tableone(lung_cohort_tab1, strata = \"Sex\", overall = FALSE,                title = \"Overview over Lung Cancer patients\", datasource = DATASET,                 engine = \"dt\") # Create and render a tableone with with kable as an engine and html as output format visR::tableone(lung_cohort_tab1, strata = \"Sex\", overall = FALSE,                 title = \"Overview over Lung Cancer patients\", datasource = DATASET,                 engine = \"kable\", output_format=\"html\")"},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"survival-estimation","dir":"Articles","previous_headings":"Time-to-event analysis","what":"Survival estimation","title":"Survival Analysis with visR","text":"visR provides wrapper function estimate Kaplan-Meier curve several functions visualize results. wrapper function compatible %>% purrr::map functions without losing traceability dataset name.","code":"# Select variables of interest and change names to look nicer lung_cohort_survival <- lung_cohort %>%     dplyr::select(Age, Sex, Status, Days)    # For the survival estimate, the censor must be 0 or 1 lung_cohort_survival$Status <- lung_cohort_survival$Status - 1  # Estimate the survival curve lung_suvival_object <- lung_cohort_survival %>%    visR::estimate_KM(strata = \"Sex\", CNSR = \"Status\", AVAL = \"Days\") #> Warning: `estimate_KM()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated. lung_suvival_object #> Call: ~survival::survfit(formula = survival::Surv(Days, 1 - Status) ~  #>     Sex, data = data) #>  #>              n events median 0.95LCL 0.95UCL #> Sex=Female  90     37    529     376      NA #> Sex=Male   138     26    840     806      NA"},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"survival-visualization","dir":"Articles","previous_headings":"Time-to-event analysis","what":"Survival visualization","title":"Survival Analysis with visR","text":"two frequently used ways estimate time--event data: risk table Kaplan-Meier curve. principle, visR allows either visualize risk table Kaplan-Meier curve separately, together one plot.","code":""},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"displaying-the-risktable","dir":"Articles","previous_headings":"Time-to-event analysis > Survival visualization","what":"Displaying the risktable","title":"Survival Analysis with visR","text":"Creating visualizing risk table separately works exact way tableone (): First, get_risktable() creates tibble risk information can still changed. Secondly, risk table can rendered displayed. risktable one piece information can extracted survival object get_XXX rendered.","code":"# Create a risktable rt <- visR::get_risktable(lung_suvival_object)  # Display the risktable visR::render(rt, title = \"Overview over survival rates of Lung Cancer patients\", datasource = DATASET) # Display a summary of the survival estimate visR::render(lung_suvival_object %>% visR::get_summary(), title = \"Summary\", datasource = DATASET) # Display test statistics associated with the survival estimate visR::render(lung_suvival_object %>% visR::get_pvalue(), title = \"P-values\", datasource = DATASET) # Display qunatile information of the survival estimate visR::render(lung_suvival_object %>% visR::get_quantile(), title = \"Quantile Information\", datasource = DATASET) # Display a cox model estimate associated with the survival estimate visR::render(lung_suvival_object %>% visR::get_COX_HR(), title = \"COX estimate\", datasource = DATASET) #> tidyme S3 default method (broom::tidy) used."},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"plotting-the-kaplan-meier","dir":"Articles","previous_headings":"Time-to-event analysis > Survival visualization","what":"Plotting the Kaplan-Meier","title":"Survival Analysis with visR","text":"Alternatively, survival data can plotted Kaplan-Meier curve. visR, plot cases ggplot object adapting plot follows general principle creating plot adding visual contents step--step.    visR includes wrapper function create risktable add directly Kaplan-Meier plot.","code":"# Create and display a Kaplan-Meier from the survival object gg <- visR::visr(lung_suvival_object) #> Warning: `visr.survfit()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggsurvfit()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated. gg # Add a confidence interval to the Kaplan-Meier and display the plot gg %>% visR::add_CI() # Add a confidence interval and the censor ticks to the Kaplan-Meier and display the plot gg %>% visR::add_CI() %>% visR::add_CNSR(shape = 3, size = 2) # Add a confidence interval and the censor ticks and a risktable to the Kaplan-Meier and display the plot gg %>% visR::add_CI() %>% visR::add_CNSR(shape = 3, size = 2) %>% visR::add_risktable()"},{"path":"https://openpharma.github.io/visR/articles/Time_to_event_analysis.html","id":"competing-risks","dir":"Articles","previous_headings":"","what":"Competing Risks","title":"Survival Analysis with visR","text":"addition classic right-censored data, {visR} package supports estimation time--event outcomes presence competing events. package wraps {tidycmprsk} package, exports functions cumulative incidence estimation visualization. function estimate_cuminc() estimates cumulative incidence competing event outcome interest. syntax nearly identical estimate_KM(); however, outcome status variable (passed CNSR= argument) must factor first level indicates censoring, second level competing event interest, subsequent levels competing events. Visualization functions, visr(), add_CI(), add_CNSR(), add_risktable() share syntax Kaplan-Meier variants.","code":"visR::estimate_cuminc(   tidycmprsk::trial,   strata = \"trt\",   CNSR = \"death_cr\",   AVAL = \"ttdeath\" ) %>%   visR::visr(     legend_position = \"bottom\",      x_label = \"Months from Treatment\",     y_label = \"Risk of Death\"   ) %>%   visR::add_CI() %>%   visR::add_risktable(statlist = c(\"n.risk\", \"cum.event\")) #> Warning: `visr.tidycuminc()` was deprecated in visR 0.4.0. #> ℹ Please use `ggsurvfit::ggcuminc()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-visr---a-package-for-effective-visualizations-in-pharma","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Welcome","what":"visR - A package for Effective Visualizations in Pharma","title":"visR","text":"tutorial introduce basic usage visR, R package effective visual communication. package presents easy--use interface visualizations relevant clinical development process, implementing several best practices. developed part openpharma initiative, effort open-source cross-industry collaboration.","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-further-reading","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Welcome","what":"Further reading","title":"visR","text":"graphical principles visR aims implement [graphicsprinciples.github.io] visR Documentation [openpharma.github.io/visR] visR @ GitHub [github.com/openpharma/visR] visR @ CRAN [cran.r-project.org/web/packages/visR] openpharma overview [openpharma.github.io]","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-package-architecture","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Package architecture","title":"visR","text":"visR package implements simple--use interface adhering consistent naming conventions.","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-overview","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Package architecture","what":"Overview","title":"visR","text":"principle, extimate_XXX functions allow estimate statistical models data (e.g. estimate_KM estimates survival model), get_XXX functions get kinds summary statistics (e.g. get_tableone get_pvalue), render visr display plots tables, add_XXX functions add specific information plot table (e.g. add_CI adds confidence intervals plot.)","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-outline","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Package architecture","what":"Outline","title":"visR","text":"tutorial walk basic survival analysis show-casing principles features visR. start introducing data set provided COVID-19 India Org Data Operations Group use throughout tutorial. introduce -called table 1 gives overview patient population. estimate_ survival model introduce functions get_ summary statistics. plot survival model show can add_ additional information . integrate summary statistics plots.","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-the-study-and-the-data","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"The study and the data","title":"visR","text":"Risk Survival Analysis COVID Outbreak Data :Lessons India goal paper assess mortality India due COVID-19. understand survival COVID-19 patients, time--event analysis performed based Kaplan-Meier estimates using gender age group (separately combined) strata. According authors (Bankar et al., 2021), probably biggest retrospective-cohort survival analysis conducted India, according authors. paper used publicly available data COVID-19 India Org Data Operations Group. figure roughly described trends distributions patient population.","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-the-data","dir":"Articles > Interactive_tte_tutorial","previous_headings":"The study and the data","what":"The data","title":"visR","text":"Patients admitted Karnakata, South India, recruited. final dataset consists 26,741 patients. Age gender patient, number days hospital per stay, status patient (1=censored/alive, 2=dead) age category (<18 yr, 18 yr – 60 yr, >60 yr) collected. [Publication] [GitHub] [Raw data]","code":"# First, we set the default ggplot2 theme theme_set(theme_bw())  # Then, we directly load the data from their GitHub repository data = read.csv(\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\")  data Age Sex Stay Status Age_Cat 1      46.00   M    2      2       2 2      90.00   M    7      2       3 3      72.00   F    1      2       3 4      54.00   M    0      2       2 5      51.00   F    4      2       2 6      65.00   M    0      2       3 7      56.00   M    0      2       2 8      39.00   M    1      2       2 9      66.00   F    0      2       3 10     53.00   F   10      2       2 11     62.00   M    0      2       3 12     55.00   M    2      2       2 13     70.00   M    0      2       3 14     46.00   M    1      2       2 15     60.00   F    5      2       3 16     90.00   M    0      2       3 17     45.00   M    3      2       2 18     38.00   F    1      2       2 19     70.00   M    0      2       3 20     70.00   M   13      2       3 21     47.00   F    6      2       2 22     85.00   F    5      2       3 23     81.00   M    3      2       3 24     32.00   M    0      2       2 25     67.00   M    1      2       3 26     40.00   M    1      2       2 27     85.00   F    5      2       3 28     59.00   F   14      2       2 29     54.00   F    6      2       2 30     59.00   M    3      2       2 31     73.00   M    2      2       3 32     78.00   M    1      2       3 33     55.00   M    1      2       2 34     43.00   M    0      2       2 35     28.00   M    1      2       2 36     56.00   M    1      2       2 37     53.00   M    0      2       2 38     60.00   M    3      2       3 39     70.00   F    0      2       3 40     50.00   F    0      2       2 41     68.00   M    0      2       3 42     61.00   M    1      2       3 43     68.00   F    2      2       3 44     87.00   M    0      2       3 45     80.00   F    3      2       3 46     60.00   F    0      2       3 47     38.00   M    3      2       2 48     43.00   F    7      2       2 49     42.00   M    4      2       2 50     75.00   F    2      2       3 51     57.00   M    0      2       2 52     71.00   M    0      2       3 53     55.00   M    1      2       2 54     65.00   F    0      2       3 55     45.00   M    0      2       2 56     63.00   F    3      2       3 57     66.00   F    9      2       3 58     83.00   M   16      2       3 59     65.00   M   10      2       3 60     70.00   M    8      2       3 61     72.00   M    3      2       3 62     74.00   F    5      2       3 63     95.00   F    2      2       3 64     50.00   M    1      2       2 65     75.00   M    0      2       3 66     73.00   M    2      2       3 67     65.00   M    0      2       3 68     73.00   F    0      2       3 69     71.00   F   18      2       3 70     60.00   F   12      2       3 71     66.00   M    4      2       3 72     62.00   M    7      2       3 73     55.00   M    9      2       2 74     55.00   M    6      2       2 75     48.00   F    2      2       2 76     70.00   M    1      2       3 77     76.00   F    1      2       3 78     40.00   M    0      2       2 79     65.00   M    5      2       3 80     50.00   M    1      2       2 81     57.00   M    0      2       2 82     65.00   M    1      2       3 83     31.00   M    1      2       2 84     51.00   F    2      2       2 85     67.00   M   15      2       3 86     80.00   F    1      2       3 87     60.00   M    5      2       3 88     60.00   F    4      2       3 89     59.00   M    4      2       2 90     48.00   M    1      2       2 91     60.00   M    1      2       3 92     50.00   M    1      2       2 93     70.00   M    3      2       3 94     63.00   F    0      2       3 95     40.00   M    0      2       2 96     49.00   F    1      2       2 97     60.00   F    0      2       3 98     56.00   M    2      2       2 99     31.00   M    0      2       2 100    41.00   M    1      2       2 101    56.00   M    0      2       2 102    43.00   M    0      2       2 103    42.00   F    1      2       2 104    31.00   F    1      2       2 105    75.00   F    6      2       3 106    85.00   F    7      2       3 107    83.00   M    6      2       3 108    44.00   M    6      2       2 109    63.00   M    3      2       3 110    55.00   F    2      2       2 111    45.00   M    2      2       2 112    60.00   F    6      2       3 113    63.00   F    1      2       3 114    64.00   F    0      2       3 115    50.00   M    0      2       2 116    50.00   F    1      2       2 117    52.00   M    4      2       2 118    69.00   M    0      2       3 119    32.00   M    0      2       2 120    36.00   M    0      2       2 121    56.00   M    1      2       2 122    58.00   F    2      2       2 123    58.00   F    0      2       2 124    50.00   F    1      2       2 125    50.00   M    4      2       2 126    72.00   M    0      2       3 127    72.00   M    0      2       3 128    80.00   M    3      2       3 129    63.00   M   11      2       3 130    30.00   M    1      2       2 131    66.00   F   12      2       3 132    75.00   M    0      2       3 133    43.00   M    7      2       2 134    79.00   M    8      2       3 135    76.00   M    6      2       3 136    48.00   M    4      2       2 137    75.00   F    3      2       3 138    48.00   M   13      2       2 139    74.00   M    7      2       3 140    61.00   M    1      2       3 141    50.00   M    2      2       2 142    70.00   F    2      2       3 143    65.00   M    1      2       3 144    60.00   M    0      2       3 145    36.00   M    1      2       2 146    55.00   M    0      2       2 147    45.00   M    1      2       2 148    68.00   F    5      2       3 149    31.00   M    0      2       2 150    50.00   F    8      2       2 151    58.00   M    6      2       2 152    73.00   M   14      2       3 153    56.00   M    0      2       2 154    55.00   M    0      2       2 155    63.00   M    0      2       3 156    52.00   M    6      2       2 157    54.00   M    4      2       2 158    87.00   M    3      2       3 159    60.00   M    0      2       3 160    58.00   M    0      2       2 161    65.00   F    2      2       3 162    75.00   F    5      2       3 163    73.00   F    1      2       3 164    80.00   M    8      2       3 165    25.00   F    5      2       2 166    62.00   M    1      2       3 167    70.00   F    1      2       3 168    75.00   M    1      2       3 169    46.00   F    1      2       2 170    48.00   M    0      2       2 171    80.00   F    3      2       3 172    72.00   F    1      2       3 173    66.00   M    2      2       3 174    51.00   M    0      2       2 175    84.00   M    2      2       3 176    82.00   M    6      2       3 177    29.00   M   10      2       2 178    45.00   M    0      2       2 179    57.00   M    4      2       2 180    63.00   M    1      2       3 181    68.00   M    5      2       3 182    65.00   M    4      2       3 183    66.00   F    0      2       3 184    50.00   F    1      2       2 185    52.00   F    7      2       2 186    42.00   F    3      2       2 187    60.00   F    0      2       3 188    39.00   M    0      2       2 189    56.00   M    2      2       2 190    66.00   F    3      2       3 191    53.00   M    4      2       2 192    78.00   F    3      2       3 193    62.00   M    0      2       3 194    60.00   M    1      2       3 195    68.00   F    1      2       3 196    73.00   M    1      2       3 197    47.00   M    3      2       2 198    84.00   M    1      2       3 199    32.00   M    2      2       2 200    80.00   M    0      2       3 201    60.00   F    0      2       3 202    67.00   M    0      2       3 203    65.00   F    0      2       3 204    45.00   F    2      2       2 205    75.00   M    1      2       3 206    73.00   M    1      2       3 207    55.00   M    0      2       2 208    24.00   F    0      2       2 209    70.00   F    2      2       3 210    50.00   M    0      2       2 211    70.00   F    1      2       3 212    31.00   M    1      2       2 213    57.00   M   14      2       2 214    70.00   M    4      2       3 215    55.00   F   17      2       2 216    50.00   F    9      2       2 217    64.00   M    5      2       3 218    58.00   M    0      2       2 219    73.00   M    0      2       3 220    95.00   M    2      2       3 221    48.00   M    1      2       2 222    63.00   M    3      2       3 223    64.00   F   27      2       3 224    54.00   M    8      2       2 225    48.00   M    1      2       2 226    71.00   M    8      2       3 227    62.00   M    0      2       3 228    68.00   M    3      2       3 229    58.00   F    2      2       2 230    77.00   M    1      2       3 231    69.00   M    1      2       3 232    64.00   M    0      2       3 233    50.00   F    5      2       2 234    53.00   M    1      2       2 235    48.00   M    0      2       2 236    78.00   M    0      2       3 237    67.00   F    4      2       3 238    66.00   M    0      2       3 239    70.00   M    0      2       3 240    25.00   F    2      2       2 241    45.00   M    0      2       2 242    54.00   M    2      2       2 243    63.00   M    1      2       3 244    45.00   M    1      2       2 245    60.00   F    0      2       3 246    65.00   M    6      2       3 247    32.00   F    1      2       2 248    59.00   M    0      2       2 249    60.00   M    0      2       3 250    65.00   M    0      2       3 251    44.00   M   11      2       2 252    58.00   M   12      2       2 253    54.00   M    7      2       2 254    79.00   M    4      2       3 255    62.00   M    6      2       3 256    59.00   F    7      2       2 257    59.00   M   12      2       2 258    74.00   M    7      2       3 259    68.00   M    7      2       3 260    73.00   M    3      2       3 261    52.00   M    6      2       2 262    49.00   M    4      2       2 263    60.00   F    3      2       3 264    50.00   M    3      2       2 265    64.00   M    2      2       3 266    78.00   M    1      2       3 267    60.00   M    0      2       3 268    80.00   M    0      2       3 269    90.00   F    0      2       3 270    75.00   M    0      2       3 271    70.00   M    0      2       3 272    22.00   M    1      2       2 273    65.00   F    0      2       3 274    65.00   F    1      2       3 275    40.00   M    0      2       2 276    46.00   M    2      2       2 277    75.00   M    2      2       3 278    38.00   F    0      2       2 279    58.00   M    0      2       2 280    30.00   M    0      2       2 281    80.00   M    2      2       3 282    48.00   M    2      2       2 283    68.00   M    1      2       3 284    75.00   F    2      2       3 285    47.00   M    1      2       2 286    52.00   M    0      2       2 287    55.00   F    0      2       2 288    55.00   M    1      2       2 289    71.00   M    0      2       3 290    40.00   M    2      2       2 291    68.00   F    1      2       3 292    70.00   M    0      2       3 293    70.00   F    0      2       3 294    65.00   M    1      2       3 295    90.00   F   16      2       3 296    42.00   M    4      2       2 297    73.00   M   11      2       3 298    56.00   F    6      2       2 299    37.00   M   10      2       2 300    44.00   M    6      2       2 301    34.00   M   10      2       2 302    60.00   M    7      2       3 303    70.00   M    0      2       3 304    67.00   F   12      2       3 305    60.00   M    5      2       3 306    68.00   M    2      2       3 307    65.00   M    1      2       3 308    55.00   M    8      2       2 309    58.00   F    8      2       2 310    58.00   F   11      2       2 311    70.00   M    5      2       3 312    36.00   M    6      2       2 313    14.00   M    2      2       1 314    50.00   F   10      2       2 315    67.00   M    7      2       3 316    85.00   M    8      2       3 317    65.00   M    1      2       3 318    58.00   M    0      2       2 319    61.00   F    1      2       3 320    29.00   M    4      2       2 321    57.00   M    7      2       2 322    60.00   F    6      2       3 323    35.00   M   13      2       2 324    69.00   M    3      2       3 325    47.00   M    2      2       2 326    34.00   M    5      2       2 327    65.00   M    6      2       3 328    65.00   M    5      2       3 329    65.00   M   13      2       3 330    56.00   M    0      2       2 331    60.00   F    3      2       3 332    70.00   F    8      2       3 333    40.00   M    6      2       2 334    50.00   M    1      2       2 335    80.00   M    3      2       3 336    54.00   M    1      2       2 337    52.00   F    0      2       2 338    48.00   F    1      2       2 339    58.00   F    0      2       2 340    55.00   F    0      2       2 341    48.00   F    1      2       2 342    85.00   F    0      2       3 343    62.00   M    0      2       3 344    65.00   M    1      2       3 345    54.00   F    1      2       2 346    70.00   M    1      2       3 347    65.00   M    0      2       3 348    45.00   F   12      2       2 349    55.00   M    6      2       2 350    88.00   M    2      2       3 351    42.00   M    0      2       2 352    65.00   M    5      2       3 353    77.00   M    3      2       3 354    67.00   M    4      2       3 355    50.00   F    3      2       2 356    78.00   F    0      2       3 357    76.00   F    3      2       3 358    64.00   M    0      2       3 359    52.00   M    0      2       2 360    53.00   M    1      2       2 361    73.00   M    1      2       3 362    54.00   M    3      2       2 363    85.00   M    2      2       3 364    53.00   M    4      2       2 365    59.00   F   15      2       2 366    75.00   M    9      2       3 367    32.00   F    2      2       2 368    70.00   F    9      2       3 369    51.00   M    0      2       2 370    37.00   M    6      2       2 371    71.00   M    4      2       3 372    51.00   M    0      2       2 373    65.00   M    5      2       3 374    65.00   M    2      2       3 375    52.00   M    0      2       2 376    45.00   M    0      2       2 377    90.00   M    1      2       3 378    72.00   M    8      2       3 379    48.00   F    1      2       2 380    65.00   M    0      2       3 381    56.00   M    0      2       2 382    69.00   F    0      2       3 383    31.00   F    1      2       2 384    51.00   M    4      2       2 385    63.00   M    4      2       3 386    55.00   F    0      2       2 387    55.00   M    0      2       2 388    48.00   M   10      2       2 389    45.00   M    3      2       2 390    47.00   M    9      2       2 391    70.00   M    1      2       3 392    42.00   M    7      2       2 393    72.00   M    1      2       3 394    62.00   M    0      2       3 395    68.00   F    0      2       3 396    55.00   F    3      2       2 397    44.00   F    4      2       2 398    66.00   M    6      2       3 399    83.00   M    6      2       3 400    75.00   M    7      2       3 401    62.00   M    8      2       3 402    58.00   F    2      2       2 403    52.00   M    3      2       2 404    40.00   M    3      2       2 405    57.00   M    4      2       2 406    61.00   M    5      2       3 407    61.00   M    4      2       3 408    72.00   M    2      2       3 409    71.00   F    1      2       3 410    55.00   M    1      2       2 411    42.00   M    1      2       2 412    60.00   M   33      2       3 413    35.00   M    1      2       2 414    52.00   M    0      2       2 415    50.00   F    1      2       2 416    57.00   M    1      2       2 417    65.00   F    0      2       3 418    72.00   M    1      2       3 419    49.00   M    1      2       2 420    48.00   M    0      2       2 421    82.00   M    0      2       3 422    65.00   M   14      2       3 423    30.00   M   14      2       2 424    66.00   M    0      2       3 425    58.00   M    2      2       2 426    58.00   F    0      2       2 427    68.00   M    2      2       3 428    63.00   F    3      2       3 429    68.00   M    0      2       3 430    76.00   M    8      2       3 431    62.00   M    5      2       3 432    65.00   F    2      2       3 433    60.00   F    0      2       3 434    83.00   M    1      2       3 435    59.00   M    1      2       2 436    59.00   F    1      2       2 437    58.00   M    0      2       2 438    57.00   F    0      2       2 439    67.00   M    0      2       3 440    54.00   M    4      2       2 441    70.00   F    1      2       3 442    56.00   F    1      2       2 443    49.00   F    0      2       2 444    73.00   M    0      2       3 445    88.00   M    0      2       3 446    54.00   M    1      2       2 447    61.00   M    0      2       3 448    65.00   M    0      2       3 449    79.00   M    1      2       3 450    51.00   F    0      2       2 451    73.00   F    5      2       3 452    40.00   M    8      2       2 453    55.00   F    1      2       2 454    80.00   M    6      2       3 455    51.00   M    4      2       2 456    75.00   M    6      2       3 457    78.00   F    5      2       3 458    84.00   M    6      2       3 459    59.00   M    2      2       2 460    68.00   M    5      2       3 461    73.00   M    1      2       3 462    65.00   M    2      2       3 463    67.00   M    0      2       3 464    55.00   M    2      2       2 465    51.00   F    1      2       2 466    65.00   M    3      2       3 467    58.00   F    0      2       2 468    31.00   M   14      2       2 469    72.00   M    4      2       3 470    65.00   F    0      2       3 471    55.00   M    0      2       2 472    45.00   F    6      2       2 473    58.00   F    1      2       2 474    47.00   M    1      2       2 475    65.00   M    0      2       3 476    54.00   M    1      2       2 477    58.00   M    1      2       2 478    66.00   M    2      2       3 479    55.00   M   11      2       2 480    62.00   M    0      2       3 481    12.00   F    1      2       1 482    46.00   F    0      2       2 483    26.00   F    0      2       2 484    57.00   F    1      2       2 485    62.00   F    5      2       3 486    70.00   M    4      2       3 487    60.00   F    0      2       3 488    68.00   M    1      2       3 489    50.00   M    0      2       2 490    71.00   M    0      2       3 491    58.00   M    1      2       2 492    46.00   M    6      2       2 493     0.00   F    0      2       1 494    70.00   M   11      2       3 495    65.00   M    3      2       3 496    17.00   F    8      2       1 497    45.00   F   13      2       2 498    64.00   M   11      2       3 499    61.00   M    7      2       3 500    59.00   F    2      2       2 501    63.00   M    0      2       3 502    36.00   M   14      2       2 503    60.00   M    3      2       3 504    49.00   F    2      2       2 505    60.00   M   11      2       3 506    45.00   M    3      2       2 507    71.00   F    2      2       3 508    42.00   F    6      2       2 509    84.00   M    0      2       3 510    57.00   M    1      2       2 511    60.00   M    9      2       3 512    33.00   M   23      2       2 513    75.00   F    1      2       3 514    50.00   F    1      2       2 515    60.00   F    6      2       3 516    45.00   F    7      2       2 517    70.00   F    0      2       3 518    60.00   M    9      2       3 519    69.00   M   13      2       3 520    82.00   M   10      2       3 521    68.00   M    0      2       3 522    49.00   M    1      2       2 523    56.00   M    4      2       2 524    33.00   F    2      2       2 525    80.00   F    3      2       3 526    74.00   M    1      2       3 527    65.00   M    2      2       3 528    48.00   M    0      2       2 529    52.00   F   11      2       2 530    75.00   F    0      2       3 531    26.00   M    3      2       2 532    57.00   M    5      2       2 533    62.00   M    7      2       3 534    65.00   M    3      2       3 535    71.00   M    1      2       3 536    56.00   M    1      2       2 537    76.00   F    1      2       3 538    49.00   F    0      2       2 539    57.00   M    1      2       2 540    79.00   M    3      2       3 541    59.00   F    0      2       2 542    54.00   F    0      2       2 543    65.00   F    0      2       3 544    48.00   M    1      2       2 545    70.00   F    8      2       3 546    72.00   M    6      2       3 547    78.00   M    4      2       3 548    53.00   M    2      2       2 549    45.00   F    1      2       2 550    52.00   M    3      2       2 551    50.00   M    2      2       2 552    42.00   M    1      2       2 553    68.00   M    0      2       3 554    58.00   M    3      2       2 555    47.00   M    0      2       2 556    55.00   F    1      2       2 557    60.00   F    1      2       3 558    58.00   F    0      2       2 559    42.00   F    0      2       2 560    60.00   M    1      2       3 561    80.00   F    0      2       3 562    57.00   M    2      2       2 563    45.00   F   10      2       2 564    55.00   F    3      2       2 565    61.00   F    1      2       3 566    52.00   M    4      2       2 567    57.00   M   11      2       2 568    35.00   M    0      2       2 569    36.00   F    1      2       2 570    50.00   F    3      2       2 571    55.00   F   17      2       2 572    48.00   M    0      2       2 573    52.00   M    2      2       2 574    36.00   M    0      2       2 575    48.00   M    0      2       2 576    59.00   M    1      2       2 577    46.00   F    8      2       2 578    45.00   M    0      2       2 579    72.00   F    1      2       3 580    84.00   M    2      2       3 581    29.00   M   10      2       2 582    45.00   M    0      2       2 583    57.00   M    4      2       2 584    65.00   M    3      2       3 585    50.00   F    1      2       2 586    52.00   F    7      2       2 587    42.00   F    3      2       2 588    39.00   M    0      2       2 589    40.00   M    0      2       2 590    66.00   F    3      2       3 591    78.00   F    5      2       3 592    62.00   M    0      2       3 593    55.00   F    1      2       2 594    36.00   F    1      2       2 595    80.00   F    3      2       3 596    63.00   M    2      2       3 597    75.00   F    2      2       3 598    65.00   F    9      2       3 599    48.00   F    1      2       2 600    60.00   F    0      2       3 601    90.00   F    6      2       3 602    80.00   M    6      2       3 603    77.00   M    4      2       3 604    70.00   M    4      2       3 605    62.00   M    2      2       3 606    59.00   M    0      2       2 607    65.00   M    0      2       3 608    63.00   M    1      2       3 609    79.00   M    3      2       3 610    50.00   M    0      2       2 611    65.00   M    4      2       3 612    50.00   M    0      2       2 613    63.00   M    0      2       3 614    52.00   M    0      2       2 615    70.00   M    0      2       3 616    45.00   M    4      2       2 617    20.00   M    3      2       2 618    86.00   F    0      2       3 619    85.00   F    6      2       3 620    39.00   M    6      2       2 621    74.00   M    2      2       3 622    70.00   M    0      2       3 623    51.00   M    1      2       2 624    68.00   M    0      2       3 625    76.00   M    1      2       3 626    52.00   M    2      2       2 627    72.00   M    0      2       3 628    45.00   M    2      2       2 629    45.00   M    1      2       2 630    82.00   F    0      2       3 631    58.00   F    0      2       2 632    69.00   M    2      2       3 633    68.00   M    2      2       3 634    63.00   M    0      2       3 635    38.00   M    2      2       2 636    45.00   F   16      2       2 637    59.00   M    1      2       2 638    47.00   M    0      2       2 639    40.00   M    1      2       2 640    29.00   F    0      2       2 641    50.00   F    0      2       2 642    59.00   M    2      2       2 643    74.00   M    1      2       3 644    58.00   F    1      2       2 645    47.00   M    1      2       2 646    67.00   M    1      2       3 647    73.00   M    2      2       3 648    42.00   F    2      2       2 649    23.00   F    2      2       2 650    55.00   M    0      2       2 651    35.00   M    1      2       2 652    55.00   M    6      2       2 653    64.00   M    7      2       3 654    76.00   F    0      2       3 655    84.00   M    1      2       3 656    54.00   M    3      2       2 657    48.00   M    0      2       2 658    60.00   M    5      2       3 659    60.00   F    0      2       3 660    75.00   F    0      2       3 661    68.00   M    1      2       3 662    53.00   M    2      2       2 663    85.00   M    0      2       3 664    60.00   M    3      2       3 665    55.00   F    0      2       2 666    67.00   F    0      2       3 667    49.00   F    0      2       2 668    68.00   F    2      2       3 669    80.00   F    1      2       3 670    70.00   M    1      2       3 671    35.00   F    1      2       2 672    40.00   F    2      2       2 673    70.00   F    0      2       3 674    49.00   M    0      2       2 675    48.00   M    0      2       2 676    48.00   F    1      2       2 677    42.00   F    3      2       2 678    75.00   F    0      2       3 679    72.00   M    2      2       3 680    52.00   M    4      2       2 681    60.00   M    6      2       3 682    61.00   M    5      2       3 683    50.00   M    0      2       2 684    70.00   F    2      2       3 685    49.00   M    0      2       2 686    38.00   M    0      2       2 687    34.00   F    1      2       2 688    50.00   M    1      2       2 689    41.00   M    1      2       2 690    76.00   M    1      2       3 691    40.00   M    0      2       2 692    55.00   M    1      2       2 693    57.00   M    2      2       2 694    53.00   M    7      2       2 695    61.00   M    0      2       3 696    60.00   M    0      2       3 697    90.00   M    3      2       3 698    41.00   M    1      2       2 699    68.00   M    6      2       3 700    40.00   F    0      2       2 701    50.00   F    2      2       2 702    70.00   M    2      2       3 703    75.00   F    3      2       3 704    70.00   M   22      2       3 705    49.00   F    4      2       2 706    70.00   M    0      2       3 707    56.00   F    2      2       2 708    56.00   M    1      2       2 709    52.00   M    2      2       2 710    76.00   F    0      2       3 711    51.00   F    2      2       2 712    65.00   M    0      2       3 713    85.00   F    3      2       3 714    48.00   M    3      2       2 715    68.00   F    0      2       3 716    60.00   M    0      2       3 717    63.00   F    1      2       3 718    60.00   M    0      2       3 719    71.00   M    0      2       3 720    69.00   M    0      2       3 721    70.00   F    1      2       3 722    58.00   M    0      2       2 723    46.00   M    9      2       2 724    78.00   M    4      2       3 725    56.00   M    4      2       2 726    60.00   F   15      2       3 727    41.00   M    1      2       2 728    66.00   M    6      2       3 729    60.00   F    0      2       3 730    70.00   M   11      2       3 731    52.00   F   13      2       2 732    48.00   M    6      2       2 733    85.00   F    5      2       3 734    55.00   F    9      2       2 735    77.00   M    6      2       3 736    78.00   M    9      2       3 737    51.00   M    6      2       2 738    65.00   M    0      2       3 739    48.00   F    0      2       2 740    62.00   F    1      2       3 741    40.00   M    2      2       2 742    78.00   M    2      2       3 743    61.00   M    1      2       3 744    62.00   M    0      2       3 745    51.00   F    0      2       2 746    26.00   M    0      2       2 747    51.00   M    0      2       2 748    60.00   M    1      2       3 749    81.00   F    0      2       3 750    60.00   M    0      2       3 751    55.00   F    0      2       2 752    63.00   M    3      2       3 753    28.00   M    0      2       2 754    78.00   M    0      2       3 755    60.00   F    1      2       3 756    65.00   M    2      2       3 757    14.00   M    4      2       1 758    70.00   M    6      2       3 759    70.00   F    1      2       3 760    46.00   M    2      2       2 761    47.00   M    1      2       2 762    41.00   M    0      2       2 763    72.00   M    2      2       3 764    65.00   M    0      2       3 765    76.00   M    0      2       3 766    50.00   M    4      2       2 767    80.00   F    2      2       3 768    84.00   M    0      2       3 769    68.00   F    1      2       3 770    54.00   M    0      2       2 771    39.00   M    0      2       2 772    75.00   M    3      2       3 773    48.00   F    1      2       2 774    41.00   M    3      2       2 775    61.00   M    6      2       3 776    27.00   F    0      2       2 777    55.00   F    4      2       2 778    75.00   F    0      2       3 779    56.00   F    0      2       2 780    59.00   M    0      2       2 781    63.00   M    0      2       3 782    45.00   F    1      2       2 783    54.00   F    9      2       2 784    47.00   M    1      2       2 785    38.00   M    0      2       2 786    60.00   M    0      2       3 787    43.00   M    1      2       2 788    62.00   F    0      2       3 789    72.00   F    0      2       3 790    50.00   F    0      2       2 791    38.00   M    0      2       2 792    45.00   M    0      2       2 793    65.00   M    4      2       3 794    49.00   M    0      2       2 795    52.00   F    0      2       2 796    52.00   M    0      2       2 797    75.00   M    1      2       3 798    74.00   F    1      2       3 799    53.00   M    1      2       2 800    38.00   F    0      2       2 801    75.00   F    0      2       3 802    55.00   F    0      2       2 803    46.00   F    1      2       2 804    60.00   M    3      2       3 805    58.00   F    1      2       2 806    68.00   M    1      2       3 807    51.00   M    4      2       2 808    61.00   F    0      2       3 809    45.00   M    0      2       2 810    59.00   M    0      2       2 811    41.00   M    0      2       2 812    56.00   M    0      2       2 813    83.00   F    1      2       3 814    55.00   F    0      2       2 815    66.00   M   11      2       3 816    56.00   M    2      2       2 817    55.00   M    4      2       2 818    55.00   M    1      2       2 819    45.00   M    0      2       2 820    67.00   M    1      2       3 821    27.00   M    0      2       2 822    57.00   M    0      2       2 823    20.00   M    1      2       2 824    61.00   M    0      2       3 825    70.00   M    0      2       3 826    55.00   M    1      2       2 827    56.00   F    1      2       2 828    65.00   M    0      2       3 829    69.00   M    0      2       3 830    50.00   F    1      2       2 831    55.00   M    1      2       2 832    68.00   M    5      2       3 833    58.00   M    2      2       2 834    40.00   F    3      2       2 835    75.00   F    1      2       3 836    58.00   M    2      2       2 837    41.00   M    0      2       2 838    68.00   M    1      2       3 839    70.00   M    0      2       3 840    45.00   M    1      2       2 841    51.00   M    5      2       2 842    72.00   F    1      2       3 843    38.00   M    2      2       2 844    56.00   M    0      2       2 845    34.00   M    0      2       2 846    81.00   M    0      2       3 847    70.00   M    3      2       3 848    45.00   M    0      2       2 849    54.00   M    0      2       2 850    66.00   F    2      2       3 851    57.00   M    0      2       2 852    65.00   M    7      2       3 853    35.00   M    1      2       2 854    50.00   F    0      2       2 855    54.00   M    0      2       2 856    59.00   M    1      2       2 857    82.00   M    3      2       3 858    52.00   M    0      2       2 859    53.00   M    0      2       2 860    60.00   M    0      2       3 861    61.00   M    0      2       3 862    23.00   F    0      2       2 863    37.00   M    0      2       2 864    73.00   F    0      2       3 865    71.00   M    0      2       3 866    68.00   M    0      2       3 867    45.00   M    0      2       2 868    58.00   F    0      2       2 869    32.00   F    1      2       2 870    61.00   M    5      2       3 871    60.00   M    0      2       3 872    38.00   M    3      2       2 873    37.00   M    0      2       2 874    53.00   M    1      2       2 875    65.00   F    1      2       3 876    68.00   F   11      2       3 877    48.00   M    0      2       2 878    63.00   M    0      2       3 879    73.00   M    9      2       3 880    65.00   M    3      2       3 881    66.00   M    5      2       3 882    35.00   M    0      2       2 883    70.00   M    5      2       3 884    65.00   M    4      2       3 885    37.00   F    1      2       2 886    75.00   M    3      2       3 887    65.00   F    3      2       3 888    55.00   M    0      2       2 889    65.00   F    2      2       3 890    48.00   F    3      2       2 891    70.00   M    2      2       3 892    28.00   M    2      2       2 893    56.00   M    1      2       2 894    31.00   M   34      2       2 895    72.00   F    2      2       3 896    43.00   F    5      2       2 897    30.00   M    0      2       2 898    92.00   F    2      2       3 899    66.00   M    5      2       3 900    55.00   F    1      2       2 901    69.00   F    3      2       3 902    58.00   F    3      2       2 903    60.00   F    6      2       3 904    66.00   M    0      2       3 905    55.00   M    2      2       2 906    60.00   M    1      2       3 907    60.00   F    0      2       3 908    65.00   F    0      2       3 909    53.00   M    0      2       2 910    49.00   M    3      2       2 911    45.00   M    0      2       2 912    73.00   M    0      2       3 913    45.00   M    2      2       2 914    50.00   M   13      2       2 915    58.00   M    0      2       2 916    58.00   M    0      2       2 917    40.00   M    0      2       2 918    65.00   M    1      2       3 919    63.00   M   12      2       3 920    61.00   M    0      2       3 921    42.00   M    0      2       2 922    55.00   M    1      2       2 923    55.00   M    0      2       2 924    67.00   M    1      2       3 925    64.00   M    1      2       3 926    50.00   M    0      2       2 927    45.00   M    0      2       2 928    53.00   F    2      2       2 929    50.00   F    2      2       2 930    75.00   M    0      2       3 931    59.00   M    0      2       2 932    51.00   F    4      2       2 933    64.00   M    4      2       3 934    48.00   M    0      2       2 935    37.00   M    0      2       2 936    60.00   M    2      2       3 937    70.00   F   11      2       3 938    62.00   F    3      2       3 939    51.00   M    1      2       2 940    50.00   F    2      2       2 941    73.00   M    3      2       3 942    35.00   F    0      2       2 943    76.00   M    2      2       3 944    40.00   M    1      2       2 945    53.00   M    4      2       2 946    46.00   M   11      2       2 947    61.00   M    4      2       3 948    65.00   M    0      2       3 949    60.00   M    0      2       3 950    55.00   M    0      2       2 951    53.00   M    6      2       2 952    38.00   F    2      2       2 953    84.00   M   14      2       3 954    46.00   M    7      2       2 955    60.00   M    9      2       3 956    85.00   M    5      2       3 957    80.00   M    8      2       3 958    48.00   M    5      2       2 959    38.00   M    0      2       2 960    68.00   F    6      2       3 961    62.00   M    5      2       3 962    75.00   M    6      2       3 963    80.00   F    2      2       3 964    78.00   M    1      2       3 965    61.00   M    0      2       3 966    49.00   M    3      2       2 967    60.00   M    1      2       3 968    65.00   M    0      2       3 969    65.00   M    1      2       3 970    60.00   M    6      2       3 971    52.00   M    7      2       2 972    48.00   M    0      2       2 973    24.00   M    1      2       2 974    50.00   F    0      2       2 975    22.00   M    0      2       2 976    58.00   M    0      2       2 977    60.00   F    2      2       3 978    58.00   M    0      2       2 979    54.00   M    1      2       2 980    78.00   M    0      2       3 981    38.00   F    0      2       2 982    54.00   M    0      2       2 983    65.00   F    0      2       3 984    87.00   F    0      2       3 985    75.00   F    0      2       3 986    60.00   F    0      2       3 987    23.00   F    1      2       2 988    85.00   M    0      2       3 989    69.00   M    4      2       3 990    72.00   M   13      2       3 991    85.00   M    2      2       3 992    57.00   F    1      2       2 993    78.00   M    0      2       3 994    68.00   M    0      2       3 995    63.00   M    2      2       3 996    67.00   M    7      2       3 997    64.00   M    0      2       3 998    77.00   M    0      2       3 999    53.00   M    2      2       2 1000   66.00   M    2      2       3 1001   82.00   M    3      2       3 1002   63.00   M    0      2       3 1003   40.00   M    0      2       2 1004   55.00   F    0      2       2 1005   67.00   M    0      2       3 1006   49.00   M    0      2       2 1007   59.00   F    0      2       2 1008   75.00   F    1      2       3 1009   68.00   M    1      2       3 1010   72.00   M    0      2       3 1011   77.00   M    0      2       3 1012   47.00   M    1      2       2 1013   60.00   M    0      2       3 1014   72.00   M    2      2       3 1015   55.00   M    6      2       2 1016   43.00   F    1      2       2 1017   62.00   M    0      2       3 1018   55.00   M    4      2       2 1019   60.00   M    3      2       3 1020   45.00   F    3      2       2 1021   70.00   M    3      2       3 1022   90.00   F    0      2       3 1023   49.00   M    1      2       2 1024   55.00   F    2      2       2 1025   52.00   F    1      2       2 1026   63.00   M    1      2       3 1027   47.00   M    1      2       2 1028   69.00   M    1      2       3 1029   60.00   F    1      2       3 1030   60.00   M    0      2       3 1031   82.00   M    0      2       3 1032   61.00   M    2      2       3 1033   65.00   M    2      2       3 1034   65.00   M    0      2       3 1035   59.00   M   11      2       2 1036   26.00   M   10      2       2 1037   65.00   M    7      2       3 1038   69.00   M   13      2       3 1039   55.00   F    0      2       2 1040   79.00   M    3      2       3 1041   75.00   F    5      2       3 1042   51.00   M    6      2       2 1043   22.00   M    0      2       2 1044   50.00   M    0      2       2 1045   60.00   M    3      2       3 1046   67.00   F    1      2       3 1047   69.00   M    0      2       3 1048   55.00   F    0      2       2 1049   74.00   M    3      2       3 1050   65.00   M    0      2       3 1051   60.00   M    1      2       3 1052   65.00   M    0      2       3 1053   60.00   M    4      2       3 1054   55.00   M    3      2       2 1055   65.00   M    0      2       3 1056   48.00   M    0      2       2 1057   60.00   F    0      2       3 1058   38.00   M    0      2       2 1059   76.00   F    2      2       3 1060   62.00   F    5      2       3 1061   62.00   F    2      2       3 1062   63.00   M    0      2       3 1063   59.00   M    2      2       2 1064   52.00   M    2      2       2 1065   72.00   M    0      2       3 1066   58.00   M    1      2       2 1067   77.00   M    1      2       3 1068   59.00   M    1      2       2 1069   61.00   M    1      2       3 1070   61.00   F    1      2       3 1071   51.00   M   12      2       2 1072   47.00   F    0      2       2 1073   20.00   F    0      2       2 1074   53.00   M    3      2       2 1075   50.00   M    0      2       2 1076   70.00   M    3      2       3 1077   51.00   M    0      2       2 1078   73.00   M    0      2       3 1079   75.00   M    1      2       3 1080   86.00   F    2      2       3 1081   60.00   M    0      2       3 1082   73.00   F    1      2       3 1083   62.00   M    8      2       3 1084   48.00   F    3      2       2 1085   74.00   M    0      2       3 1086   45.00   M    0      2       2 1087   55.00   F    2      2       2 1088   35.00   F    2      2       2 1089   50.00   F    0      2       2 1090   44.00   M    0      2       2 1091   72.00   M    2      2       3 1092   47.00   M    7      2       2 1093   55.00   M    0      2       2 1094   47.00   F    1      2       2 1095   65.00   M    2      2       3 1096   72.00   M    1      2       3 1097   52.00   M    4      2       2 1098   45.00   F    1      2       2 1099   43.00   M    7      2       2 1100   62.00   M    3      2       3 1101   30.00   F    2      2       2 1102   66.00   F    2      2       3 1103   40.00   F    6      2       2 1104   20.00   F   14      2       2 1105   62.00   M    1      2       3 1106   70.00   M    2      2       3 1107   70.00   F    1      2       3 1108   39.00   M    4      2       2 1109   82.00   M    4      2       3 1110   48.00   F    1      2       2 1111   43.00   M    1      2       2 1112   60.00   F    1      2       3 1113   45.00   F    4      2       2 1114   58.00   F    0      2       2 1115   72.00   M    0      2       3 1116   70.00   F    1      2       3 1117   70.00   F    0      2       3 1118   45.00   F    1      2       2 1119   65.00   M    0      2       3 1120   23.00   F    4      2       2 1121   58.00   M    1      2       2 1122   67.00   M    9      2       3 1123   80.00   F    9      2       3 1124   77.00   M    4      2       3 1125   54.00   M    5      2       2 1126   82.00   M    3      2       3 1127   59.00   M    0      2       2 1128   82.00   M    1      2       3 1129   57.00   M    0      2       2 1130   55.00   F    0      2       2 1131   69.00   F    3      2       3 1132   65.00   M    0      2       3 1133   61.00   M    0      2       3 1134   69.00   M    0      2       3 1135   73.00   F    0      2       3 1136   72.00   M   10      2       3 1137   68.00   F    6      2       3 1138   57.00   M   16      2       2 1139   55.00   M   13      2       2 1140   71.00   M    4      2       3 1141   55.00   M   16      2       2 1142   48.00   M    4      2       2 1143   55.00   M    0      2       2 1144   39.00   M    0      2       2 1145   44.00   M    3      2       2 1146   68.00   F    0      2       3 1147   59.00   M    2      2       2 1148   57.00   F    7      2       2 1149   40.00   M    0      2       2 1150   57.00   M    5      2       2 1151   55.00   M    5      2       2 1152   59.00   M    4      2       2 1153   52.00   M    1      2       2 1154   75.00   F    5      2       3 1155   53.00   M    5      2       2 1156   56.00   F    4      2       2 1157   61.00   M   12      2       3 1158   84.00   M    1      2       3 1159   87.00   F    2      2       3 1160   48.00   F    4      2       2 1161   39.00   M    1      2       2 1162   48.00   M    1      2       2 1163   41.00   M    4      2       2 1164   62.00   M    5      2       3 1165   67.00   M    0      2       3 1166   53.00   F    4      2       2 1167   62.00   M    1      2       3 1168   60.00   M    0      2       3 1169   35.00   M    0      2       2 1170   67.00   M   13      2       3 1171   74.00   M    3      2       3 1172   60.00   M    2      2       3 1173   54.00   M    1      2       2 1174   51.00   F    1      2       2 1175   60.00   F    0      2       3 1176   53.00   M    0      2       2 1177   57.00   F    0      2       2 1178   86.00   F   16      2       3 1179   85.00   M    3      2       3 1180   54.00   F    1      2       2 1181   43.00   M    0      2       2 1182   54.00   M    2      2       2 1183   52.00   F    4      2       2 1184   50.00   F    0      2       2 1185   60.00   F    2      2       3 1186   67.00   M    3      2       3 1187   73.00   M    3      2       3 1188   70.00   F    1      2       3 1189   90.00   M    1      2       3 1190   52.00   F    0      2       2 1191   72.00   M    2      2       3 1192   62.00   M    1      2       3 1193   25.00   M    1      2       2 1194   52.00   M    0      2       2 1195   55.00   F    3      2       2 1196   60.00   M    4      2       3 1197   60.00   F    1      2       3 1198   45.00   M    4      2       2 1199   48.00   M    0      2       2 1200   78.00   M    0      2       3 1201   47.00   F    0      2       2 1202   40.00   M    2      2       2 1203   50.00   F    0      2       2 1204   60.00   M    0      2       3 1205   65.00   F    6      2       3 1206   52.00   M    6      2       2 1207   60.00   M    0      2       3 1208   70.00   F    9      2       3 1209   73.00   M    1      2       3 1210   45.00   M    0      2       2 1211   61.00   M    0      2       3 1212   65.00   F    1      2       3 1213   80.00   F    6      2       3 1214   79.00   M    8      2       3 1215   81.00   F    0      2       3 1216   80.00   F    0      2       3 1217   57.00   F    4      2       2 1218   65.00   M   11      2       3 1219   77.00   F    9      2       3 1220   40.00   M    3      2       2 1221   79.00   M    4      2       3 1222   61.00   F   14      2       3 1223   43.00   M    1      2       2 1224   50.00   F    3      2       2 1225   78.00   M    1      2       3 1226   78.00   M    6      2       3 1227   79.00   F    5      2       3 1228   65.00   M    1      2       3 1229   65.00   M    1      2       3 1230   54.00   M    0      2       2 1231   48.00   M    3      2       2 1232   53.00   M    4      2       2 1233   38.00   F    3      2       2 1234   70.00   M    0      2       3 1235   35.00   F    2      2       2 1236   49.00   F    2      2       2 1237   62.00   M    2      2       3 1238   62.00   M    1      2       3 1239   51.00   M    2      2       2 1240   21.00   F    0      2       2 1241   40.00   M    0      2       2 1242   64.00   M    0      2       3 1243   54.00   M    0      2       2 1244   22.00   F    0      2       2 1245   57.00   F    0      2       2 1246   52.00   M    0      2       2 1247   34.00   F    0      2       2 1248   70.00   F    4      2       3 1249   56.00   F    0      2       2 1250   72.00   M    1      2       3 1251   45.00   M    1      2       2 1252   60.00   F    0      2       3 1253   58.00   M    1      2       2 1254   67.00   F    0      2       3 1255   55.00   F    0      2       2 1256   72.00   M    1      2       3 1257   64.00   F    9      2       3 1258   58.00   M    3      2       2 1259   47.00   M    4      2       2 1260   56.00   M    0      2       2 1261   45.00   F    0      2       2 1262   52.00   M    0      2       2 1263   60.00   M    0      2       3 1264   52.00   F    0      2       2 1265   52.00   M    0      2       2 1266   58.00   F    1      2       2 1267   70.00   M    1      2       3 1268   70.00   M    0      2       3 1269   59.00   F    0      2       2 1270   64.00   M    1      2       3 1271   62.00   M   10      2       3 1272   58.00   M    1      2       2 1273   78.00   F    1      2       3 1274   75.00   F    0      2       3 1275   42.00   M    2      2       2 1276   68.00   M    0      2       3 1277   56.00   F    0      2       2 1278   42.00   M    0      2       2 1279   44.00   F    1      2       2 1280   52.00   M    0      2       2 1281   63.00   M    0      2       3 1282   70.00   M    2      2       3 1283   70.00   F    0      2       3 1284   65.00   M    2      2       3 1285   37.00   M    0      2       2 1286   51.00   M    0      2       2 1287   63.00   M    1      2       3 1288   39.00   F    0      2       2 1289   50.00   F    1      2       2 1290   48.00   M    4      2       2 1291   45.00   M   12      2       2 1292   58.00   M   12      2       2 1293   48.00   M    1      2       2 1294   65.00   M   12      2       3 1295   86.00   M    8      2       3 1296   70.00   F    2      2       3 1297   38.00   F    3      2       2 1298   50.00   M    5      2       2 1299   48.00   F    2      2       2 1300   70.00   F    2      2       3 1301   55.00   M    4      2       2 1302   76.00   M    1      2       3 1303   66.00   M    0      2       3 1304   51.00   F    0      2       2 1305   50.00   M    0      2       2 1306   60.00   M    1      2       3 1307   59.00   F    4      2       2 1308   55.00   M    2      2       2 1309   30.00   F    2      2       2 1310    0.00   M    1      2       1 1311   68.00   F    3      2       3 1312   65.00   M    2      2       3 1313   34.00   M    4      2       2 1314   48.00   M    1      2       2 1315   81.00   M    3      2       3 1316   30.00   F    1      2       2 1317   42.00   F    0      2       2 1318   70.00   F    0      2       3 1319   52.00   F    0      2       2 1320   88.00   F    1      2       3 1321   70.00   F    1      2       3 1322   40.00   M    0      2       2 1323   70.00   M    0      2       3 1324   75.00   M    3      2       3 1325   68.00   M    0      2       3 1326   91.00   F    2      2       3 1327   51.00   M    0      2       2 1328   68.00   M    3      2       3 1329   71.00   M    1      2       3 1330   49.00   M    0      2       2 1331   65.00   M    5      2       3 1332   53.00   F    4      2       2 1333   69.00   M    0      2       3 1334   68.00   M    0      2       3 1335   58.00   M    0      2       2 1336   62.00   M    0      2       3 1337   53.00   M    0      2       2 1338   50.00   M    0      2       2 1339   40.00   M    1      2       2 1340   70.00   F    0      2       3 1341   29.00   M    0      2       2 1342   62.00   M    1      2       3 1343   65.00   F    0      2       3 1344   60.00   F    0      2       3 1345   35.00   M    0      2       2 1346   70.00   M   11      2       3 1347   78.00   F   10      2       3 1348   68.00   M    6      2       3 1349   79.00   F   13      2       3 1350   68.00   M    9      2       3 1351   59.00   M   10      2       2 1352   74.00   M    9      2       3 1353   58.00   F   12      2       2 1354   67.00   M    8      2       3 1355   68.00   M    1      2       3 1356   65.00   M    1      2       3 1357   70.00   M    1      2       3 1358   69.00   M    4      2       3 1359   57.00   F    5      2       2 1360   75.00   M    5      2       3 1361   82.00   M    2      2       3 1362   48.00   F    0      2       2 1363   58.00   M    2      2       2 1364   56.00   F    2      2       2 1365   54.00   M    1      2       2 1366   46.00   M    0      2       2 1367   36.00   M    1      2       2 1368   76.00   M    2      2       3 1369   76.00   F    2      2       3 1370   47.00   M    1      2       2 1371   80.00   M    0      2       3 1372   55.00   M    0      2       2 1373   65.00   F    2      2       3 1374   48.00   F    0      2       2 1375   87.00   M    4      2       3 1376   85.00   M    0      2       3 1377   58.00   F    0      2       2 1378   57.00   M    0      2       2 1379   30.00   F    0      2       2 1380   58.00   F    3      2       2 1381   28.00   M    0      2       2 1382   50.00   M    1      2       2 1383   62.00   M    0      2       3 1384   69.00   M    0      2       3 1385   57.00   M    0      2       2 1386   53.00   M    0      2       2 1387   74.00   M    0      2       3 1388   65.00   M   24      2       3 1389   55.00   F    1      2       2 1390   74.00   F    0      2       3 1391   28.00   F    1      2       2 1392   55.00   M    0      2       2 1393   54.00   M    0      2       2 1394   52.00   M    0      2       2 1395   72.00   M    0      2       3 1396   20.00   F    0      2       2 1397   80.00   M    0      2       3 1398   42.00   F    0      2       2 1399   60.00   M    0      2       3 1400   65.00   M    0      2       3 1401   70.00   F    0      2       3 1402   45.00   F    2      2       2 1403   56.00   M    1      2       2 1404   47.00   M    7      2       2 1405   45.00   M    0      2       2 1406   52.00   M    0      2       2 1407   30.00   M    0      2       2 1408   49.00   F    0      2       2 1409   54.00   F    2      2       2 1410   53.00   M    5      2       2 1411   53.00   M   13      2       2 1412   49.00   M    0      2       2 1413   45.00   F    0      2       2 1414   70.00   M    0      2       3 1415   54.00   M    6      2       2 1416   47.00   M    1      2       2 1417   68.00   M    0      2       3 1418   71.00   M    0      2       3 1419   57.00   M    1      2       2 1420   68.00   M    2      2       3 1421   65.00   M    1      2       3 1422   59.00   F    0      2       2 1423   62.00   M    7      2       3 1424   50.00   M    3      2       2 1425   40.00   M    2      2       2 1426   45.00   M    2      2       2 1427   65.00   F    6      2       3 1428   65.00   M    3      2       3 1429   71.00   M    1      2       3 1430   68.00   F    3      2       3 1431   74.00   M    3      2       3 1432   68.00   M    6      2       3 1433   69.00   M    5      2       3 1434   58.00   F    5      2       2 1435   54.00   M    7      2       2 1436   55.00   M    7      2       2 1437   39.00   F    3      2       2 1438   60.00   M    1      2       3 1439   47.00   M    0      2       2 1440   70.00   M    2      2       3 1441   75.00   M    2      2       3 1442   55.00   M    2      2       2 1443   63.00   M    0      2       3 1444   61.00   M    0      2       3 1445   84.00   F    4      2       3 1446   61.00   M    0      2       3 1447   48.00   M    5      2       2 1448   55.00   F    8      2       2 1449   52.00   F    0      2       2 1450   49.00   M    3      2       2 1451   68.00   F    5      2       3 1452   55.00   M    6      2       2 1453   50.00   F    5      2       2 1454   62.00   M    8      2       3 1455   71.00   F    4      2       3 1456   60.00   M    0      2       3 1457   60.00   M    1      2       3 1458   52.00   M    3      2       2 1459   55.00   F    7      2       2 1460   71.00   M    6      2       3 1461   61.00   M    0      2       3 1462   78.00   M    5      2       3 1463   62.00   M    1      2       3 1464   55.00   M    4      2       2 1465   51.00   F    3      2       2 1466   94.00   F    1      2       3 1467   75.00   F    2      2       3 1468   52.00   F    1      2       2 1469   61.00   M    1      2       3 1470   70.00   M   13      2       3 1471   60.00   F    0      2       3 1472   71.00   M    0      2       3 1473   54.00   M    0      2       2 1474   35.00   F    3      2       2 1475   50.00   F    0      2       2 1476   68.00   M    0      2       3 1477   36.00   F    0      2       2 1478   28.00   M    1      2       2 1479   42.00   M    0      2       2 1480   65.00   F    0      2       3 1481   55.00   M    5      2       2 1482   70.00   M    0      2       3 1483   64.00   M    1      2       3 1484   46.00   F    0      2       2 1485   70.00   M    0      2       3 1486   48.00   M    1      2       2 1487   90.00   M    0      2       3 1488   54.00   M    1      2       2 1489   72.00   F    0      2       3 1490   60.00   M    0      2       3 1491   70.00   F    1      2       3 1492   42.00   M    2      2       2 1493   58.00   M    2      2       2 1494   72.00   M    0      2       3 1495   65.00   M   16      2       3 1496   70.00   F    2      2       3 1497   70.00   M    2      2       3 1498   61.00   M   11      2       3 1499   85.00   F   13      2       3 1500   65.00   M   10      2       3 1501   51.00   F    8      2       2 1502   65.00   M    0      2       3 1503   59.00   M    0      2       2 1504   58.00   F   11      2       2 1505   64.00   M    3      2       3 1506   65.00   F    9      2       3 1507   61.00   M    0      2       3 1508   66.00   F    0      2       3 1509   59.00   M    2      2       2 1510   65.00   M    8      2       3 1511   58.00   M    7      2       2 1512   46.00   F   10      2       2 1513   61.00   M    2      2       3 1514   67.00   M   10      2       3 1515   47.00   M    6      2       2 1516   63.00   F    6      2       3 1517   47.00   M    0      2       2 1518   69.00   M    1      2       3 1519   67.00   M    3      2       3 1520   57.00   M    4      2       2 1521   69.00   F    0      2       3 1522   61.00   M   12      2       3 1523   73.00   M    4      2       3 1524   37.00   F    1      2       2 1525   59.00   M    6      2       2 1526   63.00   M    7      2       3 1527   60.00   F    6      2       3 1528   40.00   F    7      2       2 1529   52.00   M    4      2       2 1530   40.00   M    0      2       2 1531   70.00   M    1      2       3 1532   55.00   M    0      2       2 1533   56.00   F    0      2       2 1534   80.00   M    0      2       3 1535   67.00   M    2      2       3 1536   83.00   F    4      2       3 1537   64.00   M    1      2       3 1538   59.00   F    1      2       2 1539   68.00   M    0      2       3 1540   51.00   M    0      2       2 1541   40.00   F    2      2       2 1542   64.00   F    0      2       3 1543   58.00   M    9      2       2 1544   70.00   M    1      2       3 1545   52.00   M    3      2       2 1546   44.00   M    3      2       2 1547   56.00   M    5      2       2 1548   83.00   M    8      2       3 1549   54.00   F    0      2       2 1550   55.00   F    1      2       2 1551   26.00   F    4      2       2 1552   58.00   M    4      2       2 1553   40.00   M    0      2       2 1554   50.00   M    0      2       2 1555   49.00   M    1      2       2 1556   56.00   M    0      2       2 1557   54.00   M    1      2       2 1558   57.00   M    0      2       2 1559   43.00   F    0      2       2 1560   61.00   M    1      2       3 1561   70.00   M    0      2       3 1562   21.00   M    2      2       2 1563   55.00   M    2      2       2 1564   79.00   F    2      2       3 1565   40.00   M    0      2       2 1566   40.00   M    3      2       2 1567   66.00   M    2      2       3 1568   85.00   M    0      2       3 1569   55.00   M    0      2       2 1570   59.00   F    0      2       2 1571   60.00   F    5      2       3 1572   42.00   M    3      2       2 1573   23.00   M    1      2       2 1574   83.00   M    0      2       3 1575   67.00   M    0      2       3 1576   64.00   M    2      2       3 1577   58.00   F    0      2       2 1578   60.00   M    1      2       3 1579   63.00   M    0      2       3 1580   55.00   M    3      2       2 1581   65.00   M    0      2       3 1582   58.00   M    0      2       2 1583   59.00   M    0      2       2 1584   71.00   F    1      2       3 1585   60.00   M    0      2       3 1586   74.00   M    0      2       3 1587   67.00   M    0      2       3 1588   75.00   M    1      2       3 1589   52.00   F    1      2       2 1590   45.00   M    1      2       2 1591   63.00   M    0      2       3 1592   65.00   M    0      2       3 1593   48.00   M    0      2       2 1594   59.00   M    0      2       2 1595   26.00   F    1      2       2 1596   54.00   M    2      2       2 1597   21.00   M    0      2       2 1598   59.00   M    1      2       2 1599   54.00   M    9      2       2 1600   65.00   F    0      2       3 1601   80.00   F    0      2       3 1602   68.00   M    3      2       3 1603   65.00   M    0      2       3 1604   65.00   F    0      2       3 1605   67.00   M    1      2       3 1606   52.00   F    4      2       2 1607   32.00   M    3      2       2 1608   65.00   F    0      2       3 1609   71.00   M    0      2       3 1610   59.00   F    0      2       2 1611   58.00   M    8      2       2 1612   83.00   M    9      2       3 1613   47.00   F    0      2       2 1614   80.00   F    4      2       3 1615   80.00   M    9      2       3 1616   76.00   F    5      2       3 1617   71.00   F    0      2       3 1618   66.00   M    4      2       3 1619   60.00   M    3      2       3 1620   65.00   M    7      2       3 1621   47.00   M    1      2       2 1622   60.00   M    7      2       3 1623   65.00   M    0      2       3 1624   72.00   M    6      2       3 1625   40.00   M    7      2       2 1626   75.00   M    0      2       3 1627   55.00   F    6      2       2 1628   64.00   F    5      2       3 1629   66.00   F    2      2       3 1630   40.00   M    1      2       2 1631   48.00   M    2      2       2 1632   59.00   M    8      2       2 1633   54.00   F    5      2       2 1634   41.00   M   10      2       2 1635   52.00   M    1      2       2 1636   50.00   F    0      2       2 1637   25.00   F    1      2       2 1638   42.00   M    0      2       2 1639   60.00   M    0      2       3 1640   67.00   M   30      2       3 1641   68.00   M    3      2       3 1642   72.00   M    6      2       3 1643   74.00   M    1      2       3 1644   76.00   M    2      2       3 1645   56.00   M    4      2       2 1646   37.00   M    0      2       2 1647   73.00   M    1      2       3 1648   63.00   M    1      2       3 1649   28.00   F    1      2       2 1650   49.00   F    1      2       2 1651   65.00   F    0      2       3 1652   70.00   F    0      2       3 1653   75.00   M    1      2       3 1654   81.00   M    2      2       3 1655   46.00   F    0      2       2 1656   65.00   M    0      2       3 1657   50.00   F    0      2       2 1658   68.00   M    1      2       3 1659   30.00   M    0      2       2 1660   58.00   M    0      2       2 1661   70.00   M    0      2       3 1662   65.00   F    1      2       3 1663   48.00   F    0      2       2 1664   66.00   M    0      2       3 1665   62.00   M    1      2       3 1666   67.00   M    0      2       3 1667   47.00   F    0      2       2 1668   53.00   M    0      2       2 1669   50.00   M    0      2       2 1670   44.00   M    0      2       2 1671   69.00   M    1      2       3 1672   60.00   F    0      2       3 1673   70.00   F    1      2       3 1674   49.00   M    1      2       2 1675   40.00   M    1      2       2 1676   46.00   M    0      2       2 1677   75.00   F    1      2       3 1678   55.00   F    1      2       2 1679   78.00   M    0      2       3 1680   70.00   F   17      2       3 1681   65.00   F    6      2       3 1682   34.00   M    1      2       2 1683   59.00   M    1      2       2 1684   61.00   M    2      2       3 1685   48.00   M   11      2       2 1686   65.00   M   11      2       3 1687   60.00   M    7      2       3 1688   60.00   M    0      2       3 1689   60.00   F    8      2       3 1690   76.00   M    9      2       3 1691   55.00   M    1      2       2 1692   57.00   M    7      2       2 1693   49.00   F   10      2       2 1694   34.00   M    5      2       2 1695   58.00   M    0      2       2 1696   58.00   F    2      2       2 1697   45.00   M    9      2       2 1698   31.00   M    6      2       2 1699   78.00   M    0      2       3 1700   64.00   M    6      2       3 1701   44.00   M    1      2       2 1702   60.00   M    4      2       3 1703   78.00   F    1      2       3 1704   47.00   F    2      2       2 1705   66.00   F    7      2       3 1706   55.00   M    3      2       2 1707   50.00   F    1      2       2 1708   65.00   M    7      2       3 1709   72.00   M    4      2       3 1710   63.00   M    4      2       3 1711   25.00   M    3      2       2 1712   48.00   F    1      2       2 1713   70.00   M    3      2       3 1714   70.00   M    2      2       3 1715   55.00   F    0      2       2 1716   57.00   M    1      2       2 1717   45.00   F    4      2       2 1718   64.00   M    5      2       3 1719   54.00   M    0      2       2 1720   47.00   M    5      2       2 1721   21.00   M    1      2       2 1722   79.00   M    1      2       3 1723   65.00   F    2      2       3 1724   76.00   M    4      2       3 1725   58.00   M    0      2       2 1726   45.00   F    0      2       2 1727   68.00   M    0      2       3 1728   58.00   M    4      2       2 1729   64.00   M    3      2       3 1730   78.00   M    0      2       3 1731   67.00   M    1      2       3 1732   81.00   F    1      2       3 1733   55.00   M    3      2       2 1734   55.00   M    3      2       2 1735   55.00   F    1      2       2 1736   52.00   M    0      2       2 1737   63.00   M    1      2       3 1738   55.00   M    0      2       2 1739   65.00   M    0      2       3 1740   66.00   M    0      2       3 1741   65.00   M    0      2       3 1742   70.00   F    1      2       3 1743   49.00   M    1      2       2 1744   59.00   F    1      2       2 1745   64.00   F    5      2       3 1746   57.00   M    1      2       2 1747   66.00   M    0      2       3 1748   48.00   M    3      2       2 1749   70.00   M    2      2       3 1750   82.00   M    0      2       3 1751   76.00   M    0      2       3 1752   73.00   M    4      2       3 1753   40.00   F    1      2       2 1754   45.00   M    4      2       2 1755   73.00   M    2      2       3 1756   53.00   M    0      2       2 1757   80.00   F    1      2       3 1758   69.00   M    1      2       3 1759   41.00   M    1      2       2 1760   75.00   F    0      2       3 1761   70.00   M    0      2       3 1762   72.00   M   16      2       3 1763   41.00   F    4      2       2 1764   38.00   F    2      2       2 1765   72.00   M   14      2       3 1766   45.00   M    0      2       2 1767   65.00   F    7      2       3 1768   58.00   M   13      2       2 1769   56.00   M    9      2       2 1770   59.00   F    7      2       2 1771   75.00   M   10      2       3 1772   80.00   M    0      2       3 1773   65.00   F   12      2       3 1774   73.00   M    7      2       3 1775   36.00   M   12      2       2 1776   47.00   M    3      2       2 1777   88.00   M   13      2       3 1778   53.00   F    9      2       2 1779   75.00   F    1      2       3 1780   50.00   M    5      2       2 1781   60.00   M    4      2       3 1782   65.00   M    8      2       3 1783   70.00   M    4      2       3 1784   93.00   M    8      2       3 1785   70.00   M    0      2       3 1786   70.00   F    6      2       3 1787   72.00   M    3      2       3 1788   75.00   M    6      2       3 1789   68.00   M    6      2       3 1790   54.00   M    6      2       2 1791   55.00   M    3      2       2 1792   71.00   M    4      2       3 1793   75.00   F    0      2       3 1794   55.00   M    1      2       2 1795   48.00   M    3      2       2 1796   76.00   F    3      2       3 1797   60.00   M    3      2       3 1798   71.00   F    3      2       3 1799   75.00   M    0      2       3 1800   63.00   F    1      2       3 1801   83.00   M    0      2       3 1802   76.00   M    0      2       3 1803   82.00   M    1      2       3 1804   65.00   F    5      2       3 1805   70.00   M    2      2       3 1806   60.00   M    1      2       3 1807   51.00   M    0      2       2 1808   65.00   F    1      2       3 1809   61.00   F    2      2       3 1810   64.00   M    3      2       3 1811   60.00   F    1      2       3 1812   65.00   M    0      2       3 1813   41.00   M    0      2       2 1814   35.00   M    0      2       2 1815   43.00   M    1      2       2 1816   63.00   M    1      2       3 1817   74.00   F    0      2       3 1818   60.00   M    3      2       3 1819   61.00   M    0      2       3 1820   75.00   M    7      2       3 1821   75.00   M    0      2       3 1822   35.00   M    0      2       2 1823   50.00   F    0      2       2 1824   73.00   M    0      2       3 1825   48.00   M    4      2       2 1826   75.00   F    1      2       3 1827   52.00   M    2      2       2 1828   63.00   M    0      2       3 1829   65.00   M    1      2       3 1830   65.00   M    0      2       3 1831   67.00   M    0      2       3 1832   65.00   M    3      2       3 1833   50.00   M    0      2       2 1834   55.00   M    0      2       2 1835   51.00   M    1      2       2 1836   39.00   M    0      2       2 1837   89.00   M   12      2       3 1838   46.00   M   16      2       2 1839   56.00   M   19      2       2 1840   52.00   F    1      2       2 1841   62.00   M    2      2       3 1842   58.00   M   10      2       2 1843   65.00   F   10      2       3 1844   63.00   M   10      2       3 1845   63.00   F    1      2       3 1846   67.00   M    0      2       3 1847   55.00   M    1      2       2 1848   38.00   M    2      2       2 1849   65.00   M    0      2       3 1850   60.00   M   11      2       3 1851   60.00   F    6      2       3 1852   75.00   F    6      2       3 1853   46.00   M    0      2       2 1854   74.00   M    0      2       3 1855   72.00   M    8      2       3 1856   68.00   M    2      2       3 1857   62.00   M    9      2       3 1858   51.00   M    0      2       2 1859   55.00   F    0      2       2 1860   60.00   F    6      2       3 1861   65.00   F    2      2       3 1862   66.00   F    2      2       3 1863   43.00   M    6      2       2 1864   58.00   F    5      2       2 1865   82.00   M    2      2       3 1866   63.00   M    7      2       3 1867   84.00   M    6      2       3 1868   65.00   F    7      2       3 1869   65.00   M    8      2       3 1870   68.00   M    0      2       3 1871   64.00   M    7      2       3 1872   46.00   M    6      2       2 1873   60.00   F    8      2       3 1874   48.00   M    5      2       2 1875   44.00   M    3      2       2 1876   61.00   F    0      2       3 1877   78.00   F   10      2       3 1878   55.00   M    5      2       2 1879   72.00   M    1      2       3 1880   58.00   M   11      2       2 1881   70.00   M    9      2       3 1882   56.00   M   12      2       2 1883   47.00   M    1      2       2 1884   52.00   M    0      2       2 1885   62.00   F    1      2       3 1886   74.00   F    1      2       3 1887   59.00   F    3      2       2 1888   38.00   F    0      2       2 1889   60.00   M    5      2       3 1890   68.00   F    3      2       3 1891   75.00   M    1      2       3 1892   70.00   M    2      2       3 1893   55.00   M    1      2       2 1894   50.00   M    2      2       2 1895   72.00   F    1      2       3 1896   64.00   F    2      2       3 1897   62.00   F    2      2       3 1898   58.00   F    1      2       2 1899   58.00   F    0      2       2 1900   69.00   M   20      2       3 1901   60.00   F    1      2       3 1902   76.00   F    6      2       3 1903   31.00   F    5      2       2 1904   40.00   F    0      2       2 1905   56.00   M    3      2       2 1906   59.00   F    5      2       2 1907   55.00   M    1      2       2 1908   71.00   F    2      2       3 1909   45.00   M    4      2       2 1910   53.00   M    0      2       2 1911   60.00   M    0      2       3 1912   50.00   M    2      2       2 1913   31.00   M    1      2       2 1914   50.00   F    2      2       2 1915   46.00   M    0      2       2 1916   60.00   M    1      2       3 1917   40.00   M    0      2       2 1918   62.00   F    2      2       3 1919   57.00   M    4      2       2 1920   60.00   F    1      2       3 1921   66.00   M    0      2       3 1922   55.00   F    1      2       2 1923   65.00   M    0      2       3 1924   63.00   M   20      2       3 1925   56.00   F    0      2       2 1926   60.00   M    0      2       3 1927   40.00   M    0      2       2 1928   50.00   F    0      2       2 1929   38.00   F    0      2       2 1930   65.00   M    0      2       3 1931   80.00   M    0      2       3 1932   55.00   M    0      2       2 1933   72.00   F    2      2       3 1934   50.00   M    7      2       2 1935   47.00   M    0      2       2 1936   47.00   M    2      2       2 1937   54.00   M    2      2       2 1938   65.00   F    0      2       3 1939   67.00   F   19      2       3 1940   60.00   M    0      2       3 1941   60.00   F    0      2       3 1942   50.00   F    0      2       2 1943   49.00   M    0      2       2 1944   50.00   M    9      2       2 1945   65.00   M    3      2       3 1946   54.00   M    2      2       2 1947   62.00   M    8      2       3 1948   45.00   M    2      2       2 1949   66.00   M   11      2       3 1950   59.00   M    5      2       2 1951   58.00   M    2      2       2 1952   60.00   M   25      2       3 1953   58.00   M    5      2       2 1954   60.00   F    3      2       3 1955   75.00   M    3      2       3 1956   50.00   F    9      2       2 1957   60.00   M    9      2       3 1958   55.00   F    1      2       2 1959   54.00   F    1      2       2 1960   26.00   F    3      2       2 1961   60.00   M    9      2       3 1962   53.00   F    7      2       2 1963   35.00   F    0      2       2 1964   59.00   M    8      2       2 1965   62.00   M    3      2       3 1966   55.00   M    8      2       2 1967   60.00   M    8      2       3 1968   63.00   F    2      2       3 1969   98.00   M    4      2       3 1970   68.00   F    1      2       3 1971   68.00   F    5      2       3 1972   72.00   M    4      2       3 1973   80.00   M    1      2       3 1974   72.00   M    2      2       3 1975   68.00   F    1      2       3 1976   60.00   F    5      2       3 1977   48.00   M    4      2       2 1978   50.00   M    6      2       2 1979   56.00   M    1      2       2 1980   56.00   M    4      2       2 1981   52.00   M    2      2       2 1982   60.00   M    1      2       3 1983   78.00   M    0      2       3 1984   48.00   M    2      2       2 1985   62.00   M    2      2       3 1986   62.00   M    4      2       3 1987   70.00   F    1      2       3 1988   75.00   M    2      2       3 1989   44.00   F    3      2       2 1990   62.00   M    2      2       3 1991   78.00   M    3      2       3 1992   52.00   M    0      2       2 1993   55.00   M    0      2       2 1994   75.00   F   17      2       3 1995   80.00   M    0      2       3 1996   43.00   M    1      2       2 1997   36.00   M    0      2       2 1998   65.00   M    5      2       3 1999   51.00   M    1      2       2 2000   34.00   M    1      2       2 2001   60.00   F    0      2       3 2002   47.00   M    1      2       2 2003   64.00   M   10      2       3 2004   51.00   M    3      2       2 2005   62.00   M    2      2       3 2006   82.00   M    1      2       3 2007   82.00   F    0      2       3 2008   72.00   M    0      2       3 2009   59.00   F    5      2       2 2010   65.00   F    0      2       3 2011   74.00   M    0      2       3 2012   86.00   F    0      2       3 2013   44.00   M    1      2       2 2014   70.00   F    1      2       3 2015   62.00   M    0      2       3 2016   46.00   F    2      2       2 2017   44.00   M    0      2       2 2018   50.00   F    1      2       2 2019   71.00   M    0      2       3 2020   30.00   F    1      2       2 2021   69.00   M    1      2       3 2022   65.00   F    2      2       3 2023   65.00   F    1      2       3 2024   75.00   M    0      2       3 2025   51.00   M    0      2       2 2026   58.00   M    0      2       2 2027   34.00   M    0      2       2 2028   56.00   M    0      2       2 2029   72.00   F    0      2       3 2030   65.00   M    1      2       3 2031   70.00   F    1      2       3 2032   66.00   M   16      2       3 2033   73.00   M    3      2       3 2034   68.00   F    0      2       3 2035   46.00   M    1      2       2 2036   60.00   F    0      2       3 2037   55.00   M    0      2       2 2038   59.00   M   10      2       2 2039   46.00   M   10      2       2 2040   72.00   F    5      2       3 2041   84.00   F    4      2       3 2042   74.00   F    7      2       3 2043   58.00   F    0      2       2 2044   70.00   F   14      2       3 2045   46.00   F   10      2       2 2046   71.00   M    8      2       3 2047   88.00   M    7      2       3 2048   68.00   M    0      2       3 2049   75.00   M    6      2       3 2050   52.00   M    1      2       2 2051   68.00   M    6      2       3 2052   60.00   M    7      2       3 2053   64.00   F    2      2       3 2054   67.00   M    1      2       3 2055   64.00   M    7      2       3 2056   58.00   M    4      2       2 2057   67.00   M    5      2       3 2058   52.00   M    0      2       2 2059   68.00   M    2      2       3 2060   50.00   M    5      2       2 2061   56.00   M    0      2       2 2062   85.00   F    4      2       3 2063   60.00   F   12      2       3 2064   66.00   M    5      2       3 2065   80.00   M    2      2       3 2066   65.00   F    0      2       3 2067   62.00   M    2      2       3 2068   87.00   M    2      2       3 2069   82.00   M    1      2       3 2070   66.00   M    2      2       3 2071   80.00   F    3      2       3 2072   85.00   F    3      2       3 2073   50.00   F    1      2       2 2074   54.00   M    4      2       2 2075   50.00   M    2      2       2 2076   56.00   F    1      2       2 2077   80.00   M    0      2       3 2078   80.00   F    0      2       3 2079   68.00   M    3      2       3 2080   65.00   F    0      2       3 2081   58.00   M    4      2       2 2082   55.00   M    6      2       2 2083   35.00   M    0      2       2 2084   55.00   M    1      2       2 2085   65.00   F    3      2       3 2086   58.00   M    0      2       2 2087   40.00   F    0      2       2 2088   67.00   M    3      2       3 2089   24.00   M    0      2       2 2090   36.00   F    4      2       2 2091   40.00   M    1      2       2 2092   39.00   M    3      2       2 2093   52.00   M    0      2       2 2094   66.00   F    2      2       3 2095   36.00   M    0      2       2 2096   55.00   M    3      2       2 2097   65.00   F    0      2       3 2098   42.00   M    0      2       2 2099   47.00   F    9      2       2 2100   55.00   M    1      2       2 2101   55.00   F    8      2       2 2102   73.00   F    2      2       3 2103   80.00   F   13      2       3 2104   39.00   M    0      2       2 2105   56.00   M    1      2       2 2106   52.00   M    0      2       2 2107   26.00   F    1      2       2 2108   63.00   F    5      2       3 2109   76.00   M    1      2       3 2110   50.00   M    0      2       2 2111   55.00   F    0      2       2 2112   58.00   F    1      2       2 2113   72.00   M    0      2       3 2114   62.00   F   23      2       3 2115   30.00   F    0      2       2 2116   72.00   M   12      2       3 2117   91.00   M   11      2       3 2118   59.00   M   12      2       2 2119   22.00   M    3      2       2 2120   81.00   F   10      2       3 2121   73.00   F    2      2       3 2122   53.00   M    0      2       2 2123   65.00   M    8      2       3 2124   67.00   M   15      2       3 2125   70.00   M   15      2       3 2126   87.00   F   11      2       3 2127   50.00   F    7      2       2 2128   60.00   M   12      2       3 2129   65.00   M    3      2       3 2130   30.00   M    1      2       2 2131   54.00   F   13      2       2 2132   72.00   F   22      2       3 2133   71.00   F   14      2       3 2134   52.00   F    7      2       2 2135    8.00   F    0      2       1 2136   64.00   F    3      2       3 2137   86.00   F    5      2       3 2138   86.00   M    4      2       3 2139   65.00   F    0      2       3 2140   77.00   M    1      2       3 2141   69.00   M    4      2       3 2142   54.00   F    0      2       2 2143   70.00   M    7      2       3 2144   60.00   M    1      2       3 2145   52.00   M    0      2       2 2146   70.00   M    5      2       3 2147   50.00   M    0      2       2 2148   80.00   F    2      2       3 2149   88.00   M   21      2       3 2150   76.00   M    1      2       3 2151   60.00   F    1      2       3 2152   41.00   M    3      2       2 2153   58.00   M    5      2       2 2154   54.00   M    5      2       2 2155   45.00   F    3      2       2 2156   60.00   M    1      2       3 2157   48.00   F    3      2       2 2158   63.00   M    3      2       3 2159   60.00   M    2      2       3 2160   92.00   M    3      2       3 2161   46.00   M    1      2       2 2162   65.00   F    2      2       3 2163   85.00   M    1      2       3 2164   55.00   F    2      2       2 2165   85.00   M    3      2       3 2166   64.00   F    0      2       3 2167   65.00   F    2      2       3 2168   38.00   M    1      2       2 2169   64.00   M    0      2       3 2170   69.00   M    1      2       3 2171   65.00   F    1      2       3 2172   51.00   F    3      2       2 2173   84.00   F    0      2       3 2174   57.00   F    0      2       2 2175   57.00   M    2      2       2 2176   49.00   M    4      2       2 2177   60.00   F    0      2       3 2178   63.00   F    0      2       3 2179   58.00   F    0      2       2 2180   56.00   M    0      2       2 2181   62.00   F    0      2       3 2182   83.00   F    1      2       3 2183   63.00   F    1      2       3 2184   55.00   M    0      2       2 2185   74.00   M    3      2       3 2186   56.00   F   18      2       2 2187   52.00   M    1      2       2 2188   78.00   M    1      2       3 2189   54.00   F    1      2       2 2190   53.00   M    2      2       2 2191   45.00   M    1      2       2 2192   19.00   M    1      2       2 2193   82.00   M    0      2       3 2194   58.00   M    0      2       2 2195   47.00   M    0      2       2 2196   65.00   M    1      2       3 2197   40.00   F    1      2       2 2198   60.00   F    1      2       3 2199   78.00   F   11      2       3 2200   66.00   M   10      2       3 2201   73.00   M   10      2       3 2202   60.00   F   12      2       3 2203   52.00   M   11      2       2 2204   74.00   M    3      2       3 2205   62.00   M    8      2       3 2206   65.00   M    4      2       3 2207   63.00   M    2      2       3 2208   60.00   M    2      2       3 2209   55.00   M    3      2       2 2210   52.00   M    3      2       2 2211   76.00   M   15      2       3 2212   73.00   M    5      2       3 2213   68.00   M    6      2       3 2214   62.00   M    6      2       3 2215   65.00   F    0      2       3 2216   72.00   M    4      2       3 2217   65.00   M    5      2       3 2218   52.00   M    6      2       2 2219   72.00   M    5      2       3 2220   62.00   M    5      2       3 2221   34.00   M    2      2       2 2222   72.00   M    2      2       3 2223   61.00   M    4      2       3 2224   36.00   M    4      2       2 2225   53.00   M    1      2       2 2226   39.00   M    6      2       2 2227   52.00   M    1      2       2 2228   96.00   M    1      2       3 2229   65.00   F    6      2       3 2230   45.00   M    6      2       2 2231   55.00   M    7      2       2 2232   56.00   M    0      2       2 2233   70.00   M    0      2       3 2234   81.00   F    2      2       3 2235   75.00   M    0      2       3 2236   61.00   M    0      2       3 2237   45.00   M    0      2       2 2238   57.00   M    3      2       2 2239   55.00   F    1      2       2 2240   56.00   M    0      2       2 2241   40.00   M    3      2       2 2242   66.00   M    1      2       3 2243   65.00   F    0      2       3 2244   70.00   M    0      2       3 2245   42.00   F    1      2       2 2246   50.00   M    1      2       2 2247   60.00   M    1      2       3 2248   63.00   M    0      2       3 2249   56.00   M    1      2       2 2250   56.00   M    4      2       2 2251   63.00   M    0      2       3 2252   60.00   F    0      2       3 2253   36.00   F    1      2       2 2254   59.00   M    1      2       2 2255   70.00   M    1      2       3 2256   58.00   F    0      2       2 2257   75.00   F    2      2       3 2258   90.00   M    3      2       3 2259   70.00   F    1      2       3 2260   49.00   F    0      2       2 2261   84.00   M    5      2       3 2262   60.00   M    4      2       3 2263   80.00   F    0      2       3 2264   72.00   M    0      2       3 2265   53.00   M    0      2       2 2266   69.00   M    0      2       3 2267   55.00   F    2      2       2 2268   87.00   M    2      2       3 2269   63.00   M    1      2       3 2270   56.00   M    3      2       2 2271   67.00   F    3      2       3 2272   65.00   F    0      2       3 2273   62.00   F    2      2       3 2274   51.00   M    4      2       2 2275   31.00   M    1      2       2 2276   65.00   M    2      2       3 2277   64.00   M    0      2       3 2278   94.00   M    1      2       3 2279   62.00   M    1      2       3 2280   35.00   M    0      2       2 2281   75.00   M    1      2       3 2282   70.00   F    0      2       3 2283   65.00   M    1      2       3 2284   60.00   F    0      2       3 2285   60.00   F    0      2       3 2286   59.00   M    0      2       2 2287   61.00   M    1      2       3 2288   65.00   M    0      2       3 2289   72.00   M    0      2       3 2290   60.00   F    1      2       3 2291   32.00   M    0      2       2 2292   78.00   F    0      2       3 2293   40.00   F    0      2       2 2294   55.00   F    0      2       2 2295   59.00   M   10      2       2 2296   80.00   M   19      2       3 2297   80.00   F   16      2       3 2298   67.00   M   21      2       3 2299   72.00   M    0      2       3 2300   87.00   M    3      2       3 2301   69.00   F    0      2       3 2302   65.00   M    2      2       3 2303   52.00   M    5      2       2 2304   65.00   M    8      2       3 2305   28.00   M    9      2       2 2306   61.00   M    2      2       3 2307   80.00   M    6      2       3 2308   68.00   M    9      2       3 2309   55.00   F    9      2       2 2310   65.00   M   10      2       3 2311   57.00   M    6      2       2 2312   65.00   F   11      2       3 2313   80.00   M    0      2       3 2314   50.00   M    0      2       2 2315   73.00   F    2      2       3 2316   78.00   M    3      2       3 2317   74.00   M    5      2       3 2318   82.00   M    1      2       3 2319   83.00   M    1      2       3 2320   72.00   M   22      2       3 2321   46.00   M    1      2       2 2322   36.00   M    0      2       2 2323   70.00   M    1      2       3 2324   75.00   M    3      2       3 2325   85.00   F    4      2       3 2326   85.00   M    2      2       3 2327   69.00   M    2      2       3 2328   70.00   M    6      2       3 2329   47.00   M    7      2       2 2330   66.00   M    0      2       3 2331   69.00   M   11      2       3 2332   70.00   F    3      2       3 2333   78.00   M    0      2       3 2334   56.00   M    1      2       2 2335   45.00   F    2      2       2 2336   61.00   M    1      2       3 2337   70.00   M    4      2       3 2338   51.00   M    3      2       2 2339   52.00   M    1      2       2 2340   42.00   F    0      2       2 2341   67.00   M    2      2       3 2342   77.00   F    2      2       3 2343   68.00   M    5      2       3 2344   45.00   F    1      2       2 2345   40.00   M    8      2       2 2346   68.00   M    0      2       3 2347   54.00   M    1      2       2 2348   67.00   M    3      2       3 2349   66.00   M    2      2       3 2350   66.00   M    0      2       3 2351   72.00   M    0      2       3 2352   75.00   M    0      2       3 2353   62.00   F    0      2       3 2354   57.00   M    1      2       2 2355   62.00   M    0      2       3 2356   72.00   F    3      2       3 2357   85.00   F    0      2       3 2358   58.00   M    1      2       2 2359   55.00   F    0      2       2 2360   57.00   F    8      2       2 2361   58.00   M    0      2       2 2362   75.00   M    0      2       3 2363   80.00   M    1      2       3 2364   84.00   M    0      2       3 2365   70.00   F    0      2       3 2366   66.00   M    2      2       3 2367   50.00   F    0      2       2 2368   54.00   F    0      2       2 2369   60.00   M    0      2       3 2370   80.00   M    2      2       3 2371   31.00   M    4      2       2 2372   80.00   M    5      2       3 2373   20.00   F    0      2       2 2374   40.00   M    0      2       2 2375   55.00   M    1      2       2 2376   81.00   M    0      2       3 2377   78.00   F    1      2       3 2378   62.00   M    2      2       3 2379   68.00   F   14      2       3 2380   73.00   M   16      2       3 2381   50.00   M   13      2       2 2382   31.00   F    4      2       2 2383   72.00   M   11      2       3 2384   54.00   M    5      2       2 2385   26.00   F    5      2       2 2386   35.00   M   10      2       2 2387   52.00   M    2      2       2 2388   58.00   M    0      2       2 2389   66.00   M    5      2       3 2390   70.00   F    8      2       3 2391   72.00   F   11      2       3 2392   64.00   M    0      2       3 2393   45.00   M    6      2       2 2394   86.00   M    9      2       3 2395   75.00   M    1      2       3 2396   60.00   M    9      2       3 2397   70.00   M    7      2       3 2398   75.00   M    0      2       3 2399   48.00   M    7      2       2 2400   50.00   M    9      2       2 2401   60.00   F    2      2       3 2402   80.00   F    6      2       3 2403   60.00   M    6      2       3 2404   33.00   M    0      2       2 2405   77.00   F    6      2       3 2406   54.00   M    6      2       2 2407   58.00   F   32      2       2 2408   55.00   F    0      2       2 2409   83.00   F    6      2       3 2410   64.00   M    6      2       3 2411   50.00   M    2      2       2 2412   58.00   M    1      2       2 2413   51.00   F    1      2       2 2414   74.00   F    6      2       3 2415   71.00   M    2      2       3 2416   36.00   M    6      2       2 2417   54.00   M    1      2       2 2418   58.00   M    4      2       2 2419   65.00   F    4      2       3 2420   61.00   F    2      2       3 2421   29.00   M    0      2       2 2422   62.00   F    1      2       3 2423   81.00   F    0      2       3 2424   47.00   F    6      2       2 2425   51.00   F    4      2       2 2426   64.00   M    4      2       3 2427   53.00   F    0      2       2 2428   47.00   M    3      2       2 2429   70.00   M    3      2       3 2430   76.00   F    4      2       3 2431   70.00   M    5      2       3 2432   76.00   M    0      2       3 2433   46.00   F    0      2       2 2434   51.00   M    3      2       2 2435   35.00   F    0      2       2 2436   56.00   M    6      2       2 2437   48.00   F    0      2       2 2438   40.00   M    1      2       2 2439   85.00   F    0      2       3 2440   53.00   F    4      2       2 2441   55.00   M    2      2       2 2442   85.00   M    3      2       3 2443   78.00   M    4      2       3 2444   54.00   F    2      2       2 2445   75.00   M    2      2       3 2446   59.00   M    0      2       2 2447   51.00   F    8      2       2 2448   70.00   M    2      2       3 2449   70.00   M    2      2       3 2450   65.00   M    0      2       3 2451   68.00   M    2      2       3 2452   70.00   F    2      2       3 2453   74.00   F    0      2       3 2454   70.00   M    0      2       3 2455   54.00   M    1      2       2 2456   66.00   M    2      2       3 2457   70.00   M    1      2       3 2458   70.00   M    0      2       3 2459   75.00   M    2      2       3 2460   79.00   M    0      2       3 2461   50.00   M    3      2       2 2462   68.00   F    5      2       3 2463   49.00   M    0      2       2 2464   60.00   M    2      2       3 2465   70.00   F    1      2       3 2466   55.00   F    1      2       2 2467   69.00   F   17      2       3 2468   70.00   F    0      2       3 2469   58.00   M    2      2       2 2470   86.00   M    2      2       3 2471   50.00   M    0      2       2 2472   70.00   M    0      2       3 2473   56.00   M    0      2       2 2474   51.00   M    2      2       2 2475   65.00   M    0      2       3 2476   68.00   M    1      2       3 2477   82.00   M   17      2       3 2478   62.00   M    5      2       3 2479   66.00   M    8      2       3 2480   62.00   M    2      2       3 2481   70.00   M    9      2       3 2482   49.00   F   16      2       2 2483   50.00   M    8      2       2 2484   55.00   F    2      2       2 2485   73.00   M    5      2       3 2486   70.00   F    4      2       3 2487   60.00   M    2      2       3 2488   62.00   M    0      2       3 2489   64.00   M   15      2       3 2490   55.00   F    0      2       2 2491   79.00   M    2      2       3 2492   65.00   M    8      2       3 2493   56.00   M   11      2       2 2494   67.00   M   11      2       3 2495   65.00   M   14      2       3 2496   55.00   F    2      2       2 2497   60.00   M    8      2       3 2498   58.00   M    2      2       2 2499   68.00   M    0      2       3 2500   52.00   M   10      2       2 2501   58.00   M    1      2       2 2502   80.00   F    9      2       3 2503   67.00   M    7      2       3 2504   57.00   M    7      2       2 2505   63.00   F    6      2       3 2506   68.00   M    7      2       3 2507   68.00   F    8      2       3 2508   38.00   M    0      2       2 2509   40.00   M    0      2       2 2510   54.00   M    0      2       2 2511   70.00   M    6      2       3 2512   45.00   M    1      2       2 2513   68.00   F    1      2       3 2514   65.00   M    2      2       3 2515   19.00   F    7      2       2 2516   40.00   F    4      2       2 2517   63.00   M    2      2       3 2518   56.00   F    7      2       2 2519   58.00   M   10      2       2 2520   61.00   M    1      2       3 2521   40.00   M    4      2       2 2522   52.00   F    6      2       2 2523   74.00   M    5      2       3 2524   78.00   F    5      2       3 2525   45.00   F    3      2       2 2526   55.00   F    3      2       2 2527   80.00   F    0      2       3 2528   55.00   F    5      2       2 2529   39.00   M    0      2       2 2530   74.00   M    2      2       3 2531   60.00   F    0      2       3 2532   81.00   F    2      2       3 2533   52.00   M    0      2       2 2534   60.00   F    0      2       3 2535   57.00   M    0      2       2 2536   54.00   M    1      2       2 2537   38.00   M    1      2       2 2538   45.00   M    1      2       2 2539   55.00   M    4      2       2 2540   84.00   M    1      2       3 2541   41.00   M    6      2       2 2542   68.00   F    2      2       3 2543   42.00   M    2      2       2 2544   40.00   M    3      2       2 2545   62.00   M   11      2       3 2546   65.00   M    3      2       3 2547   45.00   M    4      2       2 2548   35.00   F    0      2       2 2549   48.00   M    0      2       2 2550   62.00   M    0      2       3 2551   69.00   M    0      2       3 2552   70.00   M    3      2       3 2553   85.00   F    9      2       3 2554   60.00   F    2      2       3 2555   64.00   M    0      2       3 2556   45.00   F    1      2       2 2557   54.00   M    0      2       2 2558   67.00   M    1      2       3 2559   69.00   M   16      2       3 2560   62.00   F    2      2       3 2561   45.00   F    2      2       2 2562   65.00   M    1      2       3 2563   85.00   M    2      2       3 2564   68.00   F    1      2       3 2565   72.00   M    0      2       3 2566   44.00   M    1      2       2 2567   60.00   F    2      2       3 2568   57.00   F    1      2       2 2569   40.00   M    0      2       2 2570   76.00   F    1      2       3 2571   58.00   M    0      2       2 2572   60.00   M    2      2       3 2573   60.00   F    2      2       3 2574   69.00   M    0      2       3 2575   79.00   M    0      2       3 2576   68.00   F    9      2       3 2577   54.00   M    1      2       2 2578   60.00   M    1      2       3 2579   58.00   M    1      2       2 2580   70.00   M    0      2       3 2581   46.00   M    0      2       2 2582   60.00   M    2      2       3 2583    3.00   F    0      2       1 2584   23.00   F    0      2       2 2585   52.00   F    0      2       2 2586   55.00   F    4      2       2 2587   65.00   M   18      2       3 2588   40.00   M   20      2       2 2589   70.00   M   12      2       3 2590   39.00   M   14      2       2 2591   75.00   F    2      2       3 2592   62.00   F    1      2       3 2593   69.00   M    0      2       3 2594   70.00   M   13      2       3 2595   45.00   F    0      2       2 2596   54.00   M    0      2       2 2597   77.00   M    9      2       3 2598   58.00   F    3      2       2 2599   85.00   F    4      2       3 2600   48.00   F    4      2       2 2601   48.00   M    6      2       2 2602   35.00   M    3      2       2 2603   77.00   M    9      2       3 2604   75.00   M    7      2       3 2605   56.00   M   12      2       2 2606   73.00   M    0      2       3 2607   65.00   M    7      2       3 2608   58.00   M    1      2       2 2609   88.00   M    1      2       3 2610   63.00   M    1      2       3 2611   76.00   F    5      2       3 2612   80.00   F    1      2       3 2613   64.00   F    9      2       3 2614   72.00   M    7      2       3 2615   30.00   F    8      2       2 2616   30.00   M   10      2       2 2617   73.00   F    8      2       3 2618   62.00   F    8      2       3 2619   46.00   M    1      2       2 2620   66.00   F    0      2       3 2621   74.00   F    8      2       3 2622   55.00   F    7      2       2 2623   65.00   M   10      2       3 2624   65.00   M    2      2       3 2625   50.00   M    0      2       2 2626   60.00   F    6      2       3 2627   75.00   F    4      2       3 2628   68.00   M    6      2       3 2629   62.00   M    5      2       3 2630   65.00   M    5      2       3 2631   58.00   F    0      2       2 2632   53.00   F    6      2       2 2633   60.00   F    4      2       3 2634   23.00   F    0      2       2 2635   38.00   M    6      2       2 2636   53.00   M    0      2       2 2637   36.00   F    4      2       2 2638   49.00   M    3      2       2 2639   82.00   M    0      2       3 2640   70.00   M    3      2       3 2641   43.00   M    1      2       2 2642   67.00   M    1      2       3 2643   38.00   F    0      2       2 2644   62.00   M    3      2       3 2645   70.00   M    3      2       3 2646   42.00   M    0      2       2 2647   70.00   M    3      2       3 2648   68.00   M    1      2       3 2649   73.00   M    2      2       3 2650   40.00   M    2      2       2 2651   83.00   M    0      2       3 2652   58.00   F    2      2       2 2653   48.00   F    0      2       2 2654   75.00   M    2      2       3 2655   80.00   M    3      2       3 2656   65.00   M    1      2       3 2657   40.00   M    0      2       2 2658   67.00   M    1      2       3 2659   50.00   F    3      2       2 2660   71.00   F    0      2       3 2661   61.00   M    4      2       3 2662   58.00   M    1      2       2 2663   46.00   M    0      2       2 2664   64.00   F    1      2       3 2665   44.00   M    2      2       2 2666   65.00   F    0      2       3 2667   93.00   M    0      2       3 2668   50.00   M    0      2       2 2669   52.00   F    1      2       2 2670   60.00   M    0      2       3 2671   90.00   M    1      2       3 2672   72.00   F    1      2       3 2673   74.00   F    1      2       3 2674   90.00   M    3      2       3 2675   63.00   M    1      2       3 2676   70.00   F    1      2       3 2677   82.00   F    2      2       3 2678   67.00   M    2      2       3 2679   65.00   F    0      2       3 2680   79.00   M    0      2       3 2681   70.00   M    2      2       3 2682   65.00   M    0      2       3 2683   65.00   M   32      2       3 2684   70.00   M    2      2       3 2685   70.00   F    0      2       3 2686   64.00   M    1      2       3 2687   73.00   F   13      2       3 2688   50.00   M    1      2       2 2689   66.00   F    1      2       3 2690   87.00   F   14      2       3 2691   70.00   M   12      2       3 2692   50.00   M    0      2       2 2693   62.00   F    1      2       3 2694   65.00   F   13      2       3 2695   70.00   F   17      2       3 2696   59.00   M   10      2       2 2697   73.00   M   13      2       3 2698   78.00   F    8      2       3 2699   38.00   M   11      2       2 2700   62.00   F    1      2       3 2701   82.00   M   12      2       3 2702   82.00   M   11      2       3 2703   45.00   F    6      2       2 2704   65.00   M    9      2       3 2705   55.00   F    0      2       2 2706   40.00   M    7      2       2 2707   41.00   F    1      2       2 2708   55.00   M    8      2       2 2709   70.00   F    7      2       3 2710   70.00   F    8      2       3 2711   45.00   M    0      2       2 2712   46.00   F    8      2       2 2713   60.00   M    0      2       3 2714   72.00   M    7      2       3 2715   62.00   M    2      2       3 2716   67.00   F    9      2       3 2717   70.00   M    6      2       3 2718   65.00   M    2      2       3 2719   70.00   F    1      2       3 2720   86.00   M    6      2       3 2721   70.00   M    6      2       3 2722   62.00   M    5      2       3 2723   43.00   M   12      2       2 2724   78.00   M    6      2       3 2725   36.00   M    4      2       2 2726   62.00   M    8      2       3 2727   60.00   M    4      2       3 2728   59.00   M    1      2       2 2729   65.00   M    3      2       3 2730   56.00   M    5      2       2 2731   62.00   M    4      2       3 2732   45.00   F    1      2       2 2733   63.00   M    3      2       3 2734   46.00   M    5      2       2 2735   68.00   M    4      2       3 2736   40.00   M    0      2       2 2737   68.00   F    0      2       3 2738   62.00   F    1      2       3 2739   45.00   M    0      2       2 2740   62.00   M    4      2       3 2741   69.00   M    2      2       3 2742   65.00   M    4      2       3 2743   65.00   M    0      2       3 2744   62.00   M    0      2       3 2745   75.00   M    2      2       3 2746   60.00   M    4      2       3 2747   74.00   M    0      2       3 2748   67.00   M    0      2       3 2749   60.00   M    0      2       3 2750   36.00   M    0      2       2 2751   75.00   M    0      2       3 2752   80.00   M    0      2       3 2753   62.00   M    1      2       3 2754   60.00   M    8      2       3 2755   77.00   M    1      2       3 2756   59.00   M    2      2       2 2757   60.00   M    1      2       3 2758   43.00   M    0      2       2 2759   70.00   M    2      2       3 2760   72.00   M    1      2       3 2761   49.00   F    1      2       2 2762   80.00   F    0      2       3 2763   60.00   M    1      2       3 2764   52.00   M    2      2       2 2765   67.00   M    1      2       3 2766   67.00   M    0      2       3 2767   48.00   F    1      2       2 2768   56.00   F    0      2       2 2769   65.00   F    1      2       3 2770   52.00   M    0      2       2 2771   43.00   M    0      2       2 2772   70.00   F    1      2       3 2773   55.00   M    0      2       2 2774   58.00   F    0      2       2 2775   69.00   M    0      2       3 2776   80.00   F    0      2       3 2777   52.00   M    1      2       2 2778   65.00   M    0      2       3 2779   66.00   M   20      2       3 2780   25.00   F    1      2       2 2781   42.00   M    0      2       2 2782   63.00   F    4      2       3 2783   23.00   M    0      2       2 2784   78.00   M    1      2       3 2785   60.00   M    4      2       3 2786   53.00   M   12      2       2 2787   58.00   M   10      2       2 2788   64.00   M    1      2       3 2789   50.00   M    2      2       2 2790   48.00   M    2      2       2 2791   65.00   M    3      2       3 2792   60.00   M   14      2       3 2793   73.00   F    8      2       3 2794   57.00   M    1      2       2 2795   77.00   M   14      2       3 2796   69.00   M   10      2       3 2797   67.00   M    1      2       3 2798   40.00   M   14      2       2 2799   65.00   M   10      2       3 2800   63.00   F    9      2       3 2801   45.00   M    4      2       2 2802   35.00   F    0      2       2 2803   50.00   M    1      2       2 2804   59.00   M    3      2       2 2805   53.00   M    9      2       2 2806   58.00   M    4      2       2 2807   78.00   F    9      2       3 2808   43.00   F    2      2       2 2809   21.00   M    7      2       2 2810   46.00   M    4      2       2 2811   70.00   M   11      2       3 2812   73.00   M    2      2       3 2813   67.00   M   10      2       3 2814   74.00   M    3      2       3 2815   75.00   M    5      2       3 2816   27.00   M    2      2       2 2817   70.00   F    1      2       3 2818   46.00   F    3      2       2 2819   49.00   M   17      2       2 2820   66.00   M    0      2       3 2821   58.00   M   11      2       2 2822   52.00   F    0      2       2 2823   75.00   M    6      2       3 2824   59.00   M    0      2       2 2825   58.00   M    6      2       2 2826   54.00   M    3      2       2 2827   74.00   M    5      2       3 2828   75.00   M    2      2       3 2829   70.00   F    4      2       3 2830   60.00   M    3      2       3 2831   60.00   F    0      2       3 2832   55.00   M    1      2       2 2833   69.00   M    1      2       3 2834   59.00   M    4      2       2 2835   50.00   M    0      2       2 2836   53.00   M    2      2       2 2837   65.00   F    3      2       3 2838   50.00   F    0      2       2 2839   51.00   M    3      2       2 2840   28.00   F    3      2       2 2841   56.00   M    0      2       2 2842   85.00   M    7      2       3 2843   52.00   M    1      2       2 2844   81.00   M    0      2       3 2845   59.00   F    3      2       2 2846   85.00   M    0      2       3 2847   68.00   M    0      2       3 2848   40.00   F    0      2       2 2849   65.00   F   11      2       3 2850   67.00   M    1      2       3 2851   54.00   F    1      2       2 2852   76.00   F    2      2       3 2853   56.00   M    3      2       2 2854   35.00   M    0      2       2 2855   46.00   F    2      2       2 2856   65.00   M    1      2       3 2857   70.00   M    0      2       3 2858   30.00   M    0      2       2 2859   22.00   M    0      2       2 2860   75.00   M    3      2       3 2861   37.00   F    0      2       2 2862   60.00   M    1      2       3 2863   40.00   M    2      2       2 2864   60.00   M    1      2       3 2865   65.00   M    1      2       3 2866   55.00   M    1      2       2 2867   58.00   F    0      2       2 2868   29.00   M    3      2       2 2869   65.00   M    0      2       3 2870   75.00   M    0      2       3 2871   35.00   F    2      2       2 2872   35.00   F    0      2       2 2873   65.00   F    0      2       3 2874   44.00   M    2      2       2 2875   61.00   M    0      2       3 2876   85.00   F    1      2       3 2877   72.00   F    8      2       3 2878   72.00   F    0      2       3 2879   55.00   M    0      2       2 2880   55.00   M   18      2       2 2881   49.00   M    1      2       2 2882   66.00   M   15      2       3 2883   58.00   M    9      2       2 2884   74.00   M   12      2       3 2885   67.00   M    7      2       3 2886   63.00   M    8      2       3 2887   51.00   M    7      2       2 2888   72.00   M   30      2       3 2889   38.00   M   17      2       2 2890   60.00   F   18      2       3 2891   80.00   F   12      2       3 2892   54.00   F    5      2       2 2893   59.00   M    5      2       2 2894   82.00   M   14      2       3 2895   54.00   M    0      2       2 2896   53.00   F    3      2       2 2897   64.00   M    7      2       3 2898   59.00   M    3      2       2 2899   46.00   M    7      2       2 2900   65.00   M    5      2       3 2901   78.00   F    7      2       3 2902   50.00   M   21      2       2 2903   70.00   M   10      2       3 2904   55.00   F   11      2       2 2905   50.00   M    9      2       2 2906   65.00   M   11      2       3 2907   81.00   M    9      2       3 2908   60.00   M    8      2       3 2909   65.00   F    0      2       3 2910   39.00   M    2      2       2 2911   62.00   M   10      2       3 2912   80.00   F    9      2       3 2913   50.00   F   10      2       2 2914   51.00   M   16      2       2 2915   21.00   M    3      2       2 2916   58.00   F    8      2       2 2917   41.00   F    9      2       2 2918   45.00   M    0      2       2 2919   72.00   F    3      2       3 2920   46.00   M    1      2       2 2921   51.00   M    5      2       2 2922   57.00   M    1      2       2 2923   36.00   M    6      2       2 2924   65.00   M    0      2       3 2925   91.00   M    1      2       3 2926   83.00   M    6      2       3 2927   82.00   F    5      2       3 2928   65.00   M    4      2       3 2929   60.00   M    4      2       3 2930   70.00   M    6      2       3 2931   67.00   F    7      2       3 2932   60.00   F    1      2       3 2933   51.00   F    1      2       2 2934   69.00   M    3      2       3 2935   45.00   F    0      2       2 2936   49.00   M    4      2       2 2937   65.00   F    4      2       3 2938   55.00   F    9      2       2 2939   72.00   M    1      2       3 2940   77.00   M    4      2       3 2941   55.00   M    2      2       2 2942   73.00   M    0      2       3 2943   90.00   M    1      2       3 2944   39.00   M    4      2       2 2945   56.00   M    4      2       2 2946   75.00   F    2      2       3 2947   60.00   F    4      2       3 2948   74.00   M    3      2       3 2949   49.00   M    4      2       2 2950   58.00   M    2      2       2 2951   40.00   F    3      2       2 2952   54.00   M    1      2       2 2953   78.00   M    2      2       3 2954   65.00   M    2      2       3 2955   60.00   M    2      2       3 2956   48.00   M    2      2       2 2957   44.00   M    7      2       2 2958   55.00   M    0      2       2 2959   76.00   M    0      2       3 2960   55.00   M    1      2       2 2961   75.00   M    1      2       3 2962   82.00   M    2      2       3 2963   70.00   F    3      2       3 2964   50.00   M    5      2       2 2965   52.00   M    2      2       2 2966   55.00   M    2      2       2 2967   58.00   F    0      2       2 2968   50.00   F    1      2       2 2969   38.00   M    1      2       2 2970   75.00   M    2      2       3 2971   70.00   M    0      2       3 2972   75.00   F    4      2       3 2973   64.00   M    8      2       3 2974   58.00   F   11      2       2 2975   63.00   M    0      2       3 2976   80.00   F    9      2       3 2977   64.00   F    2      2       3 2978   71.00   M    6      2       3 2979   67.00   M    3      2       3 2980   42.00   M    7      2       2 2981   37.00   M    7      2       2 2982   85.00   M    5      2       3 2983   50.00   M    0      2       2 2984   61.00   F   16      2       3 2985   40.00   M   14      2       2 2986   51.00   M   16      2       2 2987   60.00   M   13      2       3 2988   74.00   F   14      2       3 2989   58.00   F   15      2       2 2990   65.00   M   13      2       3 2991   52.00   M   10      2       2 2992   69.00   M   11      2       3 2993   61.00   M   12      2       3 2994   80.00   M   11      2       3 2995   61.00   F   11      2       3 2996   46.00   F    0      2       2 2997   50.00   F    8      2       2 2998   65.00   M   12      2       3 2999   49.00   M    3      2       2 3000   53.00   M    7      2       2 3001   65.00   F    3      2       3 3002   72.00   F    7      2       3 3003   89.00   M    1      2       3 3004   65.00   M    1      2       3 3005   38.00   M    5      2       2 3006   45.00   M    1      2       2 3007   50.00   F    0      2       2 3008   62.00   F    9      2       3 3009   48.00   M    4      2       2 3010   40.00   M    7      2       2 3011   62.00   M    0      2       3 3012   67.00   F    9      2       3 3013   65.00   F    9      2       3 3014   62.00   M    2      2       3 3015   35.00   M    7      2       2 3016   65.00   M    0      2       3 3017   71.00   F    0      2       3 3018   47.00   M    7      2       2 3019   57.00   M    7      2       2 3020   62.00   F    0      2       3 3021   75.00   F    6      2       3 3022   48.00   M    1      2       2 3023   55.00   M    1      2       2 3024   49.00   M    6      2       2 3025   57.00   M    2      2       2 3026   43.00   F    1      2       2 3027   77.00   M    5      2       3 3028   76.00   M    5      2       3 3029   50.00   M    4      2       2 3030   82.00   F    1      2       3 3031   64.00   M    0      2       3 3032   74.00   F    3      2       3 3033   59.00   M    4      2       2 3034   55.00   F    1      2       2 3035   54.00   M    1      2       2 3036   33.00   M    5      2       2 3037   78.00   F   10      2       3 3038   57.00   M    1      2       2 3039   63.00   M    4      2       3 3040   50.00   F    0      2       2 3041   63.00   M    1      2       3 3042   80.00   M    3      2       3 3043   58.00   M    0      2       2 3044   55.00   M    1      2       2 3045   60.00   M    1      2       3 3046   45.00   F    0      2       2 3047   66.00   M    5      2       3 3048   68.00   M    0      2       3 3049   48.00   M    3      2       2 3050   75.00   F   14      2       3 3051   27.00   M    0      2       2 3052   57.00   M    1      2       2 3053   73.00   M    1      2       3 3054   55.00   M    0      2       2 3055   80.00   M    0      2       3 3056   70.00   M    1      2       3 3057   55.00   M    0      2       2 3058   71.00   M    2      2       3 3059   60.00   F    1      2       3 3060   32.00   M    5      2       2 3061   70.00   F    0      2       3 3062   60.00   F    2      2       3 3063   55.00   F    0      2       2 3064   23.00   F    1      2       2 3065   55.00   F    2      2       2 3066   40.00   M   14      2       2 3067   75.00   M    0      2       3 3068   38.00   M    2      2       2 3069   30.00   M    0      2       2 3070   49.00   M    0      2       2 3071   75.00   M    3      2       3 3072   56.00   M    2      2       2 3073   55.00   F    1      2       2 3074   75.00   M    0      2       3 3075   54.00   M    0      2       2 3076   55.00   M    1      2       2 3077   65.00   M    2      2       3 3078   55.00   M    1      2       2 3079   70.00   F    0      2       3 3080   46.00   F   19      2       2 3081   80.00   M    8      2       3 3082   73.00   M    0      2       3 3083   77.00   M    2      2       3 3084   77.00   M    1      2       3 3085   54.00   F    5      2       2 3086   58.00   M   11      2       2 3087   56.00   F    8      2       2 3088   57.00   M    5      2       2 3089   62.00   M    0      2       3 3090   86.00   F   15      2       3 3091   50.00   F    0      2       2 3092   46.00   M    2      2       2 3093   56.00   M    6      2       2 3094   54.00   F    2      2       2 3095   76.00   F    5      2       3 3096   56.00   M    2      2       2 3097   61.00   M    4      2       3 3098   77.00   M    4      2       3 3099   53.00   F    0      2       2 3100   70.00   M    2      2       3 3101   73.00   F    6      2       3 3102   63.00   F    1      2       3 3103   59.00   M   15      2       2 3104   72.00   F   16      2       3 3105   78.00   M    2      2       3 3106   52.00   F   12      2       2 3107   53.00   M   15      2       2 3108   64.00   M    1      2       3 3109   70.00   F    0      2       3 3110   64.00   M    8      2       3 3111   66.00   M    7      2       3 3112   70.00   M    1      2       3 3113   70.00   F    1      2       3 3114   60.00   M    9      2       3 3115   21.00   M    6      2       2 3116   61.00   M    3      2       3 3117   82.00   M    8      2       3 3118   50.00   M    4      2       2 3119   49.00   M    8      2       2 3120   67.00   M    9      2       3 3121   79.00   M   10      2       3 3122   69.00   M   13      2       3 3123   70.00   M    4      2       3 3124   67.00   M    7      2       3 3125   58.00   M   10      2       2 3126   52.00   M    0      2       2 3127   64.00   M    5      2       3 3128   72.00   M    9      2       3 3129   65.00   M    5      2       3 3130   70.00   F    5      2       3 3131   45.00   F    5      2       2 3132   49.00   M    5      2       2 3133   46.00   M    4      2       2 3134   68.00   F    1      2       3 3135   54.00   M    8      2       2 3136   39.00   M    4      2       2 3137   67.00   M    3      2       3 3138   77.00   M    5      2       3 3139   41.00   M    2      2       2 3140   48.00   M    2      2       2 3141   46.00   M    1      2       2 3142   71.00   F   12      2       3 3143   44.00   M    5      2       2 3144   68.00   M    5      2       3 3145   78.00   F    4      2       3 3146   38.00   M    0      2       2 3147   68.00   M    6      2       3 3148   67.00   M    2      2       3 3149   65.00   M    2      2       3 3150   92.00   M    3      2       3 3151   52.00   F    0      2       2 3152   56.00   M    0      2       2 3153   70.00   M    0      2       3 3154   68.00   M    4      2       3 3155   45.00   F    3      2       2 3156   67.00   F    1      2       3 3157   50.00   M    2      2       2 3158   58.00   F    3      2       2 3159   65.00   M    4      2       3 3160   57.00   M    2      2       2 3161   51.00   M    1      2       2 3162   62.00   M    1      2       3 3163   87.00   M    3      2       3 3164   45.00   F    0      2       2 3165   48.00   F    1      2       2 3166   65.00   M    0      2       3 3167   55.00   M    2      2       2 3168   66.00   M    0      2       3 3169   76.00   F    5      2       3 3170   69.00   M    1      2       3 3171   62.00   M    0      2       3 3172   60.00   M    0      2       3 3173   75.00   M    1      2       3 3174  102.00   M    0      2       3 3175   65.00   F    1      2       3 3176   89.00   F    2      2       3 3177   62.00   M    1      2       3 3178   32.00   F    3      2       2 3179   36.00   M   16      2       2 3180   40.00   F    0      2       2 3181   41.00   M    0      2       2 3182   14.00   M    2      2       1 3183   41.00   M    1      2       2 3184   60.00   M    1      2       3 3185   55.00   M    0      2       2 3186   60.00   M    0      2       3 3187   57.00   M    4      2       2 3188   63.00   M    6      2       3 3189   63.00   F    1      2       3 3190   58.00   M    0      2       2 3191   45.00   F    0      2       2 3192   45.00   F    1      2       2 3193   65.00   M    1      2       3 3194   76.00   M    0      2       3 3195   63.00   M    1      2       3 3196   83.00   F    8      2       3 3197   64.00   F   16      2       3 3198   89.00   M    5      2       3 3199   40.00   M    0      2       2 3200   89.00   M    1      2       3 3201   55.00   M   13      2       2 3202   43.00   F    9      2       2 3203   73.00   M   10      2       3 3204   43.00   F    1      2       2 3205   49.00   M    5      2       2 3206   70.00   M   10      2       3 3207   80.00   M    2      2       3 3208   55.00   M    0      2       2 3209   38.00   M    8      2       2 3210   75.00   M    1      2       3 3211   65.00   F    0      2       3 3212   48.00   M    7      2       2 3213   52.00   F   13      2       2 3214   64.00   M   12      2       3 3215   74.00   M   19      2       3 3216   62.00   F    2      2       3 3217   72.00   M   10      2       3 3218   71.00   M   12      2       3 3219   50.00   M   10      2       2 3220   52.00   M    5      2       2 3221   70.00   M   10      2       3 3222   53.00   M   10      2       2 3223   80.00   M    9      2       3 3224   70.00   F    7      2       3 3225   35.00   M    9      2       2 3226   72.00   M    1      2       3 3227   75.00   M    9      2       3 3228   73.00   M    1      2       3 3229   50.00   F    4      2       2 3230   64.00   F    0      2       3 3231   60.00   F    0      2       3 3232   42.00   F    7      2       2 3233   74.00   F    2      2       3 3234   85.00   F    7      2       3 3235   72.00   F    3      2       3 3236   75.00   F    5      2       3 3237   80.00   M    6      2       3 3238   50.00   F    3      2       2 3239   76.00   M    4      2       3 3240   69.00   M    7      2       3 3241   48.00   M    5      2       2 3242   73.00   F    3      2       3 3243   66.00   M    5      2       3 3244   53.00   M    5      2       2 3245   55.00   F    6      2       2 3246   38.00   M    4      2       2 3247   72.00   F    3      2       3 3248   55.00   M   14      2       2 3249   82.00   M    4      2       3 3250   54.00   M    0      2       2 3251   75.00   F    2      2       3 3252   50.00   M    0      2       2 3253   55.00   M    1      2       2 3254   63.00   M    2      2       3 3255   88.00   F    4      2       3 3256   71.00   F    1      2       3 3257   75.00   F    1      2       3 3258   65.00   F    0      2       3 3259   80.00   M    1      2       3 3260   70.00   F    1      2       3 3261   55.00   M    1      2       2 3262   50.00   M    2      2       2 3263   40.00   M    4      2       2 3264   57.00   F    0      2       2 3265   55.00   M    5      2       2 3266   56.00   M    0      2       2 3267   65.00   M    0      2       3 3268   45.00   F    1      2       2 3269   80.00   F    1      2       3 3270   40.00   F    3      2       2 3271   83.00   M    0      2       3 3272   38.00   M    0      2       2 3273   49.00   F    0      2       2 3274   70.00   F    1      2       3 3275   71.00   M    1      2       3 3276   42.00   M    0      2       2 3277   70.00   F    0      2       3 3278   65.00   M    0      2       3 3279   72.00   F    0      2       3 3280   65.00   M    1      2       3 3281   35.00   M   21      2       2 3282   67.00   M   10      2       3 3283   23.00   M    1      2       2 3284   66.00   F    7      2       3 3285   28.00   M   12      2       2 3286   68.00   F    1      2       3 3287   61.00   F    0      2       3 3288   63.00   F   12      2       3 3289   57.00   F    0      2       2 3290   53.00   M   11      2       2 3291   65.00   F    9      2       3 3292   53.00   M    7      2       2 3293   66.00   M   12      2       3 3294   52.00   F    1      2       2 3295   60.00   M   13      2       3 3296   77.00   M    7      2       3 3297   43.00   F    1      2       2 3298   61.00   F    0      2       3 3299   56.00   M    6      2       2 3300   46.00   F    4      2       2 3301   72.00   M    8      2       3 3302   75.00   M   11      2       3 3303   65.00   F    1      2       3 3304   18.00   M   15      2       2 3305   81.00   M   12      2       3 3306   70.00   M    7      2       3 3307   33.00   M   11      2       2 3308   62.00   M    6      2       3 3309   50.00   M    2      2       2 3310   55.00   F    1      2       2 3311   77.00   F    8      2       3 3312   66.00   M    5      2       3 3313   31.00   M   11      2       2 3314   71.00   M    7      2       3 3315   67.00   M    3      2       3 3316   66.00   M    4      2       3 3317   65.00   M   11      2       3 3318   50.00   M    0      2       2 3319   65.00   F    9      2       3 3320   78.00   M    4      2       3 3321   80.00   M    7      2       3 3322   45.00   F    1      2       2 3323   73.00   F    3      2       3 3324   56.00   M    1      2       2 3325   55.00   M    5      2       2 3326   48.00   M    6      2       2 3327   52.00   F    5      2       2 3328   37.00   M    7      2       2 3329   92.00   M    8      2       3 3330   75.00   F    1      2       3 3331   65.00   F    0      2       3 3332   53.00   M    6      2       2 3333   75.00   M    6      2       3 3334   74.00   M    0      2       3 3335   23.00   M    7      2       2 3336   61.00   M    1      2       3 3337   70.00   M    7      2       3 3338   78.00   M    4      2       3 3339   58.00   F   37      2       2 3340   60.00   M   11      2       3 3341   72.00   M    3      2       3 3342   72.00   M    1      2       3 3343   75.00   M    6      2       3 3344   68.00   M    9      2       3 3345   82.00   M    4      2       3 3346   68.00   F   17      2       3 3347   66.00   M    4      2       3 3348   40.00   M    0      2       2 3349   11.00   M    5      2       1 3350   55.00   F    0      2       2 3351   86.00   M    5      2       3 3352   37.00   M    2      2       2 3353   69.00   M    2      2       3 3354   34.00   M    1      2       2 3355   65.00   M    3      2       3 3356   52.00   M    7      2       2 3357   70.00   F    4      2       3 3358   66.00   M    3      2       3 3359   65.00   F   14      2       3 3360   57.00   M    1      2       2 3361   65.00   M    1      2       3 3362   74.00   M    2      2       3 3363   49.00   M    0      2       2 3364   23.00   M    1      2       2 3365   56.00   M    2      2       2 3366   63.00   M    2      2       3 3367   75.00   M    0      2       3 3368   69.00   M    4      2       3 3369   82.00   M    0      2       3 3370   64.00   M    0      2       3 3371   43.00   F    1      2       2 3372   75.00   M    2      2       3 3373   67.00   M    0      2       3 3374   90.00   M    3      2       3 3375   67.00   M    0      2       3 3376   70.00   M    1      2       3 3377   63.00   M    1      2       3 3378   60.00   M    0      2       3 3379   67.00   M    0      2       3 3380   85.00   F    2      2       3 3381   45.00   M    0      2       2 3382   70.00   M    1      2       3 3383   82.00   M    1      2       3 3384   67.00   M    0      2       3 3385   55.00   M    2      2       2 3386   60.00   M    2      2       3 3387   65.00   M    0      2       3 3388   67.00   F    0      2       3 3389   49.00   F    8      2       2 3390   90.00   F    1      2       3 3391   61.00   M    2      2       3 3392   45.00   F    1      2       2 3393   90.00   M    7      2       3 3394   56.00   F    1      2       2 3395   65.00   F    4      2       3 3396   67.00   M   10      2       3 3397   68.00   M    2      2       3 3398   68.00   M   17      2       3 3399   65.00   M    3      2       3 3400   68.00   M    0      2       3 3401   58.00   M   13      2       2 3402   58.00   F    2      2       2 3403   60.00   F    6      2       3 3404   67.00   F   13      2       3 3405   51.00   F   19      2       2 3406   76.00   F    1      2       3 3407   51.00   M    3      2       2 3408   83.00   M    9      2       3 3409   76.00   M    1      2       3 3410   74.00   F    6      2       3 3411   87.00   F   19      2       3 3412   79.00   F   16      2       3 3413   76.00   M    6      2       3 3414   76.00   M    1      2       3 3415   70.00   M    3      2       3 3416   88.00   M    2      2       3 3417   22.00   F   16      2       2 3418   65.00   M    6      2       3 3419   54.00   M    6      2       2 3420   74.00   M   13      2       3 3421   57.00   M    6      2       2 3422   70.00   M   13      2       3 3423   60.00   F    0      2       3 3424   56.00   M   14      2       2 3425   68.00   M    2      2       3 3426   65.00   F   10      2       3 3427   45.00   M    1      2       2 3428   75.00   M    6      2       3 3429   61.00   M    8      2       3 3430   71.00   M    5      2       3 3431   47.00   M    6      2       2 3432   35.00   M    8      2       2 3433   67.00   F    8      2       3 3434   45.00   M    8      2       2 3435   80.00   F    9      2       3 3436   54.00   M   25      2       2 3437   58.00   M    3      2       2 3438   52.00   F    0      2       2 3439   87.00   M    6      2       3 3440   80.00   M    6      2       3 3441   75.00   M    7      2       3 3442   64.00   M    8      2       3 3443   43.00   M    0      2       2 3444   34.00   M    2      2       2 3445   35.00   F    0      2       2 3446   50.00   M    6      2       2 3447   56.00   M    7      2       2 3448   48.00   M    6      2       2 3449   68.00   F    1      2       3 3450   70.00   M    4      2       3 3451   83.00   M    5      2       3 3452   50.00   M    4      2       2 3453   75.00   M    2      2       3 3454   34.00   M   12      2       2 3455   58.00   M    3      2       2 3456   53.00   F    4      2       2 3457   65.00   F    6      2       3 3458   78.00   F    0      2       3 3459   70.00   F    3      2       3 3460   65.00   M    5      2       3 3461   77.00   M    0      2       3 3462   80.00   F    4      2       3 3463   65.00   M    3      2       3 3464   82.00   M    2      2       3 3465   58.00   M    5      2       2 3466   57.00   M   31      2       2 3467   60.00   M    2      2       3 3468   35.00   F    4      2       2 3469   52.00   M    2      2       2 3470   58.00   F    6      2       2 3471   45.00   M    0      2       2 3472   80.00   M    1      2       3 3473   56.00   M    5      2       2 3474   80.00   M    2      2       3 3475   70.00   F    1      2       3 3476   95.00   F    0      2       3 3477   39.00   M    0      2       2 3478   70.00   M    0      2       3 3479   59.00   M    2      2       2 3480   60.00   M    2      2       3 3481   48.00   M    2      2       2 3482   57.00   M    2      2       2 3483   58.00   F    3      2       2 3484   70.00   M    1      2       3 3485   65.00   M    2      2       3 3486   77.00   M   21      2       3 3487   70.00   M    0      2       3 3488   65.00   M    1      2       3 3489   24.00   M    1      2       2 3490   50.00   M    1      2       2 3491   70.00   M    0      2       3 3492   62.00   M    1      2       3 3493   80.00   M    1      2       3 3494   80.00   M    0      2       3 3495   78.00   M    0      2       3 3496   55.00   M    1      2       2 3497   58.00   M    0      2       2 3498   42.00   M    3      2       2 3499   74.00   F    0      2       3 3500   54.00   F    0      2       2 3501   53.00   F   16      2       2 3502   58.00   M    2      2       2 3503   69.00   M    7      2       3 3504   58.00   M    3      2       2 3505   34.00   M    0      2       2 3506   73.00   M    0      2       3 3507   86.00   F    8      2       3 3508   46.00   M    1      2       2 3509   79.00   M    5      2       3 3510   62.00   F    3      2       3 3511   59.00   F    4      2       2 3512   75.00   M    0      2       3 3513   80.00   M    4      2       3 3514   65.00   F    7      2       3 3515   60.00   F    0      2       3 3516   76.00   F   18      2       3 3517   65.00   M    3      2       3 3518   70.00   F    0      2       3 3519   84.00   M    8      2       3 3520   46.00   F    0      2       2 3521   55.00   F    1      2       2 3522   57.00   M    3      2       2 3523   55.00   M    0      2       2 3524   66.00   M   15      2       3 3525   72.00   M   18      2       3 3526   70.00   F    3      2       3 3527   63.00   M   14      2       3 3528   66.00   M   14      2       3 3529   64.00   F    7      2       3 3530   60.00   M    0      2       3 3531   73.00   M    4      2       3 3532   55.00   M   15      2       2 3533   65.00   M    2      2       3 3534   86.00   M    0      2       3 3535   88.00   F   13      2       3 3536   68.00   M   13      2       3 3537   58.00   M   11      2       2 3538   58.00   F   12      2       2 3539   82.00   M   10      2       3 3540   64.00   M    6      2       3 3541   67.00   M   11      2       3 3542   64.00   F    0      2       3 3543   72.00   M   10      2       3 3544   64.00   M   11      2       3 3545   55.00   F    2      2       2 3546   44.00   M    9      2       2 3547   73.00   M    7      2       3 3548   68.00   M    0      2       3 3549   40.00   F    0      2       2 3550   70.00   M    1      2       3 3551   80.00   M    4      2       3 3552   65.00   M    3      2       3 3553   52.00   M    1      2       2 3554   72.00   M   16      2       3 3555   70.00   M    1      2       3 3556   75.00   M    0      2       3 3557   48.00   M    6      2       2 3558   60.00   F    6      2       3 3559   72.00   M    9      2       3 3560   33.00   F   10      2       2 3561   53.00   M    7      2       2 3562   30.00   M    2      2       2 3563   52.00   M    0      2       2 3564   62.00   M    4      2       3 3565   76.00   M    5      2       3 3566   87.00   M    9      2       3 3567   80.00   M    6      2       3 3568   53.00   F    2      2       2 3569   86.00   M    5      2       3 3570   67.00   M    0      2       3 3571   33.00   M    8      2       2 3572   52.00   F    1      2       2 3573   48.00   F    2      2       2 3574   78.00   F    4      2       3 3575   80.00   M    3      2       3 3576   67.00   M    0      2       3 3577   58.00   M    3      2       2 3578   61.00   F    5      2       3 3579   73.00   M    2      2       3 3580   52.00   F    2      2       2 3581   50.00   M    0      2       2 3582   85.00   M    2      2       3 3583   70.00   M    2      2       3 3584   60.00   M    4      2       3 3585   55.00   F    0      2       2 3586   69.00   M    0      2       3 3587   80.00   F    2      2       3 3588   50.00   M    3      2       2 3589   64.00   F    0      2       3 3590   65.00   M    1      2       3 3591   70.00   M    1      2       3 3592   82.00   M    3      2       3 3593   61.00   M    0      2       3 3594   72.00   M    6      2       3 3595   68.00   M    0      2       3 3596   60.00   F    0      2       3 3597   68.00   M    1      2       3 3598   62.00   F    0      2       3 3599   50.00   M    0      2       2 3600   71.00   M    9      2       3 3601   45.00   M    0      2       2 3602   45.00   M    0      2       2 3603   70.00   F    0      2       3 3604   54.00   M    1      2       2 3605   53.00   F    4      2       2 3606   49.00   M    4      2       2 3607   49.00   M    6      2       2 3608   65.00   F    4      2       3 3609   62.00   M   17      2       3 3610   70.00   M   22      2       3 3611   50.00   F    7      2       2 3612   56.00   F   18      2       2 3613   46.00   F   20      2       2 3614   49.00   M    8      2       2 3615   58.00   M    1      2       2 3616   60.00   M    2      2       3 3617   72.00   M    7      2       3 3618   60.00   F   19      2       3 3619   45.00   F    1      2       2 3620   40.00   M   10      2       2 3621   75.00   M    3      2       3 3622   65.00   M    2      2       3 3623   55.00   M    9      2       2 3624   43.00   M   11      2       2 3625   42.00   M   14      2       2 3626   53.00   M   13      2       2 3627   32.00   M   16      2       2 3628   66.00   M   11      2       3 3629   66.00   F   12      2       3 3630   38.00   M   11      2       2 3631   68.00   F   10      2       3 3632   70.00   M   11      2       3 3633   62.00   F   11      2       3 3634   55.00   M    6      2       2 3635   68.00   M   12      2       3 3636   72.00   F   12      2       3 3637   70.00   M    0      2       3 3638   85.00   F    9      2       3 3639   48.00   M    7      2       2 3640   50.00   M    4      2       2 3641   70.00   M    1      2       3 3642   63.00   M    2      2       3 3643   76.00   M    6      2       3 3644   71.00   M    9      2       3 3645   80.00   M    9      2       3 3646   92.00   M    0      2       3 3647   68.00   M    0      2       3 3648   64.00   M    6      2       3 3649   66.00   M   26      2       3 3650   76.00   M    4      2       3 3651   60.00   F    0      2       3 3652   67.00   M    6      2       3 3653   68.00   M   33      2       3 3654   80.00   M    7      2       3 3655   78.00   M    4      2       3 3656   50.00   M    0      2       2 3657   25.00   F    1      2       2 3658   37.00   F    0      2       2 3659   55.00   M    6      2       2 3660   70.00   F    1      2       3 3661   77.00   M    7      2       3 3662   50.00   F    1      2       2 3663   65.00   M    1      2       3 3664   60.00   M    0      2       3 3665   72.00   M    5      2       3 3666   53.00   M    6      2       2 3667   74.00   M    0      2       3 3668   70.00   M    1      2       3 3669   49.00   M    0      2       2 3670   64.00   F    4      2       3 3671   78.00   M    5      2       3 3672   36.00   M    3      2       2 3673   58.00   M    1      2       2 3674   67.00   M   22      2       3 3675   65.00   M    3      2       3 3676   54.00   F    5      2       2 3677   62.00   F    0      2       3 3678   69.00   M    2      2       3 3679   80.00   M   21      2       3 3680   44.00   F    0      2       2 3681   39.00   M    3      2       2 3682   61.00   M    3      2       3 3683   73.00   M    2      2       3 3684   70.00   M    3      2       3 3685   34.00   M   10      2       2 3686   62.00   M    5      2       3 3687   48.00   M    2      2       2 3688   75.00   F    2      2       3 3689   38.00   F    0      2       2 3690   65.00   M    0      2       3 3691   87.00   F    0      2       3 3692   59.00   M    1      2       2 3693   55.00   M    1      2       2 3694   45.00   F    2      2       2 3695   48.00   F    0      2       2 3696   28.00   M    0      2       2 3697   52.00   M    5      2       2 3698   56.00   F    6      2       2 3699   60.00   M   11      2       3 3700   74.00   M    2      2       3 3701   65.00   F    2      2       3 3702   48.00   M    0      2       2 3703   65.00   M    1      2       3 3704   65.00   M    2      2       3 3705   60.00   M    0      2       3 3706   40.00   M    0      2       2 3707   70.00   F    1      2       3 3708   62.00   F    6      2       3 3709   39.00   M    1      2       2 3710   63.00   M    1      2       3 3711   70.00   M    1      2       3 3712   70.00   F    0      2       3 3713   86.00   M    0      2       3 3714   63.00   F    0      2       3 3715   48.00   M    0      2       2 3716   65.00   M    0      2       3 3717   69.00   M    0      2       3 3718   55.00   F    0      2       2 3719   71.00   M    0      2       3 3720   75.00   M    0      2       3 3721   58.00   F    0      2       2 3722   71.00   F    0      2       3 3723   78.00   M    0      2       3 3724   71.00   F    0      2       3 3725   67.00   F    0      2       3 3726   67.00   F    0      2       3 3727   70.00   M    0      2       3 3728   53.00   M    0      2       2 3729   72.00   M    0      2       3 3730   65.00   M    0      2       3 3731   58.00   M    0      2       2 3732   44.00   F    0      2       2 3733   58.00   F    0      2       2 3734   79.00   M    0      2       3 3735   40.00   M    0      2       2 3736   65.00   F    0      2       3 3737   80.00   F    0      2       3 3738   70.00   M    0      2       3 3739   40.00   F    0      2       2 3740   75.00   M    0      2       3 3741   63.00   M    0      2       3 3742   74.00   M    0      2       3 3743   70.00   M    0      2       3 3744   42.00   M    0      2       2 3745   78.00   M    0      2       3 3746   71.00   M    0      2       3 3747   46.00   F    0      2       2 3748   76.00   M    0      2       3 3749   63.00   M    0      2       3 3750   73.00   M    0      2       3 3751   80.00   M    0      2       3 3752   85.00   M    0      2       3 3753   62.00   M    0      2       3 3754   73.00   M    0      2       3 3755   46.00   M    0      2       2 3756   78.00   F    0      2       3 3757   55.00   M    0      2       2 3758   66.00   M    0      2       3 3759   60.00   F    0      2       3 3760   70.00   F    0      2       3 3761   68.00   F    0      2       3 3762   62.00   M    0      2       3 3763   75.00   F    0      2       3 3764   62.00   M    0      2       3 3765   71.00   M    0      2       3 3766   63.00   M    0      2       3 3767   23.00   M    0      2       2 3768   50.00   M    0      2       2 3769   60.00   M    0      2       3 3770   70.00   M    0      2       3 3771   76.00   M    0      2       3 3772   70.00   M    0      2       3 3773   68.00   M    0      2       3 3774   79.00   F    0      2       3 3775   67.00   M    0      2       3 3776   80.00   M    0      2       3 3777   26.00   F    0      2       2 3778   72.00   F    0      2       3 3779   82.00   M    0      2       3 3780   62.00   M    0      2       3 3781   48.00   M    0      2       2 3782   50.00   M    0      2       2 3783   72.00   F    0      2       3 3784   75.00   M    0      2       3 3785   63.00   M    0      2       3 3786   65.00   F    0      2       3 3787   57.00   M    0      2       2 3788   46.00   M    0      2       2 3789   42.00   F    0      2       2 3790   75.00   M    0      2       3 3791   56.00   F    0      2       2 3792   56.00   M    0      2       2 3793   61.00   F    0      2       3 3794   77.00   F    0      2       3 3795   35.00   M    0      2       2 3796   41.00   M    0      2       2 3797   72.00   M    0      2       3 3798   58.00   M    0      2       2 3799   65.00   M    0      2       3 3800   65.00   F    0      2       3 3801   60.00   M    0      2       3 3802   60.00   F    0      2       3 3803   64.00   M    0      2       3 3804   65.00   M    0      2       3 3805   62.00   M    0      2       3 3806   77.00   F    0      2       3 3807   62.00   F    0      2       3 3808   56.00   M    0      2       2 3809   50.00   M    0      2       2 3810   65.00   F    0      2       3 3811   80.00   F    0      2       3 3812   65.00   F    0      2       3 3813   56.00   M    0      2       2 3814   70.00   M    0      2       3 3815   73.00   M    0      2       3 3816   70.00   M    0      2       3 3817   77.00   M    0      2       3 3818   78.00   M    0      2       3 3819   80.00   M    0      2       3 3820   80.00   F    0      2       3 3821   60.00   F    0      2       3 3822   63.00   M    0      2       3 3823   46.00   M    0      2       2 3824   50.00   F    0      2       2 3825   40.00   M    0      2       2 3826   80.00   M    0      2       3 3827   38.00   M    0      2       2 3828   56.00   M    0      2       2 3829   51.00   F    0      2       2 3830   76.00   M    0      2       3 3831   67.00   F    0      2       3 3832   16.00   M    0      2       1 3833   68.00   M    0      2       3 3834   68.00   M    0      2       3 3835   66.00   M    0      2       3 3836   58.00   M    0      2       2 3837   50.00   M    0      2       2 3838   52.00   M    6      2       2 3839   52.00   F    9      2       2 3840   61.00   M   24      2       3 3841   77.00   M    7      2       3 3842   85.00   F    4      2       3 3843   44.00   F    1      2       2 3844   59.00   M    9      2       2 3845   57.00   M    3      2       2 3846   48.00   M    1      2       2 3847   48.00   M    3      2       2 3848   74.00   F    5      2       3 3849   50.00   M   24      2       2 3850   33.00   M    5      2       2 3851   49.00   M   12      2       2 3852   72.00   M   28      2       3 3853   65.00   M    7      2       3 3854   66.00   M    4      2       3 3855   70.00   M   11      2       3 3856   49.00   F   11      2       2 3857   78.00   M   21      2       3 3858   42.00   M    2      2       2 3859   55.00   M    4      2       2 3860   62.00   M    2      2       3 3861   70.00   M   48      2       3 3862   70.00   F   18      2       3 3863   73.00   M    4      2       3 3864   68.00   F    3      2       3 3865   56.00   F    9      2       2 3866   70.00   M    6      2       3 3867   85.00   M    2      2       3 3868   64.00   M    5      2       3 3869   52.00   M   18      2       2 3870   60.00   F   10      2       3 3871   33.00   M   11      2       2 3872   30.00   F    1      2       2 3873   65.00   M   14      2       3 3874   86.00   M    9      2       3 3875   51.00   M   11      2       2 3876   59.00   M    0      2       2 3877   57.00   M   12      2       2 3878   30.00   M    0      2       2 3879   58.00   M   17      2       2 3880   64.00   M    0      2       3 3881   55.00   M    9      2       2 3882   51.00   M   37      2       2 3883   69.00   M    1      2       3 3884   50.00   M   16      2       2 3885   41.00   M    8      2       2 3886   62.00   M   10      2       3 3887   81.00   M    7      2       3 3888   56.00   M    6      2       2 3889   67.00   M    5      2       3 3890   48.00   F    9      2       2 3891   50.00   M    6      2       2 3892   72.00   M    3      2       3 3893   90.00   F    8      2       3 3894   59.00   F    5      2       2 3895   60.00   M    5      2       3 3896   42.00   M    1      2       2 3897   56.00   M    5      2       2 3898   54.00   M    0      2       2 3899   55.00   M    6      2       2 3900   45.00   M    4      2       2 3901   65.00   M    1      2       3 3902   78.00   M    6      2       3 3903   82.00   M   11      2       3 3904   70.00   M    2      2       3 3905   72.00   M    2      2       3 3906   45.00   F    2      2       2 3907   65.00   M    1      2       3 3908   49.00   F    5      2       2 3909   50.00   M    3      2       2 3910   35.00   M    0      2       2 3911   72.00   M    6      2       3 3912   38.00   M    1      2       2 3913   60.00   M    1      2       3 3914   63.00   M    1      2       3 3915   42.00   M    4      2       2 3916   29.00   F    6      2       2 3917   68.00   M    4      2       3 3918   75.00   M    1      2       3 3919   72.00   F    0      2       3 3920   55.00   M    0      2       2 3921   87.00   M    1      2       3 3922   72.00   M    1      2       3 3923   80.00   F    1      2       3 3924   58.00   F    2      2       2 3925   70.00   M    3      2       3 3926   60.00   F    0      2       3 3927   85.00   F    3      2       3 3928   38.00   F    2      2       2 3929   86.00   F    4      2       3 3930   60.00   M    2      2       3 3931   67.00   F    1      2       3 3932   80.00   F    8      2       3 3933   74.00   M    0      2       3 3934   42.00   M    3      2       2 3935   43.00   F    4      2       2 3936   35.00   F    0      2       2 3937   73.00   M    1      2       3 3938   50.00   F    0      2       2 3939   50.00   M    0      2       2 3940   69.00   M    2      2       3 3941   57.00   F    0      2       2 3942   50.00   M    0      2       2 3943   51.00   M    0      2       2 3944   62.00   M    0      2       3 3945   47.00   F    2      2       2 3946   53.00   M    1      2       2 3947   70.00   F    2      2       3 3948   76.00   M    2      2       3 3949   75.00   M    0      2       3 3950   40.00   M    4      2       2 3951   50.00   M    0      2       2 3952   65.00   M    0      2       3 3953   66.00   M    4      2       3 3954   37.00   F    3      2       2 3955   42.00   F    6      2       2 3956   58.00   M   14      2       2 3957   51.00   M   10      2       2 3958   73.00   M    1      2       3 3959   92.00   M    0      2       3 3960   62.00   F    0      2       3 3961   38.00   F   10      2       2 3962   67.00   M   20      2       3 3963   65.00   F    0      2       3 3964   67.00   M   20      2       3 3965   73.00   M   23      2       3 3966   56.00   M   16      2       2 3967   80.00   M   13      2       3 3968   55.00   M   17      2       2 3969   63.00   F    6      2       3 3970   39.00   F    8      2       2 3971   55.00   M   12      2       2 3972   75.00   F    6      2       3 3973   42.00   M    2      2       2 3974   75.00   F   19      2       3 3975   87.00   M    7      2       3 3976   75.00   F    6      2       3 3977   26.00   M    7      2       2 3978   63.00   M    5      2       3 3979   60.00   M   18      2       3 3980   45.00   M   10      2       2 3981   68.00   M   18      2       3 3982   68.00   M   12      2       3 3983   62.00   F    1      2       3 3984   65.00   F   10      2       3 3985   57.00   M   19      2       2 3986   54.00   M    2      2       2 3987   62.00   M    7      2       3 3988   34.00   M   14      2       2 3989   41.00   M   16      2       2 3990   55.00   F    4      2       2 3991   60.00   M    2      2       3 3992   50.00   M    2      2       2 3993   74.00   M    6      2       3 3994   48.00   M   10      2       2 3995   56.00   M   10      2       2 3996   35.00   F   10      2       2 3997   75.00   M    0      2       3 3998   80.00   M   13      2       3 3999   72.00   M   11      2       3 4000   83.00   M    1      2       3 4001   48.00   M    8      2       2 4002   65.00   M   22      2       3 4003   84.00   M    0      2       3 4004   77.00   M    8      2       3 4005   56.00   M    8      2       2 4006   75.00   M    0      2       3 4007   38.00   M    0      2       2 4008   63.00   F    0      2       3 4009   72.00   M    4      2       3 4010   62.00   M   10      2       3 4011   54.00   F    0      2       2 4012   75.00   M   10      2       3 4013   67.00   F    2      2       3 4014   52.00   M    3      2       2 4015   75.00   F    3      2       3 4016   64.00   M    1      2       3 4017   72.00   F   11      2       3 4018   75.00   F    0      2       3 4019   57.00   M   11      2       2 4020   65.00   F    3      2       3 4021   94.00   M    3      2       3 4022   63.00   M    1      2       3 4023   60.00   M    8      2       3 4024   42.00   M   11      2       2 4025   56.00   F    7      2       2 4026   58.00   M    7      2       2 4027   68.00   M    5      2       3 4028   60.00   M    6      2       3 4029   65.00   F   31      2       3 4030   40.00   M    2      2       2 4031   67.00   M    5      2       3 4032   68.00   M    3      2       3 4033   48.00   M    3      2       2 4034   38.00   M    4      2       2 4035   37.00   M    8      2       2 4036   67.00   M    1      2       3 4037   62.00   F    1      2       3 4038   52.00   M    2      2       2 4039   53.00   M    4      2       2 4040   67.00   F    1      2       3 4041   48.00   M    7      2       2 4042   75.00   M    3      2       3 4043   80.00   M    1      2       3 4044   40.00   M    0      2       2 4045   41.00   M    0      2       2 4046   65.00   M    1      2       3 4047   63.00   M    0      2       3 4048   45.00   M    5      2       2 4049   49.00   M    0      2       2 4050   55.00   F    5      2       2 4051   62.00   M    4      2       3 4052   45.00   M    4      2       2 4053   60.00   F    1      2       3 4054   60.00   M    0      2       3 4055   80.00   M    3      2       3 4056   50.00   M    0      2       2 4057   55.00   M    2      2       2 4058   78.00   M    0      2       3 4059   67.00   F    0      2       3 4060   45.00   M    0      2       2 4061   90.00   F    0      2       3 4062   62.00   M    0      2       3 4063   53.00   M    0      2       2 4064   60.00   M    0      2       3 4065   64.00   F    0      2       3 4066   37.00   F    0      2       2 4067   24.00   F    2      2       2 4068   75.00   M    0      2       3 4069   22.00   F    4      2       2 4070   75.00   F    0      2       3 4071   54.00   F    1      2       2 4072   34.00   F    2      2       2 4073   64.00   M    2      2       3 4074    0.00   M    0      2       1 4075   44.00   F    0      2       2 4076   65.00   M    1      2       3 4077   64.00   M    4      2       3 4078   60.00   M    3      2       3 4079   65.00   F    1      2       3 4080   56.00   M    0      2       2 4081   47.00   M    0      2       2 4082   65.00   M    2      2       3 4083   51.00   M    2      2       2 4084   65.00   F    0      2       3 4085   80.00   M    0      2       3 4086   55.00   M    1      2       2 4087   60.00   M    0      2       3 4088   65.00   F    1      2       3 4089   32.00   M    0      2       2 4090   59.00   F    0      2       2 4091   24.00   M    1      2       2 4092   74.00   F    9      2       3 4093   63.00   M   20      2       3 4094   72.00   M   21      2       3 4095   85.00   F   20      2       3 4096   42.00   M    9      2       2 4097   79.00   M   14      2       3 4098   65.00   M    5      2       3 4099   49.00   M   17      2       2 4100   70.00   M   22      2       3 4101   46.00   M    5      2       2 4102   70.00   F    7      2       3 4103   84.00   M    4      2       3 4104   67.00   F    5      2       3 4105   45.00   M    6      2       2 4106   48.00   M    1      2       2 4107   47.00   M   13      2       2 4108   82.00   M    7      2       3 4109   65.00   M    1      2       3 4110   78.00   M    7      2       3 4111   70.00   M    0      2       3 4112   38.00   M    0      2       2 4113   45.00   M    0      2       2 4114   80.00   M    5      2       3 4115   57.00   M    6      2       2 4116   50.00   M    0      2       2 4117   80.00   M    4      2       3 4118   76.00   M    4      2       3 4119   45.00   M    1      2       2 4120   73.00   M    1      2       3 4121   72.00   M    5      2       3 4122   65.00   M    3      2       3 4123   65.00   M    0      2       3 4124   68.00   M   13      2       3 4125   52.00   M    9      2       2 4126   45.00   F    1      2       2 4127   52.00   M    4      2       2 4128   64.00   M    9      2       3 4129   76.00   M    3      2       3 4130   84.00   M    4      2       3 4131   36.00   M   11      2       2 4132   46.00   F    2      2       2 4133   64.00   M    9      2       3 4134   53.00   M    9      2       2 4135   75.00   F    0      2       3 4136   82.00   M    5      2       3 4137   56.00   F    8      2       2 4138   58.00   M    9      2       2 4139   73.00   F    4      2       3 4140   62.00   M    7      2       3 4141   45.00   M    5      2       2 4142   55.00   F    7      2       2 4143   55.00   F    4      2       2 4144   55.00   M    8      2       2 4145   53.00   F    6      2       2 4146   42.00   M    0      2       2 4147   78.00   M    6      2       3 4148   60.00   M    8      2       3 4149   62.00   M    0      2       3 4150   71.00   M    7      2       3 4151   27.00   M    6      2       2 4152   60.00   F    7      2       3 4153   66.00   F    9      2       3 4154   61.00   F    4      2       3 4155   55.00   M    5      2       2 4156   65.00   M    0      2       3 4157   73.00   M    4      2       3 4158   75.00   M    4      2       3 4159   64.00   F    5      2       3 4160   52.00   F    6      2       2 4161   85.00   M    0      2       3 4162   40.00   F    1      2       2 4163   52.00   F    4      2       2 4164   31.00   M    0      2       2 4165   65.00   M    3      2       3 4166   50.00   M    3      2       2 4167   67.00   M    1      2       3 4168   58.00   M    2      2       2 4169   60.00   F    0      2       3 4170   56.00   M   29      2       2 4171   55.00   M    2      2       2 4172   65.00   M    3      2       3 4173   53.00   M    0      2       2 4174   60.00   M    3      2       3 4175   50.00   F    1      2       2 4176   55.00   M    0      2       2 4177   72.00   F    2      2       3 4178   85.00   F    1      2       3 4179   64.00   M    4      2       3 4180   67.00   F    0      2       3 4181   42.00   M    4      2       2 4182   38.00   M    4      2       2 4183   58.00   M    3      2       2 4184   56.00   M    0      2       2 4185   61.00   M    3      2       3 4186   65.00   F    0      2       3 4187   20.00   M    1      2       2 4188   89.00   M    1      2       3 4189   70.00   M    3      2       3 4190   65.00   F    2      2       3 4191   47.00   M    1      2       2 4192   71.00   F    0      2       3 4193   70.00   M    1      2       3 4194   47.00   M    0      2       2 4195   88.00   M    1      2       3 4196   46.00   M    0      2       2 4197   71.00   M    9      2       3 4198   45.00   M    0      2       2 4199   70.00   F   22      2       3 4200   75.00   M    0      2       3 4201   70.00   M    0      2       3 4202   58.00   M    2      2       2 4203   55.00   F    2      2       2 4204   59.00   M    0      2       2 4205   55.00   F    0      2       2 4206   50.00   F    0      2       2 4207   75.00   M    1      2       3 4208   46.00   M    0      2       2 4209   33.00   M    0      2       2 4210   60.00   M    3      2       3 4211   78.00   M    0      2       3 4212   73.00   F   10      2       3 4213   65.00   M    0      2       3 4214   70.00   F    0      2       3 4215   60.00   M    0      2       3 4216   50.00   F    1      2       2 4217   48.00   M    2      2       2 4218   75.00   M   16      2       3 4219   72.00   F    9      2       3 4220   52.00   F    6      2       2 4221   10.00   F    1      2       1 4222   55.00   F    5      2       2 4223   67.00   M   15      2       3 4224   61.00   M    2      2       3 4225   53.00   F    4      2       2 4226   60.00   M    8      2       3 4227   68.00   F    9      2       3 4228   62.00   M   15      2       3 4229   50.00   F    4      2       2 4230   71.00   M    2      2       3 4231   72.00   F    0      2       3 4232   53.00   M    4      2       2 4233   36.00   M   11      2       2 4234   75.00   F    8      2       3 4235   70.00   M    2      2       3 4236   55.00   M    6      2       2 4237   52.00   F    9      2       2 4238   62.00   M    4      2       3 4239   70.00   M   11      2       3 4240   42.00   F    3      2       2 4241   68.00   F   11      2       3 4242   56.00   M   11      2       2 4243   56.00   F    9      2       2 4244   59.00   M    6      2       2 4245   60.00   F   10      2       3 4246   63.00   M    8      2       3 4247   75.00   F    8      2       3 4248   64.00   M    9      2       3 4249   75.00   M    0      2       3 4250   34.00   F    2      2       2 4251   61.00   F    6      2       3 4252   40.00   M   13      2       2 4253   70.00   M    8      2       3 4254   55.00   F    9      2       2 4255   74.00   M    1      2       3 4256   36.00   M    6      2       2 4257   68.00   F    3      2       3 4258   40.00   M    6      2       2 4259   45.00   F    2      2       2 4260   68.00   M    6      2       3 4261   64.00   M    3      2       3 4262   63.00   M    4      2       3 4263   62.00   M    4      2       3 4264   65.00   M    5      2       3 4265   43.00   M    5      2       2 4266   50.00   M    2      2       2 4267   62.00   M    6      2       3 4268   42.00   M    2      2       2 4269   71.00   M    5      2       3 4270   64.00   M    3      2       3 4271   40.00   M    3      2       2 4272   55.00   F    3      2       2 4273   68.00   M    5      2       3 4274   60.00   F    4      2       3 4275   68.00   M    1      2       3 4276   60.00   M    4      2       3 4277   45.00   F    3      2       2 4278   84.00   M    4      2       3 4279   60.00   F    5      2       3 4280   55.00   M    1      2       2 4281   45.00   M    1      2       2 4282   82.00   M    4      2       3 4283   57.00   F    3      2       2 4284   30.00   M    2      2       2 4285    0.00   F    3      2       1 4286   58.00   F    1      2       2 4287   45.00   M    8      2       2 4288   55.00   M    0      2       2 4289   42.00   M    3      2       2 4290   65.00   F    1      2       3 4291   65.00   F    0      2       3 4292   46.00   F    3      2       2 4293   53.00   M    1      2       2 4294   75.00   F    2      2       3 4295   65.00   M    0      2       3 4296   65.00   M    2      2       3 4297   51.00   F    0      2       2 4298   69.00   F    1      2       3 4299   52.00   M    1      2       2 4300   55.00   M    3      2       2 4301   68.00   F    1      2       3 4302   51.00   M    4      2       2 4303   27.00   M    2      2       2 4304   40.00   F    2      2       2 4305   52.00   M    4      2       2 4306   30.00   M    9      2       2 4307   25.00   F    1      2       2 4308   65.00   F    0      2       3 4309   57.00   M    0      2       2 4310   63.00   M    1      2       3 4311   70.00   M    5      2       3 4312   65.00   M    0      2       3 4313   71.00   M    1      2       3 4314   65.00   M    1      2       3 4315   50.00   F    1      2       2 4316   52.00   M    0      2       2 4317   63.00   M    1      2       3 4318   55.00   M    0      2       2 4319   60.00   M    1      2       3 4320   52.00   F    1      2       2 4321   75.00   F    2      2       3 4322   38.00   M    6      2       2 4323   55.00   F    4      2       2 4324   73.00   M    3      2       3 4325   60.00   F   42      2       3 4326   88.00   F    6      2       3 4327   70.00   F    2      2       3 4328   75.00   M   18      2       3 4329   56.00   M    8      2       2 4330   52.00   M    7      2       2 4331   55.00   F    4      2       2 4332   65.00   F   10      2       3 4333   60.00   M   11      2       3 4334   66.00   M   11      2       3 4335   75.00   M    3      2       3 4336   76.00   M   11      2       3 4337   65.00   M   10      2       3 4338   63.00   M    5      2       3 4339   70.00   M    1      2       3 4340   65.00   M   12      2       3 4341   71.00   M   11      2       3 4342   85.00   F    1      2       3 4343   41.00   F    8      2       2 4344   49.00   M   10      2       2 4345    7.00   F    0      2       1 4346   69.00   M    9      2       3 4347   41.00   M    6      2       2 4348   53.00   M   10      2       2 4349   62.00   F    7      2       3 4350   58.00   M    0      2       2 4351   61.00   M    0      2       3 4352   53.00   M    2      2       2 4353   65.00   F    0      2       3 4354   68.00   F    3      2       3 4355   92.00   M    0      2       3 4356   60.00   M    6      2       3 4357   75.00   F    5      2       3 4358   78.00   M    6      2       3 4359   58.00   M    9      2       2 4360   76.00   F    9      2       3 4361   77.00   M    4      2       3 4362   78.00   M    4      2       3 4363   52.00   M    4      2       2 4364   60.00   M    1      2       3 4365   60.00   F    4      2       3 4366   57.00   M    7      2       2 4367   55.00   F    2      2       2 4368   64.00   M    6      2       3 4369   65.00   M    4      2       3 4370   72.00   M    6      2       3 4371   60.00   M    4      2       3 4372   50.00   M    2      2       2 4373   65.00   F    6      2       3 4374   60.00   M    5      2       3 4375   68.00   F    4      2       3 4376   60.00   F    1      2       3 4377   62.00   M    3      2       3 4378   50.00   M    0      2       2 4379   65.00   F    3      2       3 4380   73.00   M    1      2       3 4381   48.00   F    6      2       2 4382   59.00   M    2      2       2 4383   65.00   F    1      2       3 4384   68.00   M    0      2       3 4385   53.00   M    0      2       2 4386   77.00   M    0      2       3 4387   35.00   M    3      2       2 4388   68.00   M    4      2       3 4389   59.00   F    0      2       2 4390   38.00   M    0      2       2 4391   60.00   F    7      2       3 4392   82.00   M    3      2       3 4393   95.00   M   10      2       3 4394   60.00   M    0      2       3 4395   92.00   F    0      2       3 4396   70.00   M    0      2       3 4397   76.00   M    1      2       3 4398   29.00   M    1      2       2 4399   20.00   M    0      2       2 4400   85.00   M    0      2       3 4401   55.00   M    0      2       2 4402   75.00   F    0      2       3 4403   75.00   M    0      2       3 4404   48.00   M    6      2       2 4405   74.00   F    1      2       3 4406   61.00   M    6      2       3 4407   35.00   M    1      2       2 4408   60.00   F    1      2       3 4409   64.00   M    7      2       3 4410   30.00   M    0      2       2 4411   74.00   F    0      2       3 4412   58.00   F    0      2       2 4413   62.00   M    0      2       3 4414   86.00   M    1      2       3 4415   58.00   M    7      2       2 4416   80.00   M    1      2       3 4417   66.00   M   12      2       3 4418   80.00   M    3      2       3 4419   91.00   F   14      2       3 4420   57.00   M   13      2       2 4421   68.00   F    1      2       3 4422   75.00   M   12      2       3 4423   72.00   M    7      2       3 4424   76.00   M    6      2       3 4425   70.00   M   12      2       3 4426   70.00   M   11      2       3 4427   75.00   M   10      2       3 4428   56.00   F    9      2       2 4429   68.00   M   10      2       3 4430   70.00   M    8      2       3 4431   59.00   M    9      2       2 4432   52.00   M    6      2       2 4433   54.00   F    0      2       2 4434   75.00   M    7      2       3 4435   63.00   M   10      2       3 4436   60.00   M    1      2       3 4437   45.00   M    8      2       2 4438   75.00   M    5      2       3 4439   69.00   M    6      2       3 4440   70.00   M    9      2       3 4441   77.00   F    7      2       3 4442   62.00   M    6      2       3 4443   65.00   M    4      2       3 4444   64.00   M    2      2       3 4445   58.00   M    8      2       2 4446   56.00   M    4      2       2 4447   70.00   M    9      2       3 4448   49.00   M    3      2       2 4449   33.00   M    6      2       2 4450   84.00   M    5      2       3 4451   82.00   M    6      2       3 4452   43.00   M    5      2       2 4453   66.00   F    4      2       3 4454   60.00   M    5      2       3 4455   67.00   M    2      2       3 4456   82.00   M    2      2       3 4457   40.00   F    3      2       2 4458   70.00   F    3      2       3 4459   78.00   F    4      2       3 4460   60.00   F    2      2       3 4461   41.00   M    0      2       2 4462   78.00   M    3      2       3 4463   67.00   M    1      2       3 4464   42.00   M    0      2       2 4465   58.00   F    1      2       2 4466   60.00   M    0      2       3 4467   75.00   F    0      2       3 4468   55.00   F    0      2       2 4469   85.00   M    3      2       3 4470   64.00   F    4      2       3 4471   56.00   M    1      2       2 4472   38.00   M    2      2       2 4473   68.00   F    3      2       3 4474   65.00   M    0      2       3 4475   78.00   F    2      2       3 4476   74.00   F    1      2       3 4477   78.00   M    1      2       3 4478   67.00   M    1      2       3 4479   50.00   M    1      2       2 4480   84.00   F    1      2       3 4481   57.00   M    5      2       2 4482   45.00   M    0      2       2 4483   69.00   M    0      2       3 4484   55.00   F    2      2       2 4485   83.00   M    0      2       3 4486   48.00   M    1      2       2 4487   44.00   M    0      2       2 4488   63.00   M    0      2       3 4489   48.00   M    2      2       2 4490   80.00   M    0      2       3 4491   58.00   M    1      2       2 4492   72.00   M   18      2       3 4493   52.00   M    1      2       2 4494   47.00   M    2      2       2 4495   85.00   M    3      2       3 4496   65.00   M    4      2       3 4497   45.00   M    1      2       2 4498   49.00   F    2      2       2 4499   45.00   F    2      2       2 4500   43.00   M    1      2       2 4501   40.00   M    0      2       2 4502   65.00   M    0      2       3 4503   65.00   M    1      2       3 4504   63.00   F    3      2       3 4505   55.00   M    1      2       2 4506   65.00   F    2      2       3 4507   60.00   M   16      2       3 4508   54.00   M    2      2       2 4509   28.00   F   15      2       2 4510   68.00   F    5      2       3 4511   45.00   M    3      2       2 4512   55.00   M    0      2       2 4513   50.00   M   10      2       2 4514   55.00   M   10      2       2 4515   76.00   F    9      2       3 4516   60.00   M    2      2       3 4517   96.00   M   10      2       3 4518   72.00   M    1      2       3 4519   49.00   M    5      2       2 4520   56.00   M   13      2       2 4521   65.00   F    7      2       3 4522   70.00   F    8      2       3 4523   61.00   M    4      2       3 4524   35.00   M   10      2       2 4525   70.00   M    9      2       3 4526   65.00   F    3      2       3 4527   80.00   M    9      2       3 4528   32.00   M   10      2       2 4529   70.00   M    4      2       3 4530   76.00   M    4      2       3 4531   42.00   M    1      2       2 4532   55.00   F    4      2       2 4533   55.00   F    2      2       2 4534   55.00   M    1      2       2 4535   61.00   F    3      2       3 4536   48.00   M    1      2       2 4537   64.00   M    5      2       3 4538   74.00   M    5      2       3 4539   60.00   M    4      2       3 4540   80.00   F    2      2       3 4541   65.00   F    0      2       3 4542   60.00   M    6      2       3 4543   50.00   F    3      2       2 4544   54.00   M    1      2       2 4545   80.00   M    5      2       3 4546   55.00   M    4      2       2 4547   60.00   F    3      2       3 4548   66.00   M    3      2       3 4549   44.00   M    1      2       2 4550   86.00   F    1      2       3 4551   45.00   M    0      2       2 4552   73.00   M    1      2       3 4553   60.00   M    1      2       3 4554   38.00   F    0      2       2 4555   51.00   F    0      2       2 4556   52.00   F   17      2       2 4557   72.00   M    2      2       3 4558   70.00   M    1      2       3 4559   55.00   M    0      2       2 4560   60.00   F    0      2       3 4561   65.00   F    0      2       3 4562   60.00   M    0      2       3 4563   45.00   F    5      2       2 4564   74.00   M    0      2       3 4565   75.00   M    2      2       3 4566   63.00   M    0      2       3 4567   70.00   M    0      2       3 4568   58.00   M    1      2       2 4569   39.00   M    0      2       2 4570   60.00   M    1      2       3 4571   60.00   M    1      2       3 4572   89.00   F    1      2       3 4573   58.00   M    1      2       2 4574   77.00   F    3      2       3 4575   35.00   M   11      2       2 4576   65.00   M    0      2       3 4577   74.00   M   13      2       3 4578   56.00   M   11      2       2 4579   80.00   M    1      2       3 4580   51.00   M    2      2       2 4581   82.00   M   16      2       3 4582   76.00   F    1      2       3 4583   82.00   M   13      2       3 4584   31.00   M   11      2       2 4585   60.00   F    9      2       3 4586   78.00   F   10      2       3 4587   55.00   M    6      2       2 4588   76.00   M    8      2       3 4589   75.00   M    5      2       3 4590   60.00   M    0      2       3 4591   65.00   M    2      2       3 4592   45.00   M   10      2       2 4593   57.00   M   10      2       2 4594   74.00   M    6      2       3 4595   72.00   F   10      2       3 4596   40.00   M   10      2       2 4597   48.00   F    5      2       2 4598   60.00   M    4      2       3 4599   40.00   M    7      2       2 4600   65.00   F   10      2       3 4601   59.00   M    3      2       2 4602   32.00   M    1      2       2 4603   25.00   M    0      2       2 4604   70.00   M    7      2       3 4605   71.00   F    3      2       3 4606   57.00   M    1      2       2 4607   60.00   F    5      2       3 4608   65.00   M   19      2       3 4609   60.00   F   13      2       3 4610   92.00   F    8      2       3 4611   68.00   F    7      2       3 4612   50.00   M    7      2       2 4613   56.00   M    1      2       2 4614   47.00   M    5      2       2 4615   71.00   F    9      2       3 4616   54.00   F    8      2       2 4617   45.00   M    1      2       2 4618   38.00   M    9      2       2 4619   66.00   M    2      2       3 4620   72.00   M    6      2       3 4621   72.00   F    0      2       3 4622   65.00   F    3      2       3 4623   69.00   M    7      2       3 4624   68.00   M    4      2       3 4625   55.00   M    1      2       2 4626   78.00   M    6      2       3 4627   60.00   M    3      2       3 4628   61.00   F    6      2       3 4629   40.00   M    3      2       2 4630   67.00   M    0      2       3 4631   60.00   M    1      2       3 4632   40.00   F    7      2       2 4633   73.00   M    6      2       3 4634   88.00   F    5      2       3 4635   40.00   F    9      2       2 4636   35.00   F    4      2       2 4637   55.00   F    1      2       2 4638   75.00   F    1      2       3 4639   62.00   F    5      2       3 4640   65.00   F    7      2       3 4641   65.00   M    4      2       3 4642   73.00   M    2      2       3 4643   55.00   M    1      2       2 4644   57.00   M    7      2       2 4645   75.00   M    6      2       3 4646   58.00   M    7      2       2 4647   70.00   M    2      2       3 4648   46.00   M    2      2       2 4649   57.00   F    9      2       2 4650   49.00   F    8      2       2 4651   55.00   M    6      2       2 4652   51.00   F    4      2       2 4653   37.00   M    3      2       2 4654   46.00   M    0      2       2 4655   39.00   M    3      2       2 4656   55.00   M    1      2       2 4657   65.00   F    4      2       3 4658   40.00   F    0      2       2 4659   53.00   F    1      2       2 4660   38.00   F    1      2       2 4661   50.00   F    1      2       2 4662   85.00   M    1      2       3 4663   55.00   M    0      2       2 4664   60.00   F    1      2       3 4665   57.00   M    2      2       2 4666   50.00   F    2      2       2 4667   55.00   M    0      2       2 4668   58.00   F    3      2       2 4669   57.00   M    5      2       2 4670   58.00   F    0      2       2 4671   61.00   M    8      2       3 4672   63.00   F    2      2       3 4673   85.00   F    2      2       3 4674   77.00   F    2      2       3 4675   61.00   F    2      2       3 4676   55.00   M    0      2       2 4677   65.00   M    2      2       3 4678   42.00   M    3      2       2 4679   75.00   F    2      2       3 4680   60.00   F    3      2       3 4681   64.00   F    5      2       3 4682   65.00   M    0      2       3 4683   58.00   F    4      2       2 4684   65.00   M    0      2       3 4685   44.00   M    0      2       2 4686   85.00   M    0      2       3 4687   55.00   F    4      2       2 4688   26.00   M    1      2       2 4689   50.00   M    0      2       2 4690   54.00   M    0      2       2 4691   45.00   M    1      2       2 4692   62.00   M    0      2       3 4693   85.00   M    0      2       3 4694   85.00   F    0      2       3 4695   72.00   F    1      2       3 4696   72.00   M    1      2       3 4697   40.00   F    0      2       2 4698   70.00   F    0      2       3 4699   64.00   M    0      2       3 4700   60.00   F    0      2       3 4701   70.00   M    0      2       3 4702   75.00   M    1      2       3 4703   73.00   M    0      2       3 4704   54.00   M   14      2       2 4705   55.00   M   17      2       2 4706   69.00   F    4      2       3 4707   34.00   M   23      2       2 4708   38.00   F   19      2       2 4709   58.00   M    9      2       2 4710   61.00   M   17      2       3 4711   64.00   M    6      2       3 4712   63.00   F    2      2       3 4713   50.00   M    8      2       2 4714   64.00   M   13      2       3 4715   54.00   F   32      2       2 4716   86.00   M    7      2       3 4717   64.00   F    3      2       3 4718   71.00   M    1      2       3 4719   60.00   M   12      2       3 4720   70.00   M    1      2       3 4721   45.00   F   13      2       2 4722   35.00   M   13      2       2 4723   70.00   M   20      2       3 4724   72.00   M    1      2       3 4725   64.00   M    3      2       3 4726   53.00   M    0      2       2 4727   57.00   F    1      2       2 4728   58.00   M    1      2       2 4729   62.00   M    1      2       3 4730   71.00   M    5      2       3 4731   57.00   M   11      2       2 4732   50.00   F    8      2       2 4733   72.00   M    6      2       3 4734   65.00   M    6      2       3 4735   55.00   M   13      2       2 4736   50.00   M   10      2       2 4737   57.00   M    5      2       2 4738   54.00   F    7      2       2 4739   80.00   M    7      2       3 4740   40.00   M    5      2       2 4741   79.00   M    4      2       3 4742   56.00   M    0      2       2 4743   49.00   F    3      2       2 4744   46.00   M    0      2       2 4745   62.00   M    7      2       3 4746   58.00   F    7      2       2 4747   53.00   M    0      2       2 4748   45.00   M    7      2       2 4749   69.00   M   32      2       3 4750   65.00   F    8      2       3 4751   52.00   M    9      2       2 4752   67.00   M   10      2       3 4753   46.00   F    0      2       2 4754   76.00   F    2      2       3 4755   50.00   F    8      2       2 4756   60.00   F    1      2       3 4757   80.00   M    1      2       3 4758   45.00   M    0      2       2 4759   78.00   M    1      2       3 4760   72.00   M    0      2       3 4761   82.00   F    0      2       3 4762   45.00   M    0      2       2 4763   62.00   M    0      2       3 4764   55.00   F    4      2       2 4765   60.00   F    0      2       3 4766   73.00   M   10      2       3 4767   48.00   F   13      2       2 4768   42.00   M    8      2       2 4769   65.00   M    3      2       3 4770   49.00   M    0      2       2 4771   73.00   F    1      2       3 4772   78.00   M    7      2       3 4773   83.00   F    1      2       3 4774   47.00   M    0      2       2 4775   74.00   M    4      2       3 4776   42.00   M    3      2       2 4777   58.00   M   10      2       2 4778   75.00   F    0      2       3 4779   43.00   F    3      2       2 4780   61.00   M    1      2       3 4781   35.00   M    0      2       2 4782   94.00   M    5      2       3 4783   62.00   M    3      2       3 4784   50.00   M    6      2       2 4785   68.00   M    6      2       3 4786   48.00   M    8      2       2 4787   53.00   M    4      2       2 4788   60.00   F    8      2       3 4789   50.00   M    6      2       2 4790   56.00   M    2      2       2 4791   72.00   M    5      2       3 4792   60.00   F    5      2       3 4793   52.00   M    4      2       2 4794   58.00   F    6      2       2 4795   73.00   F    3      2       3 4796   53.00   F    2      2       2 4797   65.00   M    4      2       3 4798   92.00   M    1      2       3 4799   59.00   M    0      2       2 4800   61.00   M    6      2       3 4801   63.00   M    5      2       3 4802   50.00   M    6      2       2 4803   63.00   M    0      2       3 4804   84.00   M    3      2       3 4805   75.00   F    5      2       3 4806   65.00   M    3      2       3 4807   52.00   M    5      2       2 4808   42.00   M    3      2       2 4809   48.00   M    2      2       2 4810   70.00   M    5      2       3 4811   61.00   F    1      2       3 4812   56.00   M    1      2       2 4813   75.00   F    2      2       3 4814   65.00   M    1      2       3 4815   30.00   F    1      2       2 4816   78.00   M    3      2       3 4817   78.00   F    9      2       3 4818   69.00   M    1      2       3 4819   30.00   F    3      2       2 4820   62.00   F    2      2       3 4821   52.00   M    2      2       2 4822   55.00   F    6      2       2 4823   73.00   M    6      2       3 4824   95.00   M    1      2       3 4825   72.00   M    3      2       3 4826   74.00   M    0      2       3 4827   75.00   M    1      2       3 4828   55.00   M    1      2       2 4829   76.00   M    0      2       3 4830   44.00   F    0      2       2 4831   64.00   M    2      2       3 4832   58.00   M    0      2       2 4833   50.00   M    0      2       2 4834   59.00   F    1      2       2 4835   45.00   F    0      2       2 4836   60.00   F    0      2       3 4837   67.00   M    0      2       3 4838   70.00   M    1      2       3 4839   67.00   M    3      2       3 4840   80.00   M    3      2       3 4841   66.00   M    2      2       3 4842   87.00   M    1      2       3 4843   50.00   M    1      2       2 4844   64.00   M    0      2       3 4845   61.00   M    0      2       3 4846   58.00   M    0      2       2 4847   80.00   F    0      2       3 4848   56.00   M    1      2       2 4849   38.00   M    2      2       2 4850   96.00   F    0      2       3 4851   52.00   M    0      2       2 4852   60.00   M    4      2       3 4853   56.00   M    8      2       2 4854   55.00   F    0      2       2 4855   75.00   F   11      2       3 4856   45.00   F    9      2       2 4857   81.00   M    8      2       3 4858   55.00   M   20      2       2 4859   56.00   M    7      2       2 4860   56.00   F    6      2       2 4861   33.00   M   16      2       2 4862   60.00   M   12      2       3 4863   92.00   F   14      2       3 4864   66.00   M    3      2       3 4865   58.00   F   11      2       2 4866   44.00   M   12      2       2 4867   78.00   M   11      2       3 4868   65.00   F    4      2       3 4869   32.00   M   14      2       2 4870   65.00   M    6      2       3 4871   70.00   F    3      2       3 4872   73.00   M    6      2       3 4873   60.00   F    6      2       3 4874   37.00   M    8      2       2 4875   56.00   M   12      2       2 4876   53.00   F    4      2       2 4877   88.00   M   13      2       3 4878   55.00   M   10      2       2 4879   41.00   F    0      2       2 4880   70.00   M    9      2       3 4881   31.00   M    5      2       2 4882   60.00   M    6      2       3 4883   46.00   M   11      2       2 4884   65.00   M    7      2       3 4885   60.00   F   11      2       3 4886   51.00   F    2      2       2 4887   79.00   M   10      2       3 4888   62.00   M    4      2       3 4889   72.00   M   12      2       3 4890   70.00   M    9      2       3 4891   67.00   F    4      2       3 4892   42.00   M    4      2       2 4893   65.00   F    3      2       3 4894   40.00   M    7      2       2 4895   81.00   F    7      2       3 4896   69.00   F   10      2       3 4897   50.00   F    7      2       2 4898   84.00   M    6      2       3 4899   65.00   M    6      2       3 4900   66.00   M    6      2       3 4901   31.00   M    7      2       2 4902   46.00   M    8      2       2 4903   60.00   M    5      2       3 4904   63.00   F    2      2       3 4905   58.00   F    5      2       2 4906   70.00   F    1      2       3 4907   50.00   F    0      2       2 4908   49.00   M    8      2       2 4909   62.00   M    0      2       3 4910   36.00   M    0      2       2 4911   78.00   F    7      2       3 4912   66.00   M    5      2       3 4913   67.00   M    3      2       3 4914   63.00   M    7      2       3 4915   75.00   M    6      2       3 4916   58.00   M    2      2       2 4917   76.00   M    4      2       3 4918   70.00   F    1      2       3 4919   72.00   M    3      2       3 4920   73.00   M    7      2       3 4921   61.00   M    5      2       3 4922   57.00   M    4      2       2 4923   64.00   M    1      2       3 4924   64.00   F    1      2       3 4925   58.00   M    6      2       2 4926   65.00   M    0      2       3 4927   60.00   M    0      2       3 4928   58.00   M    4      2       2 4929   70.00   M    5      2       3 4930   88.00   F    1      2       3 4931   67.00   M   12      2       3 4932   60.00   M    0      2       3 4933   78.00   F    1      2       3 4934   75.00   F    1      2       3 4935   65.00   M    2      2       3 4936   24.00   M    5      2       2 4937   67.00   M    1      2       3 4938   65.00   F    0      2       3 4939   55.00   M    0      2       2 4940   40.00   M    0      2       2 4941   65.00   M    1      2       3 4942   55.00   M    3      2       2 4943   58.00   M    3      2       2 4944   62.00   M    1      2       3 4945   49.00   M    0      2       2 4946   71.00   M    1      2       3 4947   70.00   M    0      2       3 4948   57.00   M    0      2       2 4949   65.00   F    2      2       3 4950   55.00   M    1      2       2 4951   85.00   M    1      2       3 4952   60.00   M    4      2       3 4953   60.00   F    2      2       3 4954   60.00   M    7      2       3 4955   58.00   M    1      2       2 4956   57.00   M    0      2       2 4957   65.00   M    6      2       3 4958   75.00   F    2      2       3 4959   55.00   M    1      2       2 4960   65.00   F    3      2       3 4961   65.00   F    0      2       3 4962   50.00   M    0      2       2 4963   75.00   F    1      2       3 4964   45.00   F    1      2       2 4965   56.00   M    0      2       2 4966   46.00   M    1      2       2 4967   65.00   F    3      2       3 4968   54.00   M    5      2       2 4969   32.00   F    1      2       2 4970   60.00   F    1      2       3 4971   68.00   M    6      2       3 4972   70.00   M    1      2       3 4973   35.00   F    0      2       2 4974   68.00   M    2      2       3 4975   62.00   F    0      2       3 4976   75.00   M    1      2       3 4977   40.00   M    0      2       2 4978   73.00   M    0      2       3 4979   60.00   F    0      2       3 4980   52.00   F    0      2       2 4981   55.00   F    0      2       2 4982   36.00   M    0      2       2 4983   30.00   M    2      2       2 4984   70.00   M    1      2       3 4985   59.00   M    8      2       2 4986   77.00   M    0      2       3 4987   69.00   M    5      2       3 4988   60.00   M   14      2       3 4989   53.00   F    2      2       2 4990   54.00   F   24      2       2 4991   82.00   M    5      2       3 4992   44.00   M    7      2       2 4993   55.00   F    6      2       2 4994   54.00   F    4      2       2 4995   45.00   F    3      2       2 4996   75.00   M    8      2       3 4997   45.00   F    8      2       2 4998   76.00   F    3      2       3 4999   66.00   M   14      2       3 5000   75.00   M    3      2       3 5001   48.00   M   14      2       2 5002   54.00   M   10      2       2 5003   63.00   F    0      2       3 5004   55.00   F    6      2       2 5005   69.00   F    5      2       3 5006   85.00   M   15      2       3 5007   64.00   M    8      2       3 5008   63.00   M    0      2       3 5009   40.00   M   20      2       2 5010   57.00   M   23      2       2 5011   26.00   F    0      2       2 5012   57.00   M    8      2       2 5013   54.00   M    3      2       2 5014   73.00   F   12      2       3 5015   60.00   M    3      2       3 5016   68.00   M    4      2       3 5017   55.00   M    7      2       2 5018   34.00   M    8      2       2 5019   48.00   M    0      2       2 5020   70.00   M    5      2       3 5021   58.00   M    0      2       2 5022   66.00   F    5      2       3 5023   70.00   M    2      2       3 5024   34.00   M   17      2       2 5025   74.00   F   17      2       3 5026   67.00   M   16      2       3 5027   76.00   M    0      2       3 5028   67.00   F   13      2       3 5029   68.00   M   12      2       3 5030   56.00   M   19      2       2 5031   75.00   F   15      2       3 5032   90.00   M   14      2       3 5033   51.00   M   12      2       2 5034   33.00   F    1      2       2 5035   72.00   M   11      2       3 5036   58.00   M   12      2       2 5037   55.00   M   10      2       2 5038   75.00   F    2      2       3 5039   61.00   M   12      2       3 5040   55.00   F    1      2       2 5041   20.00   F    0      2       2 5042   56.00   F    2      2       2 5043   72.00   M   11      2       3 5044   63.00   M    4      2       3 5045   55.00   F    6      2       2 5046   78.00   M   12      2       3 5047   65.00   M    1      2       3 5048   60.00   M   11      2       3 5049   58.00   F    4      2       2 5050   70.00   M    3      2       3 5051   65.00   M    8      2       3 5052   67.00   F    9      2       3 5053   65.00   M    1      2       3 5054   86.00   M   10      2       3 5055   40.00   M    0      2       2 5056   43.00   M    0      2       2 5057   70.00   M    3      2       3 5058   65.00   F    0      2       3 5059   85.00   F    7      2       3 5060   70.00   M    1      2       3 5061   52.00   F    5      2       2 5062   80.00   F    2      2       3 5063   39.00   M    1      2       2 5064   37.00   M    5      2       2 5065   67.00   M    1      2       3 5066   70.00   M    1      2       3 5067   57.00   F    5      2       2 5068   80.00   F    6      2       3 5069   74.00   F    7      2       3 5070   24.00   M    4      2       2 5071   50.00   F    7      2       2 5072   57.00   M    6      2       2 5073   46.00   M    3      2       2 5074   68.00   M    3      2       3 5075   68.00   M    1      2       3 5076   32.00   M    8      2       2 5077   75.00   M    3      2       3 5078   28.00   F    4      2       2 5079   44.00   M    3      2       2 5080   57.00   M    0      2       2 5081   53.00   F    5      2       2 5082   55.00   F    5      2       2 5083   67.00   M    0      2       3 5084   71.00   M    3      2       3 5085   50.00   M    4      2       2 5086   60.00   M    0      2       3 5087   76.00   M    0      2       3 5088   70.00   F    0      2       3 5089   64.00   F    1      2       3 5090   74.00   F    4      2       3 5091   55.00   M    2      2       2 5092   47.00   M    1      2       2 5093   72.00   M    1      2       3 5094   63.00   M    1      2       3 5095   50.00   M    5      2       2 5096   55.00   F    3      2       2 5097   57.00   M    1      2       2 5098   50.00   M    0      2       2 5099   64.00   M    0      2       3 5100   61.00   M    5      2       3 5101   60.00   M    3      2       3 5102   50.00   M    2      2       2 5103   58.00   F    0      2       2 5104   73.00   F    2      2       3 5105   73.00   M    1      2       3 5106   80.00   F    2      2       3 5107   75.00   F    0      2       3 5108   60.00   F    1      2       3 5109   58.00   M    0      2       2 5110   52.00   M    1      2       2 5111   32.00   M   10      2       2 5112   39.00   F    0      2       2 5113   60.00   M    0      2       3 5114   73.00   F    0      2       3 5115   68.00   M    0      2       3 5116   72.00   M    1      2       3 5117   74.00   F    1      2       3 5118   70.00   F    0      2       3 5119   62.00   M    0      2       3 5120   56.00   M    1      2       2 5121   99.00   M    1      2       3 5122   70.00   F    0      2       3 5123   70.00   M    1      2       3 5124   82.00   M    0      2       3 5125   70.00   F    0      2       3 5126   66.00   M    0      2       3 5127   49.00   M    5      2       2 5128   49.00   M    8      2       2 5129   68.00   F    1      2       3 5130   41.00   M    0      2       2 5131   50.00   M    3      2       2 5132   70.00   M   17      2       3 5133   42.00   F    4      2       2 5134   54.00   F   16      2       2 5135   85.00   M   14      2       3 5136   85.00   M   17      2       3 5137   44.00   M   16      2       2 5138   63.00   F   15      2       3 5139   61.00   M    2      2       3 5140   72.00   M    6      2       3 5141   46.00   F   15      2       2 5142   68.00   M   11      2       3 5143   65.00   F   14      2       3 5144   65.00   M    3      2       3 5145   66.00   M   15      2       3 5146   88.00   F    7      2       3 5147   50.00   F    9      2       2 5148   40.00   M   10      2       2 5149   45.00   F   11      2       2 5150   55.00   M    5      2       2 5151   53.00   F    0      2       2 5152   81.00   M    0      2       3 5153   63.00   M    9      2       3 5154   53.00   M    5      2       2 5155   65.00   M    1      2       3 5156   66.00   M   10      2       3 5157   35.00   M   10      2       2 5158   55.00   M    8      2       2 5159   70.00   F    6      2       3 5160   74.00   M    6      2       3 5161   70.00   M   10      2       3 5162   50.00   F    9      2       2 5163   55.00   M    9      2       2 5164   69.00   M    9      2       3 5165   74.00   M    7      2       3 5166   73.00   M   11      2       3 5167   60.00   F   10      2       3 5168   62.00   M    4      2       3 5169   73.00   M    7      2       3 5170   75.00   M    2      2       3 5171   53.00   M    7      2       2 5172   54.00   M    2      2       2 5173   67.00   M    8      2       3 5174   35.00   M    6      2       2 5175   61.00   M   12      2       3 5176   77.00   M    0      2       3 5177   47.00   M    3      2       2 5178   67.00   M    8      2       3 5179   62.00   M    8      2       3 5180   61.00   M    7      2       3 5181   70.00   M    6      2       3 5182   53.00   M    8      2       2 5183   61.00   M    6      2       3 5184   65.00   M    2      2       3 5185   59.00   M    1      2       2 5186   66.00   M    5      2       3 5187   89.00   F    5      2       3 5188   80.00   F    5      2       3 5189   49.00   F    6      2       2 5190   73.00   M    8      2       3 5191   55.00   M    3      2       2 5192   75.00   M    6      2       3 5193   46.00   M    7      2       2 5194   60.00   F    8      2       3 5195   67.00   M    6      2       3 5196   90.00   M    0      2       3 5197   40.00   F    9      2       2 5198   83.00   F    5      2       3 5199   44.00   M    6      2       2 5200   60.00   F    7      2       3 5201   66.00   F    1      2       3 5202   57.00   M    7      2       2 5203   59.00   M    4      2       2 5204   60.00   M    1      2       3 5205   85.00   M    7      2       3 5206   70.00   M    5      2       3 5207   36.00   M    6      2       2 5208   37.00   M    1      2       2 5209   74.00   F    4      2       3 5210   79.00   F    6      2       3 5211   68.00   M    4      2       3 5212   55.00   F    1      2       2 5213   78.00   M    1      2       3 5214   62.00   F   15      2       3 5215   86.00   M    0      2       3 5216   43.00   M   10      2       2 5217   58.00   M    1      2       2 5218   62.00   M    0      2       3 5219   64.00   F    0      2       3 5220   74.00   M    0      2       3 5221   62.00   M    3      2       3 5222   31.00   M    6      2       2 5223   59.00   M    5      2       2 5224   76.00   M    0      2       3 5225   65.00   M    1      2       3 5226   48.00   F    4      2       2 5227   45.00   F    4      2       2 5228   68.00   F    3      2       3 5229   65.00   M    2      2       3 5230   45.00   F    3      2       2 5231   70.00   M    1      2       3 5232   44.00   M    1      2       2 5233   56.00   M    2      2       2 5234   65.00   M    5      2       3 5235   65.00   M    3      2       3 5236   68.00   F    2      2       3 5237   55.00   M    2      2       2 5238   70.00   M    1      2       3 5239   68.00   M    0      2       3 5240   58.00   F    1      2       2 5241   48.00   M    1      2       2 5242   68.00   F    2      2       3 5243   36.00   M    3      2       2 5244   66.00   M   13      2       3 5245   58.00   F    1      2       2 5246   63.00   M    1      2       3 5247   75.00   M    0      2       3 5248   59.00   F    1      2       2 5249   65.00   F    1      2       3 5250   55.00   M    2      2       2 5251   45.00   F    2      2       2 5252   75.00   M    1      2       3 5253   58.00   M    1      2       2 5254   63.00   F    3      2       3 5255   73.00   F    2      2       3 5256   80.00   F    0      2       3 5257   63.00   M    0      2       3 5258   63.00   F    3      2       3 5259   44.00   M    0      2       2 5260   63.00   M    1      2       3 5261   58.00   M    1      2       2 5262   54.00   M    2      2       2 5263   70.00   F    0      2       3 5264   60.00   M   27      2       3 5265   57.00   M    4      2       2 5266   63.00   M   23      2       3 5267   78.00   M   18      2       3 5268   58.00   M   16      2       2 5269   70.00   M   15      2       3 5270   55.00   F   15      2       2 5271   56.00   M    4      2       2 5272   80.00   M    5      2       3 5273   55.00   M   15      2       2 5274   60.00   M    9      2       3 5275   44.00   M   14      2       2 5276   55.00   M   10      2       2 5277   78.00   M   11      2       3 5278   47.00   M   15      2       2 5279   70.00   M    0      2       3 5280   63.00   M    9      2       3 5281   72.00   F    2      2       3 5282   65.00   M    2      2       3 5283   65.00   M   11      2       3 5284   74.00   F    1      2       3 5285   63.00   M   13      2       3 5286   50.00   M    8      2       2 5287   52.00   M    8      2       2 5288   84.00   M    8      2       3 5289   59.00   M    3      2       2 5290   78.00   F    7      2       3 5291   64.00   F    8      2       3 5292   68.00   F   11      2       3 5293   68.00   F    6      2       3 5294   60.00   F    7      2       3 5295   65.00   M    9      2       3 5296   47.00   M    8      2       2 5297   60.00   F    6      2       3 5298   42.00   F   10      2       2 5299   64.00   F    7      2       3 5300   47.00   F    3      2       2 5301   72.00   M    9      2       3 5302   70.00   M    6      2       3 5303   68.00   M   11      2       3 5304   64.00   F    7      2       3 5305   59.00   M    3      2       2 5306   72.00   M    5      2       3 5307   75.00   M    2      2       3 5308   59.00   F    1      2       2 5309   50.00   M   10      2       2 5310   51.00   M    5      2       2 5311   65.00   M   12      2       3 5312   81.00   M    3      2       3 5313   50.00   F    1      2       2 5314   70.00   M    4      2       3 5315   70.00   M    5      2       3 5316   36.00   M    6      2       2 5317   66.00   M    4      2       3 5318   62.00   M    7      2       3 5319   63.00   M    0      2       3 5320   74.00   M    5      2       3 5321   65.00   M    1      2       3 5322   67.00   M    1      2       3 5323   57.00   M    0      2       2 5324   65.00   M    8      2       3 5325   70.00   M    4      2       3 5326   70.00   M    2      2       3 5327   50.00   F    1      2       2 5328   75.00   M    3      2       3 5329   75.00   F    0      2       3 5330   76.00   M    5      2       3 5331   75.00   M    2      2       3 5332   39.00   F    3      2       2 5333   64.00   M   23      2       3 5334   71.00   M    0      2       3 5335   44.00   F    4      2       2 5336   80.00   M    1      2       3 5337   70.00   M    1      2       3 5338   45.00   M    0      2       2 5339   53.00   M    3      2       2 5340   60.00   M    0      2       3 5341   72.00   F    4      2       3 5342   61.00   M    3      2       3 5343   75.00   F    0      2       3 5344   76.00   M    1      2       3 5345   62.00   F    1      2       3 5346   69.00   M    1      2       3 5347   37.00   M    1      2       2 5348   47.00   M    0      2       2 5349   80.00   M    0      2       3 5350   32.00   M    1      2       2 5351   80.00   M    5      2       3 5352   42.00   M    1      2       2 5353   63.00   M    2      2       3 5354   55.00   M    0      2       2 5355   70.00   M    1      2       3 5356   56.00   M    0      2       2 5357   70.00   M    0      2       3 5358   69.00   M    0      2       3 5359   78.00   F    2      2       3 5360   74.00   F    2      2       3 5361   80.00   M    0      2       3 5362   39.00   M    1      2       2 5363   60.00   F   13      2       3 5364   70.00   F    2      2       3 5365   70.00   M    2      2       3 5366   48.00   F    2      2       2 5367   50.00   M    9      2       2 5368   14.00   M    0      2       1 5369   17.00   F    0      2       1 5370   70.00   M    1      2       3 5371   44.00   F    1      2       2 5372   66.00   M    0      2       3 5373   34.00   M    0      2       2 5374   72.00   M    1      2       3 5375   50.00   M    0      2       2 5376   60.00   M    0      2       3 5377   70.00   F    1      2       3 5378   60.00   M   15      2       3 5379   43.00   M   10      2       2 5380   60.00   F   17      2       3 5381   68.00   M   20      2       3 5382   65.00   M   11      2       3 5383   59.00   M    8      2       2 5384   85.00   M    3      2       3 5385   56.00   M   22      2       2 5386   54.00   F   17      2       2 5387   58.00   F    6      2       2 5388   50.00   M    2      2       2 5389   60.00   M   13      2       3 5390   68.00   M   13      2       3 5391   60.00   F    2      2       3 5392   69.00   F    0      2       3 5393   50.00   M   15      2       2 5394   50.00   M    0      2       2 5395   84.00   M   18      2       3 5396   54.00   M    3      2       2 5397   73.00   M    3      2       3 5398   60.00   M   12      2       3 5399   94.00   F   17      2       3 5400   63.00   F   19      2       3 5401   65.00   F   12      2       3 5402   75.00   F    1      2       3 5403   60.00   M   14      2       3 5404   58.00   F    1      2       2 5405   75.00   M    1      2       3 5406   76.00   M    8      2       3 5407   55.00   F    2      2       2 5408   68.00   F    0      2       3 5409   60.00   M    4      2       3 5410   65.00   M   11      2       3 5411   50.00   F    0      2       2 5412   62.00   M   11      2       3 5413   65.00   F    2      2       3 5414   69.00   M    0      2       3 5415   37.00   M    6      2       2 5416   60.00   M   10      2       3 5417   48.00   F   16      2       2 5418   44.00   M    3      2       2 5419   80.00   F    6      2       3 5420   49.00   F    9      2       2 5421   62.00   F    7      2       3 5422   79.00   M    1      2       3 5423   49.00   M   10      2       2 5424   53.00   F    0      2       2 5425   91.00   F    6      2       3 5426   52.00   F    5      2       2 5427   75.00   M    4      2       3 5428   59.00   F    6      2       2 5429   65.00   M    0      2       3 5430   35.00   F    2      2       2 5431   43.00   M    5      2       2 5432   48.00   M    6      2       2 5433   59.00   F    0      2       2 5434   40.00   F    4      2       2 5435   60.00   M    0      2       3 5436   60.00   M   10      2       3 5437   65.00   F    5      2       3 5438   73.00   M    5      2       3 5439   48.00   M    2      2       2 5440   35.00   M    5      2       2 5441   70.00   F    4      2       3 5442   63.00   M    3      2       3 5443   62.00   F    2      2       3 5444   68.00   M    2      2       3 5445   70.00   F    4      2       3 5446   45.00   M    4      2       2 5447   72.00   F    4      2       3 5448   65.00   M    3      2       3 5449   73.00   M    3      2       3 5450   60.00   F    1      2       3 5451   44.00   M    4      2       2 5452   55.00   M    3      2       2 5453   63.00   M    0      2       3 5454   70.00   M    3      2       3 5455   60.00   M    2      2       3 5456   39.00   M    3      2       2 5457   31.00   M    0      2       2 5458   49.00   M    0      2       2 5459   58.00   M    2      2       2 5460   69.00   M    2      2       3 5461   48.00   M    0      2       2 5462   60.00   F    2      2       3 5463   75.00   M    5      2       3 5464   58.00   M    9      2       2 5465   51.00   M    3      2       2 5466   78.00   M    3      2       3 5467   55.00   F    1      2       2 5468   64.00   M    0      2       3 5469   88.00   F    1      2       3 5470   64.00   M    1      2       3 5471   60.00   M    0      2       3 5472   50.00   M    1      2       2 5473   60.00   F    0      2       3 5474   38.00   M    1      2       2 5475   63.00   M    1      2       3 5476   50.00   M    1      2       2 5477   50.00   F    1      2       2 5478   68.00   M    2      2       3 5479   80.00   M    1      2       3 5480   35.00   M    1      2       2 5481   73.00   M    1      2       3 5482   82.00   M    2      2       3 5483   48.00   M   27      2       2 5484   60.00   M   17      2       3 5485   69.00   M   14      2       3 5486   62.00   M   10      2       3 5487   58.00   M   18      2       2 5488   76.00   M    9      2       3 5489   63.00   M   15      2       3 5490   35.00   F   22      2       2 5491   39.00   F   16      2       2 5492   75.00   M   10      2       3 5493   55.00   F   17      2       2 5494   60.00   F   12      2       3 5495   67.00   F   13      2       3 5496   60.00   F    3      2       3 5497   58.00   F    6      2       2 5498   70.00   M    3      2       3 5499   65.00   M    0      2       3 5500   80.00   M   11      2       3 5501   67.00   M   12      2       3 5502   48.00   M   15      2       2 5503   55.00   F    5      2       2 5504   63.00   F    4      2       3 5505   55.00   M   10      2       2 5506   68.00   F    7      2       3 5507   40.00   F   10      2       2 5508   90.00   F    8      2       3 5509   73.00   M    8      2       3 5510   72.00   M    8      2       3 5511   62.00   F   12      2       3 5512   84.00   M    8      2       3 5513   74.00   M   14      2       3 5514   60.00   M    6      2       3 5515   48.00   F    7      2       2 5516   58.00   M    5      2       2 5517   70.00   F   10      2       3 5518   80.00   M    6      2       3 5519   65.00   M    6      2       3 5520   41.00   F    7      2       2 5521   52.00   M    2      2       2 5522   84.00   M    6      2       3 5523   48.00   M    0      2       2 5524   56.00   F    5      2       2 5525   61.00   F    3      2       3 5526   60.00   M    2      2       3 5527   55.00   M    6      2       2 5528   65.00   M    4      2       3 5529   58.00   F    3      2       2 5530   65.00   M    5      2       3 5531   73.00   F    5      2       3 5532   60.00   M    4      2       3 5533   68.00   F    2      2       3 5534   49.00   F    0      2       2 5535   77.00   M   15      2       3 5536   65.00   F    6      2       3 5537   25.00   M   11      2       2 5538   70.00   F    1      2       3 5539   63.00   F    1      2       3 5540   70.00   F    2      2       3 5541   60.00   M    2      2       3 5542   47.00   M    7      2       2 5543   89.00   M    7      2       3 5544   68.00   F    2      2       3 5545   68.00   M    4      2       3 5546   53.00   M    9      2       2 5547   75.00   M    4      2       3 5548   41.00   M    0      2       2 5549   74.00   M    0      2       3 5550   65.00   M    5      2       3 5551   70.00   M    0      2       3 5552   40.00   M    2      2       2 5553   47.00   M    2      2       2 5554   55.00   M    2      2       2 5555   65.00   M    2      2       3 5556   66.00   M    4      2       3 5557   53.00   F    0      2       2 5558   61.00   M    2      2       3 5559   80.00   F    2      2       3 5560   75.00   M    2      2       3 5561   52.00   F    2      2       2 5562   87.00   M    3      2       3 5563   64.00   M    0      2       3 5564   58.00   M    1      2       2 5565   67.00   F    1      2       3 5566   50.00   F    4      2       2 5567   47.00   F    1      2       2 5568   55.00   F    0      2       2 5569   69.00   F    0      2       3 5570   60.00   M    0      2       3 5571   71.00   M    1      2       3 5572   48.00   M    4      2       2 5573   85.00   M    0      2       3 5574   87.00   M    0      2       3 5575   92.00   M    3      2       3 5576   82.00   M    2      2       3 5577   80.00   F    0      2       3 5578   60.00   F    2      2       3 5579   43.00   M    1      2       2 5580   72.00   M    2      2       3 5581   79.00   F    1      2       3 5582   82.00   M    0      2       3 5583   65.00   M    1      2       3 5584   67.00   M    2      2       3 5585   54.00   M    0      2       2 5586   63.00   M    1      2       3 5587   65.00   F    1      2       3 5588   56.00   M    0      2       2 5589   35.00   M    0      2       2 5590   84.00   M    4      2       3 5591   52.00   M    2      2       2 5592   50.00   F    0      2       2 5593   62.00   F    1      2       3 5594   73.00   M    0      2       3 5595   69.00   M    1      2       3 5596   40.00   F    3      2       2 5597   44.00   M    3      2       2 5598   62.00   M    1      2       3 5599   76.00   F   18      2       3 5600   46.00   M   17      2       2 5601   63.00   F   19      2       3 5602   38.00   F    4      2       2 5603   70.00   M    0      2       3 5604   45.00   M    0      2       2 5605   59.00   M   50      2       2 5606   75.00   M   11      2       3 5607   60.00   F   18      2       3 5608   78.00   F   41      2       3 5609   70.00   M    4      2       3 5610   60.00   M    1      2       3 5611   60.00   M    0      2       3 5612   80.00   M    0      2       3 5613   50.00   F   22      2       2 5614   53.00   M    1      2       2 5615   57.00   M   21      2       2 5616   45.00   F    1      2       2 5617   61.00   M    5      2       3 5618   60.00   M    4      2       3 5619   70.00   M    3      2       3 5620   87.00   F    7      2       3 5621   50.00   M    8      2       2 5622   64.00   M   21      2       3 5623   52.00   M    0      2       2 5624   56.00   M    9      2       2 5625   68.00   M    1      2       3 5626   75.00   F    9      2       3 5627   27.00   M   10      2       2 5628   64.00   M    7      2       3 5629   75.00   F    5      2       3 5630   68.00   M   17      2       3 5631   51.00   M   13      2       2 5632   58.00   M   16      2       2 5633   39.00   F   10      2       2 5634   58.00   M   11      2       2 5635   58.00   M    2      2       2 5636   63.00   M   14      2       3 5637   46.00   F   30      2       2 5638   61.00   M   14      2       3 5639   45.00   M   13      2       2 5640   62.00   F    6      2       3 5641   52.00   F    0      2       2 5642   65.00   M    0      2       3 5643   75.00   M    0      2       3 5644   59.00   F   10      2       2 5645   60.00   M   11      2       3 5646   55.00   M    1      2       2 5647   70.00   F   10      2       3 5648   60.00   F    5      2       3 5649   69.00   M   17      2       3 5650   68.00   F    8      2       3 5651   78.00   F    0      2       3 5652   78.00   M    8      2       3 5653   65.00   F   13      2       3 5654   75.00   M    8      2       3 5655   30.00   F   11      2       2 5656   85.00   F    2      2       3 5657   72.00   F    7      2       3 5658   89.00   M    3      2       3 5659   48.00   M   14      2       2 5660   65.00   M    0      2       3 5661   42.00   M    0      2       2 5662   82.00   M    7      2       3 5663   55.00   M    0      2       2 5664   28.00   F    1      2       2 5665   60.00   M    2      2       3 5666   65.00   F    5      2       3 5667   45.00   M    3      2       2 5668   73.00   F    5      2       3 5669   70.00   M    8      2       3 5670   70.00   M    9      2       3 5671   60.00   M    7      2       3 5672   66.00   F    2      2       3 5673   82.00   F    7      2       3 5674   79.00   F   10      2       3 5675   67.00   M    3      2       3 5676   65.00   M    1      2       3 5677   62.00   M    3      2       3 5678   78.00   M    7      2       3 5679   73.00   M    4      2       3 5680   66.00   M    4      2       3 5681   42.00   M    4      2       2 5682   58.00   M    2      2       2 5683   65.00   M    6      2       3 5684   84.00   M    0      2       3 5685   60.00   M    4      2       3 5686   45.00   M    0      2       2 5687   65.00   M    7      2       3 5688   89.00   M    4      2       3 5689   40.00   F    1      2       2 5690   53.00   M    2      2       2 5691   60.00   M    3      2       3 5692   75.00   M    4      2       3 5693   60.00   M    4      2       3 5694   86.00   M    2      2       3 5695   30.00   M    3      2       2 5696   65.00   F    3      2       3 5697   75.00   F    1      2       3 5698   70.00   M    5      2       3 5699   50.00   M    0      2       2 5700   35.00   M    2      2       2 5701   48.00   F    2      2       2 5702   75.00   M    1      2       3 5703   72.00   M    2      2       3 5704   72.00   M    3      2       3 5705   36.00   M    2      2       2 5706   74.00   M    3      2       3 5707   90.00   F    1      2       3 5708   60.00   M    2      2       3 5709   45.00   M    3      2       2 5710   83.00   M    3      2       3 5711   58.00   M    0      2       2 5712   49.00   M    0      2       2 5713   50.00   M    0      2       2 5714   83.00   M    0      2       3 5715   48.00   F    0      2       2 5716   40.00   M    0      2       2 5717   40.00   F    0      2       2 5718   85.00   M    1      2       3 5719   75.00   M    2      2       3 5720   55.00   F    0      2       2 5721   62.00   F    1      2       3 5722   68.00   M    1      2       3 5723   70.00   M    3      2       3 5724   70.00   M    1      2       3 5725   35.00   F    0      2       2 5726   80.00   M    3      2       3 5727   71.00   M    0      2       3 5728   53.00   M    0      2       2 5729   46.00   M    1      2       2 5730   53.00   M    2      2       2 5731   50.00   M    3      2       2 5732   60.00   M    1      2       3 5733   52.00   M    0      2       2 5734   77.00   F   24      2       3 5735   70.00   M    6      2       3 5736   12.00   F    1      2       1 5737   81.00   M    2      2       3 5738   84.00   M   13      2       3 5739   51.00   M    1      2       2 5740   69.00   M   14      2       3 5741   68.00   F    6      2       3 5742   60.00   M   19      2       3 5743   66.00   F   15      2       3 5744   32.00   M   16      2       2 5745   65.00   M   14      2       3 5746   48.00   M   13      2       2 5747   13.00   M    3      2       1 5748   58.00   M    0      2       2 5749   82.00   M    0      2       3 5750   45.00   M   12      2       2 5751   72.00   M   13      2       3 5752   66.00   M   15      2       3 5753   78.00   M    2      2       3 5754   79.00   M    3      2       3 5755   67.00   M   11      2       3 5756   42.00   F    2      2       2 5757   74.00   F    7      2       3 5758   44.00   M   10      2       2 5759   70.00   M    2      2       3 5760   60.00   M   24      2       3 5761   80.00   M    6      2       3 5762   85.00   M    1      2       3 5763   75.00   M    9      2       3 5764   58.00   F    1      2       2 5765   72.00   M    8      2       3 5766   51.00   M    7      2       2 5767   70.00   M    7      2       3 5768   71.00   M    0      2       3 5769   85.00   M    5      2       3 5770   56.00   M    7      2       2 5771   71.00   M    8      2       3 5772   30.00   F    8      2       2 5773   69.00   F    1      2       3 5774   83.00   M    6      2       3 5775   69.00   M    6      2       3 5776   70.00   M    1      2       3 5777   53.00   M    1      2       2 5778   55.00   M    7      2       2 5779   29.00   M    4      2       2 5780   65.00   M    3      2       3 5781   48.00   M    8      2       2 5782   70.00   F    5      2       3 5783   78.00   F    6      2       3 5784   62.00   F    3      2       3 5785   70.00   M    9      2       3 5786   70.00   F    9      2       3 5787   49.00   M    5      2       2 5788   60.00   M    5      2       3 5789   75.00   M    5      2       3 5790   62.00   M    4      2       3 5791   72.00   M    2      2       3 5792   32.00   M    9      2       2 5793   70.00   M    5      2       3 5794   65.00   M    5      2       3 5795   87.00   M    2      2       3 5796   60.00   F    6      2       3 5797   45.00   F    5      2       2 5798   70.00   M    4      2       3 5799   55.00   M    6      2       2 5800   73.00   M    5      2       3 5801   40.00   F    2      2       2 5802   80.00   M    6      2       3 5803   46.00   F    0      2       2 5804   65.00   F    3      2       3 5805   44.00   F    3      2       2 5806   45.00   M    3      2       2 5807   55.00   M    3      2       2 5808   69.00   F    6      2       3 5809   45.00   F    4      2       2 5810   55.00   M    4      2       2 5811   29.00   M    3      2       2 5812   65.00   M    3      2       3 5813   60.00   M    5      2       3 5814   53.00   M    4      2       2 5815   60.00   F    1      2       3 5816   85.00   M    1      2       3 5817   59.00   M    2      2       2 5818   70.00   M    5      2       3 5819   50.00   M    0      2       2 5820   66.00   F    5      2       3 5821   62.00   M    2      2       3 5822   82.00   M    4      2       3 5823   84.00   M    1      2       3 5824   70.00   F    3      2       3 5825   73.00   F    4      2       3 5826   55.00   M    1      2       2 5827   65.00   F    2      2       3 5828   86.00   F    1      2       3 5829   38.00   M    0      2       2 5830   45.00   F    3      2       2 5831   57.00   M    0      2       2 5832   49.00   M    1      2       2 5833   62.00   F    1      2       3 5834   85.00   M    0      2       3 5835   81.00   M    0      2       3 5836   70.00   M    2      2       3 5837   70.00   M    1      2       3 5838   90.00   M    0      2       3 5839   50.00   M    0      2       2 5840   28.00   F    0      2       2 5841   80.00   M    0      2       3 5842   75.00   M    4      2       3 5843   40.00   F    0      2       2 5844   78.00   F   19      2       3 5845   76.00   M    0      2       3 5846   60.00   F    0      2       3 5847   61.00   M   19      2       3 5848   70.00   F   19      2       3 5849   60.00   M    1      2       3 5850   50.00   M    1      2       2 5851   68.00   F   19      2       3 5852   38.00   M   18      2       2 5853   55.00   M   16      2       2 5854   45.00   F   31      2       2 5855   60.00   M    2      2       3 5856   47.00   M   13      2       2 5857   62.00   M   13      2       3 5858   65.00   M    3      2       3 5859   57.00   F   13      2       2 5860   60.00   M   11      2       3 5861   55.00   M    4      2       2 5862   60.00   F    9      2       3 5863   75.00   F    8      2       3 5864   68.00   M    0      2       3 5865   60.00   M    2      2       3 5866   70.00   M    3      2       3 5867   45.00   F    1      2       2 5868   65.00   M    1      2       3 5869   70.00   M   10      2       3 5870   75.00   M    1      2       3 5871   65.00   M    8      2       3 5872   54.00   M    9      2       2 5873   45.00   M   10      2       2 5874   52.00   M   11      2       2 5875   64.00   M    9      2       3 5876   64.00   M   10      2       3 5877   59.00   M    5      2       2 5878   56.00   F    4      2       2 5879   65.00   M    8      2       3 5880   75.00   F    5      2       3 5881   54.00   F    5      2       2 5882   76.00   M    8      2       3 5883   59.00   M   10      2       2 5884   79.00   M    4      2       3 5885   45.00   M    7      2       2 5886   69.00   M    7      2       3 5887   35.00   M    0      2       2 5888   72.00   M    2      2       3 5889   70.00   M    0      2       3 5890   33.00   M    8      2       2 5891   48.00   M    5      2       2 5892   65.00   M    4      2       3 5893   42.00   F    3      2       2 5894   53.00   M    7      2       2 5895   65.00   M    5      2       3 5896   85.00   M    4      2       3 5897   55.00   M   13      2       2 5898   67.00   F    5      2       3 5899   49.00   F    4      2       2 5900   75.00   M    2      2       3 5901   48.00   M    4      2       2 5902   65.00   M    0      2       3 5903   57.00   F    4      2       2 5904   73.00   M    6      2       3 5905   72.00   M    2      2       3 5906   70.00   F    4      2       3 5907   46.00   M    5      2       2 5908   74.00   F    0      2       3 5909   61.00   F    4      2       3 5910   50.00   F    0      2       2 5911   65.00   F    5      2       3 5912   32.00   F    1      2       2 5913   47.00   F    3      2       2 5914   65.00   M    4      2       3 5915   68.00   M    1      2       3 5916   57.00   F    2      2       2 5917   58.00   M    7      2       2 5918   63.00   M    2      2       3 5919   65.00   M    3      2       3 5920   48.00   F    2      2       2 5921   50.00   F    1      2       2 5922   63.00   F    1      2       3 5923   56.00   F    1      2       2 5924   63.00   F   13      2       3 5925   60.00   M    1      2       3 5926   60.00   M    0      2       3 5927   78.00   M    0      2       3 5928   71.00   M    5      2       3 5929   65.00   M    2      2       3 5930   58.00   M    5      2       2 5931   50.00   F    5      2       2 5932   75.00   F    0      2       3 5933   80.00   M    0      2       3 5934   55.00   M    2      2       2 5935   68.00   F    2      2       3 5936   52.00   F    2      2       2 5937   50.00   M    0      2       2 5938   84.00   M    2      2       3 5939   45.00   M    0      2       2 5940   55.00   M    1      2       2 5941   75.00   F    0      2       3 5942   68.00   M    0      2       3 5943   70.00   M    0      2       3 5944   46.00   M    0      2       2 5945   90.00   M    0      2       3 5946   78.00   M    0      2       3 5947   70.00   F    0      2       3 5948   60.00   M    2      2       3 5949   67.00   M   20      2       3 5950   47.00   F    1      2       2 5951   55.00   M    2      2       2 5952   44.00   M   24      2       2 5953   73.00   M   24      2       3 5954   60.00   M    2      2       3 5955   62.00   M   16      2       3 5956   61.00   M    9      2       3 5957   56.00   F    1      2       2 5958   62.00   M   17      2       3 5959   62.00   M   16      2       3 5960   61.00   F   16      2       3 5961   83.00   M    8      2       3 5962   60.00   F    0      2       3 5963   74.00   M    4      2       3 5964   50.00   M    5      2       2 5965   25.00   F   13      2       2 5966   68.00   M   11      2       3 5967   68.00   F   13      2       3 5968   75.00   M    5      2       3 5969   70.00   M   10      2       3 5970   65.00   M   12      2       3 5971   60.00   M   14      2       3 5972   59.00   M   13      2       2 5973   62.00   M    8      2       3 5974   72.00   M    5      2       3 5975   42.00   F    5      2       2 5976   29.00   F    3      2       2 5977   74.00   M    1      2       3 5978   70.00   M    2      2       3 5979   42.00   M   10      2       2 5980   65.00   F   10      2       3 5981   73.00   M    2      2       3 5982   58.00   M    9      2       2 5983   72.00   F    9      2       3 5984   52.00   F   10      2       2 5985   66.00   M   10      2       3 5986   45.00   M    7      2       2 5987   45.00   M    1      2       2 5988   69.00   M    0      2       3 5989   84.00   M    7      2       3 5990   75.00   M    3      2       3 5991   65.00   M    5      2       3 5992   68.00   F   10      2       3 5993   68.00   M    7      2       3 5994   66.00   M    0      2       3 5995   62.00   F    7      2       3 5996   65.00   F    5      2       3 5997   70.00   F    0      2       3 5998   92.00   M    5      2       3 5999   70.00   M    0      2       3 6000   75.00   F    3      2       3 6001   69.00   M   12      2       3 6002   43.00   M    6      2       2 6003   58.00   M    5      2       2 6004   81.00   F    0      2       3 6005   65.00   F    0      2       3 6006   59.00   M    0      2       2 6007   91.00   M    0      2       3 6008   65.00   M    3      2       3 6009   75.00   F    2      2       3 6010   65.00   M    5      2       3 6011   77.00   M    2      2       3 6012   23.00   M    2      2       2 6013   63.00   M    0      2       3 6014   62.00   M    3      2       3 6015   79.00   M    1      2       3 6016   49.00   M    5      2       2 6017   72.00   M    0      2       3 6018   75.00   M    5      2       3 6019   70.00   F    0      2       3 6020   70.00   M    5      2       3 6021   62.00   F    3      2       3 6022   61.00   F    1      2       3 6023   45.00   F    2      2       2 6024   68.00   M    1      2       3 6025   72.00   M    2      2       3 6026   73.00   M    3      2       3 6027   58.00   M    0      2       2 6028   74.00   M    3      2       3 6029   78.00   F    5      2       3 6030   85.00   M    2      2       3 6031   71.00   M    2      2       3 6032   60.00   F    2      2       3 6033   74.00   F    3      2       3 6034   29.00   M    1      2       2 6035   64.00   F    3      2       3 6036   39.00   M    1      2       2 6037   43.00   F    2      2       2 6038   76.00   M    2      2       3 6039   60.00   M    0      2       3 6040   67.00   M    2      2       3 6041   70.00   F    1      2       3 6042   67.00   M    0      2       3 6043   50.00   M    0      2       2 6044   45.00   M    0      2       2 6045   75.00   M    3      2       3 6046   46.00   F    0      2       2 6047   61.00   F    1      2       3 6048   61.00   M    1      2       3 6049   56.00   M    0      2       2 6050   58.00   M    2      2       2 6051   65.00   F    6      2       3 6052   86.00   M    0      2       3 6053   35.00   M    1      2       2 6054   85.00   M    2      2       3 6055   55.00   F    0      2       2 6056   47.00   M    0      2       2 6057   57.00   M    0      2       2 6058   84.00   M    1      2       3 6059   79.00   M    1      2       3 6060   65.00   F    1      2       3 6061   65.00   M    0      2       3 6062   68.00   M    0      2       3 6063   60.00   F    2      2       3 6064   30.00   M    7      2       2 6065   34.00   M    0      2       2 6066   60.00   M   23      2       3 6067   29.00   M   18      2       2 6068   68.00   F    4      2       3 6069   54.00   M   19      2       2 6070   49.00   M   14      2       2 6071   70.00   M    1      2       3 6072   53.00   M   15      2       2 6073   75.00   M    5      2       3 6074   78.00   F   14      2       3 6075   70.00   M   15      2       3 6076   79.00   M   14      2       3 6077   51.00   M   11      2       2 6078   67.00   M    0      2       3 6079   49.00   M   12      2       2 6080   85.00   M   13      2       3 6081   84.00   M    0      2       3 6082   58.00   F    8      2       2 6083   50.00   F    0      2       2 6084   60.00   M    0      2       3 6085   85.00   F    7      2       3 6086   48.00   M    0      2       2 6087   70.00   M   10      2       3 6088   65.00   M    5      2       3 6089   70.00   M   11      2       3 6090   35.00   M    9      2       2 6091   45.00   M    4      2       2 6092   80.00   M    3      2       3 6093   58.00   F   12      2       2 6094   80.00   M    6      2       3 6095   78.00   M    3      2       3 6096   62.00   M    5      2       3 6097   66.00   M    5      2       3 6098   82.00   M    8      2       3 6099   58.00   M    1      2       2 6100   58.00   M    9      2       2 6101   68.00   M    7      2       3 6102   86.00   M    0      2       3 6103   17.00   M    6      2       1 6104   68.00   M    5      2       3 6105   64.00   M    4      2       3 6106   58.00   F   10      2       2 6107   62.00   M    7      2       3 6108   60.00   M    9      2       3 6109   82.00   F    3      2       3 6110   60.00   F    0      2       3 6111   55.00   F    2      2       2 6112   41.00   M    3      2       2 6113   62.00   M    1      2       3 6114   54.00   M    8      2       2 6115   66.00   F    2      2       3 6116   57.00   F    7      2       2 6117   70.00   M    6      2       3 6118   79.00   F    6      2       3 6119   70.00   F    5      2       3 6120   70.00   F    0      2       3 6121   78.00   M    7      2       3 6122   72.00   M    6      2       3 6123   52.00   F    4      2       2 6124   74.00   M    6      2       3 6125   65.00   F   10      2       3 6126   70.00   M    6      2       3 6127   68.00   F    7      2       3 6128   75.00   F    0      2       3 6129   41.00   F    3      2       2 6130   52.00   M    3      2       2 6131   65.00   M    0      2       3 6132   84.00   F    4      2       3 6133   50.00   F    5      2       2 6134   55.00   M    3      2       2 6135   57.00   M    5      2       2 6136   28.00   M    1      2       2 6137   69.00   M    6      2       3 6138   90.00   M    2      2       3 6139   61.00   M    4      2       3 6140   49.00   M    4      2       2 6141   75.00   M    4      2       3 6142   70.00   M    4      2       3 6143   60.00   M    6      2       3 6144   68.00   F    5      2       3 6145   61.00   M    2      2       3 6146   45.00   M    4      2       2 6147   62.00   M    3      2       3 6148   70.00   M    3      2       3 6149   79.00   M    3      2       3 6150   38.00   F    5      2       2 6151   60.00   M    1      2       3 6152   83.00   F   12      2       3 6153   76.00   M    0      2       3 6154   47.00   F    0      2       2 6155   67.00   M    5      2       3 6156   40.00   M    0      2       2 6157   92.00   M    2      2       3 6158   67.00   M    0      2       3 6159   55.00   F    1      2       2 6160   65.00   F    3      2       3 6161   49.00   M    1      2       2 6162   25.00   M   11      2       2 6163   51.00   F    1      2       2 6164   72.00   M   16      2       3 6165   60.00   F    1      2       3 6166   65.00   M    2      2       3 6167   34.00   M    2      2       2 6168   81.00   M    7      2       3 6169   87.00   M    0      2       3 6170   46.00   F    0      2       2 6171   80.00   F    2      2       3 6172   65.00   F    2      2       3 6173   70.00   M    3      2       3 6174   58.00   M    0      2       2 6175   65.00   M    2      2       3 6176   75.00   M    3      2       3 6177   35.00   M    1      2       2 6178   44.00   M    3      2       2 6179   38.00   M    8      2       2 6180   86.00   F    0      2       3 6181   83.00   M    1      2       3 6182   52.00   M    1      2       2 6183   68.00   F    2      2       3 6184   50.00   F    2      2       2 6185   83.00   M    2      2       3 6186   35.00   M    1      2       2 6187   65.00   M    1      2       3 6188   60.00   M    0      2       3 6189   66.00   F    1      2       3 6190   52.00   M    0      2       2 6191   82.00   M    2      2       3 6192   71.00   M   27      2       3 6193   26.00   M    3      2       2 6194   65.00   M    1      2       3 6195   72.00   M    1      2       3 6196   66.00   M   20      2       3 6197   50.00   F   10      2       2 6198   48.00   F    1      2       2 6199   71.00   M   14      2       3 6200   66.00   M    2      2       3 6201   65.00   M    2      2       3 6202   51.00   M   12      2       2 6203   82.00   F    7      2       3 6204   50.00   F    1      2       2 6205   87.00   M    7      2       3 6206   39.00   M    3      2       2 6207   75.00   M    1      2       3 6208   68.00   M    5      2       3 6209   65.00   F   15      2       3 6210   68.00   M    3      2       3 6211   79.00   F    6      2       3 6212   59.00   M    4      2       2 6213   50.00   M    1      2       2 6214   50.00   M   12      2       2 6215   74.00   M    0      2       3 6216   70.00   M    0      2       3 6217   58.00   M    7      2       2 6218   46.00   M   13      2       2 6219   55.00   F    0      2       2 6220   65.00   F    0      2       3 6221   45.00   M    3      2       2 6222   45.00   F    0      2       2 6223   69.00   M    0      2       3 6224   73.00   F    1      2       3 6225   25.00   M    6      2       2 6226   72.00   M    2      2       3 6227   64.00   M    9      2       3 6228   43.00   M    4      2       2 6229   72.00   M    6      2       3 6230   39.00   F    6      2       2 6231   40.00   M    8      2       2 6232   51.00   F   11      2       2 6233   65.00   M    4      2       3 6234   51.00   M    6      2       2 6235   92.00   M    4      2       3 6236   70.00   M    5      2       3 6237   79.00   M    7      2       3 6238   73.00   M    1      2       3 6239   55.00   M    6      2       2 6240   64.00   M    2      2       3 6241   60.00   F    1      2       3 6242   52.00   M    6      2       2 6243   61.00   M    4      2       3 6244   55.00   M    1      2       2 6245   65.00   F    5      2       3 6246   52.00   M    0      2       2 6247   50.00   M   27      2       2 6248   63.00   M    3      2       3 6249   75.00   F    1      2       3 6250   45.00   F    3      2       2 6251   46.00   M    1      2       2 6252   73.00   M    3      2       3 6253   72.00   M    1      2       3 6254   56.00   M    2      2       2 6255   40.00   M    1      2       2 6256   51.00   M    6      2       2 6257   65.00   M    5      2       3 6258   40.00   M    3      2       2 6259   52.00   M    0      2       2 6260   63.00   M    5      2       3 6261   65.00   F    1      2       3 6262   69.00   F    1      2       3 6263   60.00   M    1      2       3 6264   45.00   F    1      2       2 6265   40.00   M    1      2       2 6266   68.00   M    3      2       3 6267   67.00   M    2      2       3 6268   70.00   F    0      2       3 6269   50.00   F    3      2       2 6270   65.00   M    2      2       3 6271   63.00   F    1      2       3 6272   63.00   M    5      2       3 6273   45.00   M    0      2       2 6274   70.00   M    0      2       3 6275   60.00   M    1      2       3 6276   86.00   M    1      2       3 6277   65.00   F    1      2       3 6278   75.00   F    2      2       3 6279   32.00   M    1      2       2 6280   66.00   M    1      2       3 6281   65.00   F    2      2       3 6282   82.00   M    2      2       3 6283   52.00   M    1      2       2 6284   78.00   F    1      2       3 6285   79.00   M    0      2       3 6286   75.00   M    1      2       3 6287   73.00   M    7      2       3 6288   65.00   M   13      2       3 6289   55.00   M    4      2       2 6290   74.00   M    3      2       3 6291   60.00   F    8      2       3 6292   64.00   M   13      2       3 6293   69.00   F    7      2       3 6294   39.00   M    0      2       2 6295   47.00   M    6      2       2 6296   24.00   M    0      2       2 6297   80.00   F   15      2       3 6298   54.00   F    0      2       2 6299   84.00   F   17      2       3 6300   60.00   M    2      2       3 6301   58.00   M    0      2       2 6302   78.00   F    9      2       3 6303   68.00   F    0      2       3 6304   70.00   M   24      2       3 6305   66.00   M    2      2       3 6306   75.00   M    6      2       3 6307   76.00   M   11      2       3 6308   84.00   F    0      2       3 6309   58.00   M    1      2       2 6310   52.00   M    6      2       2 6311   90.00   M    5      2       3 6312   22.00   M   11      2       2 6313   57.00   M   18      2       2 6314   76.00   F    1      2       3 6315   86.00   M    8      2       3 6316   71.00   M    4      2       3 6317   63.00   M    7      2       3 6318   68.00   M   12      2       3 6319   88.00   M   14      2       3 6320   56.00   F    2      2       2 6321   47.00   M    6      2       2 6322    7.00   M    2      2       1 6323   81.00   M    4      2       3 6324   53.00   F    7      2       2 6325   19.00   M    0      2       2 6326   70.00   M    0      2       3 6327   81.00   M    2      2       3 6328    8.00   M    1      2       1 6329   65.00   F   20      2       3 6330   74.00   M    0      2       3 6331   73.00   M    7      2       3 6332   60.00   M    2      2       3 6333   63.00   M    8      2       3 6334   75.00   F    5      2       3 6335   75.00   M    1      2       3 6336   73.00   F    7      2       3 6337   58.00   F    2      2       2 6338   57.00   F    1      2       2 6339   80.00   F    5      2       3 6340   65.00   M    1      2       3 6341   80.00   M    4      2       3 6342   70.00   F    6      2       3 6343   55.00   F    5      2       2 6344   45.00   F   15      2       2 6345   49.00   M    8      2       2 6346   75.00   M    1      2       3 6347   45.00   M    0      2       2 6348   58.00   F   12      2       2 6349   60.00   F    3      2       3 6350   59.00   M    3      2       2 6351   75.00   F    0      2       3 6352   61.00   M    4      2       3 6353   79.00   M    2      2       3 6354   49.00   F    7      2       2 6355   85.00   M   12      2       3 6356   65.00   F    0      2       3 6357   70.00   M    2      2       3 6358   90.00   M    0      2       3 6359   61.00   M    5      2       3 6360   53.00   M    7      2       2 6361   60.00   F    8      2       3 6362   53.00   F    9      2       2 6363   62.00   M   12      2       3 6364   50.00   M    5      2       2 6365   67.00   M    0      2       3 6366   65.00   M    0      2       3 6367   77.00   F    2      2       3 6368   65.00   M    3      2       3 6369   38.00   M   11      2       2 6370   48.00   M    8      2       2 6371   64.00   M    9      2       3 6372   72.00   M    3      2       3 6373   72.00   M    6      2       3 6374   78.00   M    4      2       3 6375   55.00   F    8      2       2 6376   65.00   M    6      2       3 6377   64.00   M    0      2       3 6378   21.00   M    8      2       2 6379   35.00   F    2      2       2 6380   58.00   M    7      2       2 6381   55.00   M    5      2       2 6382   55.00   M    4      2       2 6383   77.00   F    3      2       3 6384   57.00   M    5      2       2 6385   57.00   M    2      2       2 6386   54.00   M    4      2       2 6387   55.00   M   12      2       2 6388   45.00   M    6      2       2 6389   95.00   M    0      2       3 6390   52.00   F    3      2       2 6391   71.00   M    4      2       3 6392   72.00   F    5      2       3 6393   70.00   M    1      2       3 6394   53.00   F    5      2       2 6395   42.00   F    4      2       2 6396   84.00   M    5      2       3 6397   50.00   F    2      2       2 6398   80.00   M    0      2       3 6399   36.00   M    8      2       2 6400   33.00   M    2      2       2 6401   65.00   M    1      2       3 6402   52.00   F    0      2       2 6403   86.00   M    1      2       3 6404   76.00   F    2      2       3 6405   68.00   M    1      2       3 6406   41.00   M    2      2       2 6407   67.00   M    2      2       3 6408   64.00   F    3      2       3 6409   54.00   F    1      2       2 6410   58.00   M    1      2       2 6411   52.00   M    2      2       2 6412   48.00   M    1      2       2 6413   55.00   M    5      2       2 6414   67.00   M    4      2       3 6415   70.00   M    1      2       3 6416   65.00   M    2      2       3 6417   88.00   F    1      2       3 6418   30.00   M    0      2       2 6419   60.00   M    0      2       3 6420   50.00   M    1      2       2 6421   50.00   M    1      2       2 6422   66.00   F    1      2       3 6423   52.00   F    1      2       2 6424   62.00   M    5      2       3 6425   45.00   F    3      2       2 6426   56.00   M    2      2       2 6427   75.00   F    0      2       3 6428   64.00   M   24      2       3 6429   63.00   M   22      2       3 6430   54.00   M   10      2       2 6431   60.00   M    7      2       3 6432   51.00   F    3      2       2 6433   47.00   F    4      2       2 6434   78.00   M   19      2       3 6435   60.00   M   16      2       3 6436   66.00   M   15      2       3 6437   80.00   M   18      2       3 6438   65.00   F    2      2       3 6439   49.00   M   19      2       2 6440   55.00   M   18      2       2 6441   80.00   F   15      2       3 6442   82.00   F   16      2       3 6443   34.00   F   13      2       2 6444   30.00   F   13      2       2 6445   65.00   F    5      2       3 6446   67.00   M    1      2       3 6447   58.00   F   11      2       2 6448   75.00   F    3      2       3 6449   32.00   F    2      2       2 6450   70.00   F   13      2       3 6451   56.00   M   10      2       2 6452   76.00   F   11      2       3 6453   78.00   M    9      2       3 6454   56.00   M   13      2       2 6455   60.00   F    1      2       3 6456   65.00   F   15      2       3 6457   80.00   M   12      2       3 6458   60.00   F    9      2       3 6459   42.00   M    5      2       2 6460   81.00   F   10      2       3 6461   65.00   M    5      2       3 6462   88.00   M   10      2       3 6463   65.00   M    7      2       3 6464   56.00   F    0      2       2 6465   85.00   M   12      2       3 6466   58.00   M    8      2       2 6467   53.00   M    6      2       2 6468   55.00   M    8      2       2 6469   50.00   M   11      2       2 6470   50.00   F    3      2       2 6471   62.00   M    1      2       3 6472   65.00   F   12      2       3 6473   68.00   M    6      2       3 6474   65.00   M    3      2       3 6475   60.00   M    2      2       3 6476   53.00   M   31      2       2 6477   55.00   F    4      2       2 6478   54.00   M    7      2       2 6479   46.00   M    0      2       2 6480   42.00   F    6      2       2 6481   70.00   F    9      2       3 6482   65.00   M    7      2       3 6483   47.00   M    2      2       2 6484   55.00   F    3      2       2 6485   60.00   F    0      2       3 6486   65.00   M    8      2       3 6487   71.00   M    6      2       3 6488   63.00   M    7      2       3 6489   75.00   F    7      2       3 6490   66.00   M    8      2       3 6491   68.00   M    1      2       3 6492   55.00   M    7      2       2 6493   55.00   M    6      2       2 6494   42.00   M    4      2       2 6495   60.00   F   10      2       3 6496   67.00   M    2      2       3 6497   62.00   M    5      2       3 6498   61.00   M    3      2       3 6499   64.00   M    0      2       3 6500   45.00   F    4      2       2 6501   77.00   F    5      2       3 6502   48.00   F    6      2       2 6503   51.00   M    5      2       2 6504   48.00   M    1      2       2 6505   70.00   M    0      2       3 6506   48.00   M    2      2       2 6507   54.00   M    5      2       2 6508   68.00   F    3      2       3 6509   62.00   M    0      2       3 6510   46.00   M    4      2       2 6511   79.00   M    5      2       3 6512   61.00   M    4      2       3 6513   80.00   F    4      2       3 6514   73.00   M    4      2       3 6515   40.00   M    0      2       2 6516   69.00   F    4      2       3 6517   58.00   M    6      2       2 6518   62.00   F    2      2       3 6519   77.00   M    1      2       3 6520   63.00   F    2      2       3 6521   75.00   M    1      2       3 6522   35.00   M    0      2       2 6523   45.00   M    1      2       2 6524   66.00   M    3      2       3 6525   54.00   M    2      2       2 6526   67.00   M    2      2       3 6527   62.00   M    4      2       3 6528   59.00   M    0      2       2 6529   57.00   M    3      2       2 6530   64.00   F    3      2       3 6531   52.00   M    0      2       2 6532   87.00   F    0      2       3 6533   55.00   M    0      2       2 6534   60.00   M    1      2       3 6535   75.00   M    3      2       3 6536   73.00   M    5      2       3 6537   63.00   M    0      2       3 6538   88.00   M    0      2       3 6539   55.00   F    0      2       2 6540   47.00   M    5      2       2 6541   50.00   M    4      2       2 6542   92.00   F    2      2       3 6543   63.00   F    2      2       3 6544   77.00   M    3      2       3 6545   36.00   M    0      2       2 6546   75.00   M    3      2       3 6547   35.00   M    3      2       2 6548   70.00   F    0      2       3 6549   74.00   F    3      2       3 6550   54.00   F    0      2       2 6551   58.00   M    2      2       2 6552   84.00   M    0      2       3 6553   78.00   M    0      2       3 6554   42.00   M    2      2       2 6555   66.00   M    3      2       3 6556   65.00   M   10      2       3 6557   59.00   F    0      2       2 6558   50.00   M    1      2       2 6559   65.00   M    0      2       3 6560   57.00   M    2      2       2 6561   55.00   F    0      2       2 6562   76.00   M    0      2       3 6563   65.00   F    3      2       3 6564   49.00   M    0      2       2 6565   47.00   F    2      2       2 6566   59.00   F    1      2       2 6567   58.00   M    0      2       2 6568   75.00   M    0      2       3 6569   82.00   F    1      2       3 6570   57.00   M    1      2       2 6571   45.00   M    0      2       2 6572   60.00   M    0      2       3 6573   42.00   M    0      2       2 6574   65.00   M    3      2       3 6575   64.00   M    0      2       3 6576   59.00   M   24      2       2 6577   50.00   M    3      2       2 6578   55.00   M   10      2       2 6579   67.00   M   22      2       3 6580   53.00   M   16      2       2 6581   45.00   M   16      2       2 6582   70.00   M   16      2       3 6583   78.00   M    0      2       3 6584   49.00   M   15      2       2 6585   67.00   F   12      2       3 6586   47.00   M   10      2       2 6587   50.00   M    8      2       2 6588   59.00   M    5      2       2 6589   84.00   M   13      2       3 6590   52.00   M    7      2       2 6591   44.00   M    9      2       2 6592   54.00   F    9      2       2 6593   64.00   M    0      2       3 6594   75.00   F   34      2       3 6595   74.00   M    2      2       3 6596   34.00   M    6      2       2 6597   80.00   F   12      2       3 6598   68.00   F    0      2       3 6599   68.00   M    9      2       3 6600   45.00   M    3      2       2 6601   52.00   F    1      2       2 6602   45.00   F    0      2       2 6603   85.00   M    4      2       3 6604   80.00   F    3      2       3 6605   64.00   M    2      2       3 6606   35.00   M    0      2       2 6607   60.00   F    1      2       3 6608   70.00   F   35      2       3 6609   68.00   F    7      2       3 6610   70.00   M    5      2       3 6611   75.00   M    7      2       3 6612   70.00   F    9      2       3 6613   35.00   M   10      2       2 6614   84.00   F   11      2       3 6615   66.00   F    0      2       3 6616   47.00   M    0      2       2 6617   55.00   F    8      2       2 6618   70.00   M    0      2       3 6619   62.00   M   35      2       3 6620   68.00   M    7      2       3 6621   68.00   F    4      2       3 6622   74.00   M    8      2       3 6623   31.00   M    7      2       2 6624   60.00   M    7      2       3 6625   63.00   M    8      2       3 6626   80.00   M    6      2       3 6627   47.00   F    9      2       2 6628   78.00   M    7      2       3 6629   53.00   M    7      2       2 6630   84.00   M    5      2       3 6631   26.00   F    4      2       2 6632   54.00   M   10      2       2 6633   56.00   M    5      2       2 6634   75.00   M    1      2       3 6635   72.00   M    8      2       3 6636   98.00   M    7      2       3 6637   27.00   M    1      2       2 6638   73.00   M    2      2       3 6639   76.00   M    7      2       3 6640   80.00   M    5      2       3 6641   55.00   F    4      2       2 6642   69.00   M    4      2       3 6643   61.00   M   34      2       3 6644   32.00   M    0      2       2 6645   44.00   M    4      2       2 6646   45.00   M    4      2       2 6647   60.00   M    2      2       3 6648   64.00   M    6      2       3 6649   61.00   M    1      2       3 6650   33.00   F    4      2       2 6651   70.00   F    6      2       3 6652  100.00   M    3      2       3 6653   70.00   M    7      2       3 6654   59.00   M    4      2       2 6655   45.00   M    5      2       2 6656   81.00   F    2      2       3 6657   90.00   F    5      2       3 6658   60.00   M    2      2       3 6659   52.00   M   14      2       2 6660   50.00   M    5      2       2 6661   62.00   F    5      2       3 6662   48.00   M    4      2       2 6663   55.00   F    4      2       2 6664   68.00   F    0      2       3 6665   78.00   M   19      2       3 6666   77.00   F    2      2       3 6667   74.00   M    5      2       3 6668   80.00   M    3      2       3 6669   60.00   F    1      2       3 6670   45.00   M    3      2       2 6671   60.00   F    9      2       3 6672   70.00   F    2      2       3 6673   55.00   F    1      2       2 6674   74.00   M    2      2       3 6675   64.00   M    4      2       3 6676   44.00   F    2      2       2 6677   40.00   M    1      2       2 6678   73.00   M   33      2       3 6679   48.00   M    1      2       2 6680   64.00   M    0      2       3 6681   64.00   M    1      2       3 6682   42.00   M    1      2       2 6683   61.00   M    0      2       3 6684   77.00   M    2      2       3 6685   40.00   M    0      2       2 6686   17.00   M    0      2       1 6687   56.00   F    1      2       2 6688   60.00   M    2      2       3 6689   72.00   F    0      2       3 6690   80.00   M    0      2       3 6691   35.00   F    0      2       2 6692   60.00   F    0      2       3 6693   62.00   M    0      2       3 6694   53.00   F    1      2       2 6695   83.00   M    1      2       3 6696   70.00   M    0      2       3 6697   37.00   M    1      2       2 6698   73.00   M    1      2       3 6699   72.00   F    0      2       3 6700   48.00   M    4      2       2 6701   78.00   F    0      2       3 6702   65.00   M   12      2       3 6703   50.00   M    4      2       2 6704   65.00   F    1      2       3 6705   48.00   M    1      2       2 6706   65.00   M    4      2       3 6707   49.00   F   21      2       2 6708   55.00   M   23      2       2 6709   65.00   M   33      2       3 6710   65.00   M    8      2       3 6711   22.00   F   14      2       2 6712   62.00   F   16      2       3 6713   80.00   M    2      2       3 6714   85.00   F    5      2       3 6715   74.00   F   10      2       3 6716   70.00   M    2      2       3 6717   48.00   M   13      2       2 6718   65.00   F    5      2       3 6719   65.00   M    2      2       3 6720   76.00   M    2      2       3 6721   54.00   M    2      2       2 6722   87.00   M   11      2       3 6723   89.00   M    4      2       3 6724   55.00   F    7      2       2 6725   70.00   M    5      2       3 6726   70.00   M    2      2       3 6727   67.00   M    0      2       3 6728   61.00   F    5      2       3 6729   69.00   M    2      2       3 6730   62.00   F    5      2       3 6731   65.00   F    6      2       3 6732   78.00   M    6      2       3 6733   78.00   F    5      2       3 6734   65.00   M    9      2       3 6735   42.00   M    7      2       2 6736   67.00   M    6      2       3 6737   63.00   F    4      2       3 6738   56.00   M    7      2       2 6739   29.00   M    9      2       2 6740   79.00   M    8      2       3 6741   69.00   M    3      2       3 6742   86.00   M    9      2       3 6743   55.00   F    7      2       2 6744   76.00   F    4      2       3 6745   75.00   M    6      2       3 6746   75.00   F    5      2       3 6747   76.00   F    5      2       3 6748   70.00   M    7      2       3 6749   80.00   F    5      2       3 6750   73.00   F    7      2       3 6751   38.00   F    0      2       2 6752   83.00   M    7      2       3 6753   55.00   F    2      2       2 6754   58.00   M    5      2       2 6755   45.00   M    4      2       2 6756   65.00   M    8      2       3 6757   70.00   M    3      2       3 6758   33.00   M    5      2       2 6759   70.00   M    0      2       3 6760   68.00   M    3      2       3 6761   75.00   M    7      2       3 6762   59.00   M    5      2       2 6763   79.00   M    7      2       3 6764   59.00   M    5      2       2 6765   58.00   M    4      2       2 6766   42.00   F    4      2       2 6767   59.00   M    4      2       2 6768   59.00   F    6      2       2 6769   74.00   M    2      2       3 6770   79.00   M    1      2       3 6771   67.00   M    4      2       3 6772   60.00   F    6      2       3 6773   72.00   M    4      2       3 6774   83.00   M    6      2       3 6775   78.00   M    2      2       3 6776   55.00   M    0      2       2 6777   41.00   F    5      2       2 6778   76.00   M    5      2       3 6779   49.00   M    0      2       2 6780   52.00   F   23      2       2 6781   84.00   M    2      2       3 6782   65.00   M    1      2       3 6783   47.00   F    3      2       2 6784   65.00   M    1      2       3 6785   58.00   M   34      2       2 6786   80.00   M    2      2       3 6787   60.00   F    3      2       3 6788   83.00   M    0      2       3 6789   84.00   M    3      2       3 6790   65.00   M    3      2       3 6791   58.00   F    3      2       2 6792   60.00   M    3      2       3 6793   60.00   F    1      2       3 6794   68.00   F    3      2       3 6795   70.00   F    6      2       3 6796   66.00   M    0      2       3 6797   80.00   M    0      2       3 6798   59.00   F    2      2       2 6799   65.00   F    1      2       3 6800   60.00   M    0      2       3 6801   32.00   F    2      2       2 6802   56.00   M    2      2       2 6803   55.00   F    1      2       2 6804   45.00   M    1      2       2 6805   60.00   M    4      2       3 6806   40.00   F    2      2       2 6807   56.00   M    0      2       2 6808   55.00   F    1      2       2 6809   42.00   M    0      2       2 6810    0.00   M    1      2       1 6811   50.00   M    1      2       2 6812   59.00   M    1      2       2 6813   56.00   M    1      2       2 6814   52.00   F    4      2       2 6815   35.00   M    0      2       2 6816   50.00   M    2      2       2 6817   60.00   M    1      2       3 6818   73.00   M    1      2       3 6819   54.00   M    4      2       2 6820   60.00   M    2      2       3 6821   75.00   M    1      2       3 6822   64.00   M    0      2       3 6823   48.00   M    1      2       2 6824   80.00   F    3      2       3 6825   58.00   F    1      2       2 6826   25.00   M    1      2       2 6827   55.00   F    0      2       2 6828   72.00   M    0      2       3 6829   68.00   M    2      2       3 6830   49.00   F    0      2       2 6831   70.00   M    7      2       3 6832   70.00   F    0      2       3 6833   60.00   M    8      2       3 6834   52.00   F    7      2       2 6835   81.00   M    2      2       3 6836   71.00   M   11      2       3 6837   69.00   M    7      2       3 6838   77.00   M   15      2       3 6839   48.00   F   11      2       2 6840   68.00   M    6      2       3 6841   64.00   F   13      2       3 6842   55.00   M    1      2       2 6843   63.00   M   12      2       3 6844   63.00   M    6      2       3 6845   55.00   M    5      2       2 6846   64.00   M    3      2       3 6847   57.00   F    7      2       2 6848   52.00   M    4      2       2 6849   74.00   M    2      2       3 6850   75.00   M    3      2       3 6851   66.00   M   12      2       3 6852   59.00   M    5      2       2 6853   70.00   M    7      2       3 6854   69.00   F    0      2       3 6855   80.00   M    0      2       3 6856   46.00   M   11      2       2 6857   60.00   M   12      2       3 6858   65.00   M   12      2       3 6859   60.00   M    0      2       3 6860   68.00   M   13      2       3 6861   74.00   M    4      2       3 6862   60.00   F    8      2       3 6863   40.00   M    4      2       2 6864   50.00   M    7      2       2 6865   66.00   M    8      2       3 6866   75.00   M    4      2       3 6867   65.00   M    2      2       3 6868   73.00   M    9      2       3 6869   55.00   M    1      2       2 6870   70.00   M    6      2       3 6871   80.00   F    4      2       3 6872   70.00   F    5      2       3 6873   42.00   M    9      2       2 6874   60.00   M    3      2       3 6875   67.00   F    8      2       3 6876   72.00   M    3      2       3 6877   53.00   M    6      2       2 6878   70.00   M    2      2       3 6879   70.00   F    8      2       3 6880   65.00   M    2      2       3 6881   54.00   M    1      2       2 6882   51.00   M    9      2       2 6883   22.00   F    1      2       2 6884   56.00   F    8      2       2 6885   59.00   M    4      2       2 6886   68.00   M    2      2       3 6887   43.00   F    3      2       2 6888   54.00   M   11      2       2 6889   48.00   M    2      2       2 6890   76.00   M    6      2       3 6891   63.00   M    5      2       3 6892   90.00   M    9      2       3 6893   72.00   M    5      2       3 6894   57.00   M   10      2       2 6895   67.00   M    4      2       3 6896   73.00   M    7      2       3 6897   74.00   M    1      2       3 6898   70.00   M    4      2       3 6899   35.00   F    4      2       2 6900   48.00   F    5      2       2 6901   40.00   M    4      2       2 6902   73.00   M    6      2       3 6903   65.00   F    3      2       3 6904   60.00   M    2      2       3 6905   46.00   M    4      2       2 6906   71.00   M    6      2       3 6907   66.00   M    7      2       3 6908   69.00   M    0      2       3 6909   64.00   M    4      2       3 6910   65.00   F    1      2       3 6911   63.00   M    3      2       3 6912   67.00   M    5      2       3 6913   70.00   F   35      2       3 6914   55.00   F    0      2       2 6915   54.00   F    1      2       2 6916   75.00   F    5      2       3 6917   70.00   M    6      2       3 6918   58.00   M    5      2       2 6919   69.00   M    1      2       3 6920   52.00   M    2      2       2 6921   48.00   F    2      2       2 6922   90.00   M    1      2       3 6923   50.00   F    3      2       2 6924   60.00   F    6      2       3 6925   47.00   F    2      2       2 6926   52.00   M    1      2       2 6927   58.00   F    3      2       2 6928   52.00   M    5      2       2 6929   74.00   M    3      2       3 6930   55.00   M    1      2       2 6931   40.00   M    0      2       2 6932   85.00   M    2      2       3 6933   66.00   F    0      2       3 6934   38.00   M    6      2       2 6935   60.00   M    2      2       3 6936   26.00   M    2      2       2 6937   50.00   M    2      2       2 6938   59.00   F    0      2       2 6939   50.00   F    3      2       2 6940   45.00   M    1      2       2 6941   74.00   M    0      2       3 6942   83.00   F    4      2       3 6943   72.00   F    2      2       3 6944   55.00   F    3      2       2 6945   60.00   F    2      2       3 6946   75.00   M    2      2       3 6947   72.00   F    1      2       3 6948   64.00   M    0      2       3 6949   73.00   M    2      2       3 6950   63.00   F    1      2       3 6951   54.00   M    2      2       2 6952   70.00   M    2      2       3 6953   85.00   M    1      2       3 6954   70.00   F    1      2       3 6955   40.00   M    2      2       2 6956   72.00   M    0      2       3 6957   59.00   M    0      2       2 6958   53.00   M   34      2       2 6959   50.00   M    0      2       2 6960   52.00   F    1      2       2 6961   87.00   M    7      2       3 6962   54.00   F    5      2       2 6963   67.00   F   21      2       3 6964   43.00   M    8      2       2 6965   71.00   M    2      2       3 6966   48.00   F   15      2       2 6967   70.00   M   21      2       3 6968   32.00   M    0      2       2 6969   56.00   M   20      2       2 6970   75.00   M   12      2       3 6971   79.00   M    6      2       3 6972   67.00   M    5      2       3 6973   42.00   F    8      2       2 6974   75.00   M    8      2       3 6975   75.00   M    5      2       3 6976   69.00   M    5      2       3 6977   86.00   M   18      2       3 6978   51.00   M    5      2       2 6979   61.00   M    8      2       3 6980   67.00   F    5      2       3 6981   84.00   M    5      2       3 6982   76.00   M   10      2       3 6983   22.00   F   11      2       2 6984   75.00   M   11      2       3 6985   85.00   M    3      2       3 6986   55.00   M    4      2       2 6987   61.00   M    4      2       3 6988   55.00   M    4      2       2 6989   78.00   M    4      2       3 6990   89.00   F    9      2       3 6991   70.00   M    3      2       3 6992   47.00   M   14      2       2 6993   43.00   F   10      2       2 6994   80.00   M    8      2       3 6995   57.00   M    7      2       2 6996   38.00   M   33      2       2 6997   65.00   F    8      2       3 6998   60.00   M    9      2       3 6999   80.00   F    9      2       3 7000   65.00   M    0      2       3 7001   82.00   M    8      2       3 7002   65.00   F    3      2       3 7003   75.00   M   36      2       3 7004   58.00   M    0      2       2 7005   59.00   M    2      2       2 7006   57.00   M    7      2       2 7007   65.00   F    8      2       3 7008   65.00   M    4      2       3 7009   45.00   M    5      2       2 7010   84.00   F    1      2       3 7011   40.00   M    3      2       2 7012   41.00   M    3      2       2 7013   60.00   M    2      2       3 7014   46.00   M    1      2       2 7015   63.00   F   15      2       3 7016   87.00   F    6      2       3 7017   68.00   F    6      2       3 7018   80.00   F    3      2       3 7019   75.00   M    1      2       3 7020   65.00   M   11      2       3 7021   27.00   F    6      2       2 7022   70.00   F    7      2       3 7023    7.00   M    1      2       1 7024   73.00   M    0      2       3 7025   65.00   M    3      2       3 7026   50.00   M    3      2       2 7027   56.00   M    0      2       2 7028   71.00   M    5      2       3 7029   55.00   M    2      2       2 7030   83.00   M    5      2       3 7031   65.00   M    1      2       3 7032   67.00   M    0      2       3 7033   42.00   M    3      2       2 7034   83.00   F    0      2       3 7035   72.00   M    4      2       3 7036   65.00   M    3      2       3 7037   66.00   M    1      2       3 7038   70.00   F    2      2       3 7039   58.00   F    7      2       2 7040   35.00   F    3      2       2 7041   56.00   F    3      2       2 7042   57.00   F    4      2       2 7043   70.00   F    1      2       3 7044   80.00   M    3      2       3 7045   82.00   M    0      2       3 7046   55.00   M    1      2       2 7047   78.00   M    0      2       3 7048   56.00   M    1      2       2 7049   70.00   M    9      2       3 7050   60.00   F    0      2       3 7051   40.00   M    0      2       2 7052   73.00   M    1      2       3 7053   75.00   M    0      2       3 7054   37.00   M    1      2       2 7055   36.00   F    2      2       2 7056   32.00   F    5      2       2 7057   23.00   F    2      2       2 7058   72.00   F   45      2       3 7059   56.00   F    8      2       2 7060   33.00   F    4      2       2 7061   72.00   M    2      2       3 7062   67.00   F   22      2       3 7063   80.00   F    0      2       3 7064   56.00   M    7      2       2 7065   75.00   F   20      2       3 7066   58.00   M    7      2       2 7067   59.00   M   15      2       2 7068   59.00   M    0      2       2 7069   65.00   F    7      2       3 7070   64.00   F    6      2       3 7071   48.00   M   15      2       2 7072   58.00   F   14      2       2 7073   73.00   F   11      2       3 7074   68.00   F    6      2       3 7075   48.00   M    7      2       2 7076   72.00   M   13      2       3 7077   60.00   M    8      2       3 7078   45.00   M    9      2       2 7079   50.00   M    4      2       2 7080   70.00   M    9      2       3 7081   55.00   M    2      2       2 7082   55.00   F    1      2       2 7083   55.00   M    2      2       2 7084   63.00   F    6      2       3 7085   90.00   F    8      2       3 7086   79.00   M   11      2       3 7087   57.00   M   10      2       2 7088   64.00   M   10      2       3 7089   36.00   M    8      2       2 7090   66.00   M    8      2       3 7091   61.00   M   11      2       3 7092   61.00   M    6      2       3 7093   65.00   F    7      2       3 7094   65.00   M    6      2       3 7095   70.00   M    7      2       3 7096   60.00   F    2      2       3 7097   65.00   M    3      2       3 7098   52.00   F    2      2       2 7099   44.00   F   10      2       2 7100   96.00   M   13      2       3 7101   74.00   M    3      2       3 7102   63.00   F    4      2       3 7103   61.00   M    1      2       3 7104   65.00   F    6      2       3 7105   21.00   M    8      2       2 7106   52.00   M    3      2       2 7107   70.00   M    3      2       3 7108   63.00   F    4      2       3 7109   68.00   M    2      2       3 7110   33.00   M    3      2       2 7111   52.00   M    8      2       2 7112   69.00   M    9      2       3 7113   64.00   M    8      2       3 7114   70.00   F    1      2       3 7115   68.00   M   10      2       3 7116   74.00   F    6      2       3 7117   72.00   M    3      2       3 7118   50.00   F    6      2       2 7119   73.00   M    6      2       3 7120   70.00   M    4      2       3 7121   68.00   M    5      2       3 7122   59.00   M    7      2       2 7123   62.00   M    1      2       3 7124   87.00   M    5      2       3 7125   48.00   F    6      2       2 7126   58.00   F    4      2       2 7127   43.00   M    3      2       2 7128   73.00   F    5      2       3 7129   60.00   M    5      2       3 7130   49.00   F    5      2       2 7131   59.00   M    1      2       2 7132   68.00   F    1      2       3 7133   43.00   M    4      2       2 7134   70.00   F    4      2       3 7135   77.00   F    4      2       3 7136   74.00   F    3      2       3 7137   50.00   M    1      2       2 7138   40.00   F    0      2       2 7139   52.00   M    4      2       2 7140   35.00   M    0      2       2 7141   58.00   F    1      2       2 7142   40.00   F    0      2       2 7143   62.00   M    2      2       3 7144   75.00   M    5      2       3 7145   65.00   F    3      2       3 7146   78.00   F    3      2       3 7147   74.00   F    1      2       3 7148   60.00   F    0      2       3 7149   70.00   F    1      2       3 7150   69.00   M    0      2       3 7151   56.00   F    3      2       2 7152   83.00   M    1      2       3 7153   82.00   F    0      2       3 7154   39.00   M    7      2       2 7155   55.00   M    1      2       2 7156   73.00   M    1      2       3 7157   60.00   M   11      2       3 7158   70.00   F    0      2       3 7159   70.00   M   23      2       3 7160   85.00   M    4      2       3 7161   60.00   M    0      2       3 7162   65.00   F    0      2       3 7163   70.00   F    4      2       3 7164   44.00   M    1      2       2 7165   65.00   M    2      2       3 7166   57.00   M    4      2       2 7167   65.00   M   13      2       3 7168   55.00   M    2      2       2 7169   66.00   M   10      2       3 7170   71.00   M    6      2       3 7171   75.00   M   13      2       3 7172   56.00   M   15      2       2 7173   32.00   F    9      2       2 7174   42.00   M    3      2       2 7175   88.00   M   12      2       3 7176   74.00   M    6      2       3 7177   65.00   F    5      2       3 7178   54.00   M   13      2       2 7179   76.00   M   13      2       3 7180   86.00   M    4      2       3 7181   78.00   M    7      2       3 7182   54.00   M    1      2       2 7183   53.00   M    4      2       2 7184   64.00   M    7      2       3 7185   90.00   M    8      2       3 7186   40.00   F    1      2       2 7187   55.00   M    4      2       2 7188   65.00   F    6      2       3 7189   62.00   M    1      2       3 7190   72.00   M    3      2       3 7191   65.00   F    0      2       3 7192   51.00   M    5      2       2 7193   60.00   M    5      2       3 7194   60.00   M    1      2       3 7195   55.00   M    8      2       2 7196   71.00   M    3      2       3 7197   70.00   M    5      2       3 7198   71.00   F    4      2       3 7199   38.00   M    2      2       2 7200   29.00   M    7      2       2 7201   70.00   F    9      2       3 7202   55.00   M    7      2       2 7203   62.00   M    3      2       3 7204   64.00   M    4      2       3 7205   45.00   M    1      2       2 7206   76.00   M    7      2       3 7207   70.00   M    2      2       3 7208   42.00   M    6      2       2 7209   72.00   M    8      2       3 7210   58.00   M    1      2       2 7211   55.00   F    4      2       2 7212   55.00   M    1      2       2 7213   63.00   M    5      2       3 7214   65.00   M    8      2       3 7215   72.00   M    2      2       3 7216   42.00   F    3      2       2 7217   26.00   F    6      2       2 7218   69.00   M    7      2       3 7219   72.00   M    4      2       3 7220   75.00   M    8      2       3 7221   80.00   M    2      2       3 7222   56.00   M    7      2       2 7223   71.00   F    1      2       3 7224   55.00   M    2      2       2 7225   70.00   F    1      2       3 7226   75.00   M    5      2       3 7227   54.00   M    6      2       2 7228   58.00   F    0      2       2 7229   50.00   M    2      2       2 7230   65.00   F    2      2       3 7231   61.00   M    1      2       3 7232   46.00   F    1      2       2 7233   69.00   M    0      2       3 7234   69.00   M    0      2       3 7235   84.00   M    7      2       3 7236   73.00   M    2      2       3 7237   57.00   M    2      2       2 7238   63.00   F   10      2       3 7239   82.00   M    3      2       3 7240   43.00   M    1      2       2 7241   47.00   M    3      2       2 7242   52.00   M    5      2       2 7243   60.00   F    2      2       3 7244   65.00   M    0      2       3 7245   77.00   F    0      2       3 7246   60.00   M    4      2       3 7247   39.00   F    0      2       2 7248   47.00   M    0      2       2 7249   70.00   F    1      2       3 7250   71.00   F    0      2       3 7251   65.00   M    5      2       3 7252   50.00   M    1      2       2 7253   60.00   F    1      2       3 7254   76.00   M    0      2       3 7255   65.00   M    2      2       3 7256   65.00   F    1      2       3 7257   60.00   M    2      2       3 7258   70.00   F    3      2       3 7259   45.00   M    0      2       2 7260   54.00   M    1      2       2 7261   63.00   F    0      2       3 7262   62.00   F    2      2       3 7263   70.00   M    8      2       3 7264   53.00   M   11      2       2 7265   70.00   M    1      2       3 7266   63.00   M    2      2       3 7267   78.00   M    2      2       3 7268   52.00   M    3      2       2 7269   54.00   F    5      2       2 7270   62.00   M    4      2       3 7271   67.00   M    0      2       3 7272   61.00   M    0      2       3 7273   43.00   F    0      2       2 7274   65.00   M    0      2       3 7275   35.00   M    1      2       2 7276   72.00   M    0      2       3 7277   45.00   M    1      2       2 7278   76.00   M   12      2       3 7279   71.00   F   10      2       3 7280   48.00   M    3      2       2 7281   60.00   M    5      2       3 7282   35.00   M    1      2       2 7283   69.00   M    6      2       3 7284   67.00   M   18      2       3 7285   62.00   M    4      2       3 7286   65.00   F    0      2       3 7287   66.00   F    1      2       3 7288   63.00   M   25      2       3 7289   67.00   F   15      2       3 7290   50.00   M   11      2       2 7291   67.00   M   11      2       3 7292   67.00   M    2      2       3 7293   85.00   M    0      2       3 7294   45.00   M    1      2       2 7295   71.00   F    0      2       3 7296   49.00   M    7      2       2 7297   20.00   M   11      2       2 7298   77.00   F   11      2       3 7299   48.00   M    2      2       2 7300   73.00   F   14      2       3 7301   62.00   F    0      2       3 7302   45.00   M   12      2       2 7303   60.00   M    5      2       3 7304   45.00   M    1      2       2 7305   57.00   F    1      2       2 7306   83.00   M    0      2       3 7307   70.00   M    1      2       3 7308   60.00   F   14      2       3 7309   74.00   M    3      2       3 7310   26.00   M   10      2       2 7311   66.00   M   10      2       3 7312   50.00   M   12      2       2 7313   49.00   M   10      2       2 7314   53.00   F    0      2       2 7315   32.00   M    3      2       2 7316   57.00   M    3      2       2 7317   56.00   M    0      2       2 7318   65.00   M    1      2       3 7319   65.00   F    0      2       3 7320   45.00   F    7      2       2 7321   70.00   F    3      2       3 7322   42.00   M    9      2       2 7323   63.00   M    0      2       3 7324   66.00   M    5      2       3 7325   60.00   M    2      2       3 7326   65.00   M    5      2       3 7327   65.00   F    5      2       3 7328   54.00   M    0      2       2 7329   59.00   M    4      2       2 7330   58.00   M    2      2       2 7331   60.00   F    2      2       3 7332   54.00   M    2      2       2 7333   30.00   M   11      2       2 7334   50.00   F    5      2       2 7335   70.00   M    3      2       3 7336   58.00   M    6      2       2 7337   49.00   M    5      2       2 7338   68.00   M    2      2       3 7339   80.00   M    7      2       3 7340   58.00   M    8      2       2 7341   93.00   M    2      2       3 7342   60.00   M    5      2       3 7343   70.00   M    3      2       3 7344   65.00   M    1      2       3 7345   55.00   M    2      2       2 7346   42.00   M    3      2       2 7347   26.00   M    0      2       2 7348   46.00   F    3      2       2 7349   48.00   M    2      2       2 7350   55.00   F    1      2       2 7351   60.00   M    6      2       3 7352   49.00   M    2      2       2 7353   62.00   M    2      2       3 7354   66.00   M    2      2       3 7355   84.00   F    3      2       3 7356   73.00   F    0      2       3 7357   32.00   M    2      2       2 7358   59.00   M    0      2       2 7359   65.00   M    7      2       3 7360   77.00   M    4      2       3 7361   60.00   M    3      2       3 7362   58.00   M    0      2       2 7363   62.00   F    0      2       3 7364   68.00   F    1      2       3 7365   55.00   F    2      2       2 7366   52.00   F    2      2       2 7367   55.00   M    5      2       2 7368   41.00   M    0      2       2 7369   54.00   F    2      2       2 7370   65.00   F    0      2       3 7371   52.00   M    0      2       2 7372   57.00   M    2      2       2 7373   40.00   F    0      2       2 7374   63.00   M    0      2       3 7375   55.00   M    0      2       2 7376   41.00   M    0      2       2 7377   45.00   M    1      2       2 7378   50.00   M   23      2       2 7379   35.00   M    5      2       2 7380   80.00   M   16      2       3 7381   39.00   M   13      2       2 7382   65.00   M   16      2       3 7383   60.00   M    3      2       3 7384   76.00   M   15      2       3 7385   55.00   M    5      2       2 7386   70.00   M    0      2       3 7387   55.00   F    6      2       2 7388   55.00   F    3      2       2 7389   46.00   M    6      2       2 7390   37.00   M    9      2       2 7391   67.00   M    9      2       3 7392   45.00   F   10      2       2 7393   72.00   M    9      2       3 7394   80.00   M    9      2       3 7395   72.00   F    7      2       3 7396   59.00   F    8      2       2 7397   57.00   F    8      2       2 7398   50.00   F    7      2       2 7399   56.00   F    7      2       2 7400   70.00   M   10      2       3 7401   60.00   M    5      2       3 7402   75.00   M    2      2       3 7403   74.00   M    6      2       3 7404   65.00   F    7      2       3 7405   79.00   F    5      2       3 7406   72.00   M    5      2       3 7407   65.00   F    5      2       3 7408   63.00   F    6      2       3 7409   65.00   M    2      2       3 7410   43.00   M    1      2       2 7411   46.00   M    5      2       2 7412   51.00   M    0      2       2 7413   71.00   F    8      2       3 7414   69.00   F    4      2       3 7415   80.00   M    2      2       3 7416   70.00   M    2      2       3 7417   69.00   M    3      2       3 7418   74.00   M    1      2       3 7419   40.00   M    2      2       2 7420   35.00   M    2      2       2 7421   77.00   F    7      2       3 7422   91.00   F    1      2       3 7423   70.00   M    6      2       3 7424   42.00   M    0      2       2 7425   65.00   F    3      2       3 7426   70.00   M    2      2       3 7427   67.00   M    8      2       3 7428   56.00   M    1      2       2 7429   36.00   M    0      2       2 7430   63.00   M    2      2       3 7431   54.00   M    2      2       2 7432   60.00   M    4      2       3 7433   66.00   F    2      2       3 7434   56.00   M    0      2       2 7435   27.00   M    2      2       2 7436   60.00   M    0      2       3 7437   55.00   M    1      2       2 7438   73.00   M    1      2       3 7439   54.00   F    0      2       2 7440   60.00   M    1      2       3 7441   54.00   M    0      2       2 7442   40.00   M    0      2       2 7443   60.00   M    1      2       3 7444   62.00   M    1      2       3 7445   68.00   M    7      2       3 7446   97.00   M   29      2       3 7447   75.00   M   19      2       3 7448   62.00   M   15      2       3 7449   80.00   M   11      2       3 7450   40.00   M   15      2       2 7451   71.00   M    9      2       3 7452   59.00   M    3      2       2 7453   62.00   M   14      2       3 7454   75.00   F   15      2       3 7455   65.00   M   12      2       3 7456   86.00   M    4      2       3 7457   80.00   F    3      2       3 7458   62.00   M    7      2       3 7459   80.00   M    7      2       3 7460   83.00   M    0      2       3 7461   65.00   F    5      2       3 7462   53.00   M   11      2       2 7463   64.00   M    2      2       3 7464   52.00   F   11      2       2 7465   72.00   M    8      2       3 7466   66.00   F    6      2       3 7467   63.00   M   15      2       3 7468    0.00   M   10      2       1 7469   68.00   M    1      2       3 7470   82.00   F    2      2       3 7471   62.00   M    4      2       3 7472   70.00   F    8      2       3 7473   62.00   M    7      2       3 7474   89.00   M    8      2       3 7475   69.00   M    9      2       3 7476   80.00   M    8      2       3 7477   50.00   M    4      2       2 7478   80.00   M    9      2       3 7479   42.00   M   10      2       2 7480   56.00   F    6      2       2 7481   85.00   F    8      2       3 7482   85.00   M    3      2       3 7483   69.00   M    6      2       3 7484   61.00   F    9      2       3 7485   78.00   F    9      2       3 7486   81.00   M    2      2       3 7487   71.00   M    4      2       3 7488   58.00   M    0      2       2 7489   54.00   F    5      2       2 7490   62.00   M    7      2       3 7491   69.00   M    5      2       3 7492   93.00   M    0      2       3 7493   64.00   F    4      2       3 7494   50.00   M    3      2       2 7495   52.00   M    9      2       2 7496   65.00   M    2      2       3 7497   74.00   M    7      2       3 7498   68.00   M    2      2       3 7499   30.00   M    5      2       2 7500   77.00   M    7      2       3 7501   64.00   M    5      2       3 7502   66.00   M    6      2       3 7503   70.00   F    1      2       3 7504   65.00   M    6      2       3 7505   80.00   M    4      2       3 7506   70.00   F    1      2       3 7507   66.00   M    6      2       3 7508   68.00   M    3      2       3 7509   80.00   F    1      2       3 7510   45.00   F    0      2       2 7511   64.00   F   15      2       3 7512   86.00   M    0      2       3 7513   66.00   M    5      2       3 7514   65.00   F    3      2       3 7515   72.00   M    8      2       3 7516   70.00   M    1      2       3 7517   53.00   M    3      2       2 7518   73.00   F    5      2       3 7519   47.00   M    0      2       2 7520   48.00   M    4      2       2 7521   45.00   F   12      2       2 7522   62.00   M    2      2       3 7523   56.00   M    1      2       2 7524   55.00   M    2      2       2 7525   71.00   F    3      2       3 7526   70.00   F    2      2       3 7527   70.00   M    2      2       3 7528   60.00   M    1      2       3 7529   54.00   F    1      2       2 7530   73.00   F    0      2       3 7531   30.00   M    0      2       2 7532   52.00   F    0      2       2 7533   19.00   M    0      2       2 7534   65.00   M    0      2       3 7535   62.00   M    2      2       3 7536   36.00   F    1      2       2 7537   42.00   M    0      2       2 7538   61.00   M    0      2       3 7539   45.00   M    0      2       2 7540   72.00   M    0      2       3 7541   56.00   M    0      2       2 7542   54.00   M    0      2       2 7543   74.00   M   23      2       3 7544   68.00   M   25      2       3 7545   50.00   F    4      2       2 7546   60.00   F    6      2       3 7547   50.00   M    4      2       2 7548   68.00   F   19      2       3 7549   68.00   F    1      2       3 7550   58.00   F   19      2       2 7551   68.00   F   16      2       3 7552   51.00   M   13      2       2 7553   50.00   F   11      2       2 7554   66.00   F    2      2       3 7555   51.00   M   14      2       2 7556   75.00   M    9      2       3 7557   67.00   M    1      2       3 7558   68.00   M    7      2       3 7559   61.00   F    1      2       3 7560   74.00   F    1      2       3 7561   52.00   F    7      2       2 7562   92.00   M    1      2       3 7563   66.00   M    3      2       3 7564   67.00   F    6      2       3 7565   50.00   F    3      2       2 7566   67.00   M   10      2       3 7567   35.00   F    1      2       2 7568   65.00   M    2      2       3 7569   65.00   M    0      2       3 7570   72.00   M   12      2       3 7571   45.00   F    1      2       2 7572   62.00   M    4      2       3 7573   66.00   M    3      2       3 7574   52.00   F   12      2       2 7575   56.00   F    8      2       2 7576   53.00   M   12      2       2 7577   66.00   M    9      2       3 7578   43.00   M    0      2       2 7579   79.00   M    5      2       3 7580   60.00   M    6      2       3 7581   57.00   M   12      2       2 7582   88.00   M    5      2       3 7583   66.00   M   11      2       3 7584   70.00   M    2      2       3 7585   45.00   M    6      2       2 7586   65.00   M    5      2       3 7587   80.00   M    1      2       3 7588   65.00   M    5      2       3 7589   74.00   M   12      2       3 7590   76.00   M    0      2       3 7591   36.00   M    6      2       2 7592   65.00   F    0      2       3 7593   47.00   M    1      2       2 7594   85.00   M   38      2       3 7595   40.00   M    0      2       2 7596   73.00   M   10      2       3 7597   60.00   M    5      2       3 7598   54.00   M    5      2       2 7599   56.00   M    1      2       2 7600   74.00   M    1      2       3 7601   65.00   M    4      2       3 7602   53.00   M    1      2       2 7603   67.00   M    9      2       3 7604   77.00   F    1      2       3 7605   68.00   M    1      2       3 7606   78.00   M    4      2       3 7607   72.00   M    4      2       3 7608   85.00   F   10      2       3 7609   75.00   F    5      2       3 7610   56.00   M    2      2       2 7611   54.00   F    1      2       2 7612   60.00   F    5      2       3 7613   68.00   M   10      2       3 7614   47.00   F    3      2       2 7615   50.00   M    4      2       2 7616   83.00   M    9      2       3 7617   85.00   M    8      2       3 7618   75.00   F    9      2       3 7619   64.00   M   10      2       3 7620   65.00   M    6      2       3 7621   78.00   F    4      2       3 7622   67.00   F    4      2       3 7623   58.00   M    2      2       2 7624   58.00   F    2      2       2 7625   65.00   M    1      2       3 7626   70.00   M   31      2       3 7627   65.00   M    0      2       3 7628   70.00   F    4      2       3 7629   59.00   M    7      2       2 7630   64.00   F    5      2       3 7631   60.00   M    2      2       3 7632   70.00   M    4      2       3 7633   57.00   M    5      2       2 7634   72.00   M    6      2       3 7635   42.00   M    0      2       2 7636   74.00   M    3      2       3 7637   68.00   F    3      2       3 7638   45.00   M    3      2       2 7639   59.00   M    6      2       2 7640   55.00   M    5      2       2 7641   70.00   F    8      2       3 7642   52.00   M    5      2       2 7643   32.00   F    1      2       2 7644   43.00   F    1      2       2 7645   55.00   F    3      2       2 7646   42.00   M    5      2       2 7647   49.00   M    2      2       2 7648   68.00   F    0      2       3 7649   68.00   F    2      2       3 7650   87.00   M    6      2       3 7651   20.00   F    1      2       2 7652   49.00   M   14      2       2 7653   50.00   M    0      2       2 7654   55.00   F    3      2       2 7655   57.00   M    4      2       2 7656   45.00   M    5      2       2 7657   63.00   M    3      2       3 7658   34.00   F    1      2       2 7659   70.00   F    1      2       3 7660   62.00   M    0      2       3 7661   66.00   M    2      2       3 7662   54.00   M    3      2       2 7663   65.00   F    5      2       3 7664   84.00   F    1      2       3 7665   70.00   M    1      2       3 7666   69.00   M    3      2       3 7667   85.00   M    2      2       3 7668   62.00   M    1      2       3 7669   72.00   M    1      2       3 7670   50.00   M    0      2       2 7671   70.00   M    0      2       3 7672   60.00   F    0      2       3 7673   70.00   F    4      2       3 7674   76.00   M    9      2       3 7675   71.00   F    4      2       3 7676   60.00   M    0      2       3 7677   64.00   M    3      2       3 7678   68.00   M    1      2       3 7679   68.00   F    5      2       3 7680   67.00   M    5      2       3 7681   52.00   F    0      2       2 7682   85.00   F    7      2       3 7683   56.00   M    3      2       2 7684   70.00   M    0      2       3 7685   45.00   M    7      2       2 7686   63.00   F    3      2       3 7687   64.00   M    5      2       3 7688   87.00   M    2      2       3 7689   40.00   M    3      2       2 7690   86.00   F    1      2       3 7691   45.00   F    3      2       2 7692   67.00   M    1      2       3 7693   50.00   M    2      2       2 7694   58.00   M    3      2       2 7695   55.00   M    1      2       2 7696   75.00   M    3      2       3 7697   58.00   M    0      2       2 7698   21.00   M    0      2       2 7699   54.00   M    2      2       2 7700   56.00   M    3      2       2 7701   70.00   M    2      2       3 7702   73.00   M    0      2       3 7703   84.00   M    1      2       3 7704   75.00   M    1      2       3 7705   48.00   M    1      2       2 7706   50.00   F    1      2       2 7707   56.00   M    0      2       2 7708   80.00   M    3      2       3 7709   75.00   M    2      2       3 7710   46.00   F    2      2       2 7711   62.00   M    1      2       3 7712   65.00   F    2      2       3 7713   65.00   M    2      2       3 7714   69.00   M    1      2       3 7715   96.00   F    3      2       3 7716   79.00   F    6      2       3 7717   70.00   F   34      2       3 7718   73.00   M   23      2       3 7719   86.00   M   13      2       3 7720   59.00   M    8      2       2 7721   75.00   M   16      2       3 7722   70.00   M   17      2       3 7723   82.00   M   19      2       3 7724   34.00   M   17      2       2 7725   60.00   M    3      2       3 7726   65.00   M   14      2       3 7727   62.00   M   13      2       3 7728   59.00   M    6      2       2 7729   65.00   M    0      2       3 7730   78.00   F    0      2       3 7731   50.00   F    0      2       2 7732   63.00   F    5      2       3 7733   65.00   M    6      2       3 7734   21.00   F    3      2       2 7735   57.00   F    8      2       2 7736   67.00   M   18      2       3 7737   51.00   F    0      2       2 7738   71.00   F    0      2       3 7739   78.00   M    0      2       3 7740   69.00   M   17      2       3 7741   60.00   M    4      2       3 7742   84.00   F    0      2       3 7743   63.00   M    0      2       3 7744   70.00   M    1      2       3 7745   52.00   M    8      2       2 7746   36.00   M    2      2       2 7747   65.00   F    0      2       3 7748   72.00   M    2      2       3 7749   30.00   F    7      2       2 7750   74.00   M    0      2       3 7751   49.00   M    0      2       2 7752   75.00   M    1      2       3 7753   60.00   M   13      2       3 7754   49.00   M    2      2       2 7755   73.00   M   25      2       3 7756   57.00   M    1      2       2 7757   82.00   M    1      2       3 7758   75.00   M    6      2       3 7759   68.00   M    9      2       3 7760   50.00   M    4      2       2 7761   51.00   F    5      2       2 7762   84.00   M    2      2       3 7763   72.00   M    0      2       3 7764   66.00   M    5      2       3 7765   60.00   F    0      2       3 7766   72.00   M    7      2       3 7767   72.00   M    7      2       3 7768   59.00   M    6      2       2 7769   62.00   M   13      2       3 7770   68.00   M    3      2       3 7771   35.00   F    0      2       2 7772   85.00   F    2      2       3 7773   59.00   F    3      2       2 7774   85.00   M    2      2       3 7775   57.00   F    5      2       2 7776   39.00   M    4      2       2 7777   62.00   F    1      2       3 7778   55.00   M    5      2       2 7779   64.00   M    7      2       3 7780   61.00   F    2      2       3 7781   65.00   M    1      2       3 7782   51.00   M    6      2       2 7783   75.00   M    2      2       3 7784   72.00   M    4      2       3 7785   75.00   M    5      2       3 7786   62.00   M    0      2       3 7787   70.00   F    0      2       3 7788   65.00   F    2      2       3 7789   60.00   F    4      2       3 7790   70.00   F    5      2       3 7791   44.00   M    5      2       2 7792   58.00   M    0      2       2 7793   62.00   M    5      2       3 7794   50.00   M   14      2       2 7795   81.00   M   18      2       3 7796   64.00   M    2      2       3 7797   31.00   M    1      2       2 7798   73.00   M    0      2       3 7799   60.00   F    5      2       3 7800   64.00   M    1      2       3 7801   83.00   M    0      2       3 7802   48.00   M    0      2       2 7803   54.00   M    2      2       2 7804   65.00   M    2      2       3 7805   60.00   M    0      2       3 7806   68.00   M    0      2       3 7807   53.00   M    0      2       2 7808   38.00   M    1      2       2 7809   63.00   M    1      2       3 7810   61.00   M    1      2       3 7811   65.00   F    3      2       3 7812   73.00   M    0      2       3 7813   71.00   M    1      2       3 7814   62.00   F    1      2       3 7815   44.00   M    1      2       2 7816   53.00   M    1      2       2 7817   70.00   M    0      2       3 7818   63.00   M    1      2       3 7819   55.00   F    2      2       2 7820   42.00   M    1      2       2 7821   52.00   M   10      2       2 7822   73.00   M    0      2       3 7823   69.00   M    2      2       3 7824   88.00   M    0      2       3 7825   69.00   M    0      2       3 7826   65.00   F    1      2       3 7827   72.00   M    1      2       3 7828   55.00   F    1      2       2 7829   46.00   F    2      2       2 7830   56.00   M    0      2       2 7831   65.00   M   17      2       3 7832   67.00   M    0      2       3 7833   64.00   M    0      2       3 7834   60.00   F   13      2       3 7835   52.00   M    0      2       2 7836   63.00   M    5      2       3 7837   72.00   M   13      2       3 7838   52.00   M   13      2       2 7839   50.00   F   16      2       2 7840   68.00   M   10      2       3 7841   68.00   M    8      2       3 7842   57.00   M    2      2       2 7843   50.00   F    1      2       2 7844   60.00   M    6      2       3 7845   62.00   M   10      2       3 7846   59.00   F    0      2       2 7847   68.00   F    3      2       3 7848   96.00   M    5      2       3 7849   80.00   M   12      2       3 7850   80.00   M    7      2       3 7851   65.00   F    3      2       3 7852   64.00   F    2      2       3 7853   40.00   F    9      2       2 7854   72.00   M    6      2       3 7855   50.00   M    7      2       2 7856   51.00   F    5      2       2 7857   65.00   F    6      2       3 7858   75.00   M    7      2       3 7859   80.00   M    1      2       3 7860   49.00   M    0      2       2 7861   66.00   F    7      2       3 7862   12.00   M    0      2       1 7863   85.00   M    8      2       3 7864   76.00   M    4      2       3 7865   60.00   F    1      2       3 7866   80.00   F    4      2       3 7867   75.00   M    8      2       3 7868   33.00   M    9      2       2 7869   73.00   F    6      2       3 7870   80.00   F    2      2       3 7871   19.00   F    1      2       2 7872   54.00   M    8      2       2 7873   66.00   M    5      2       3 7874   57.00   M    4      2       2 7875   62.00   M    2      2       3 7876   83.00   M    1      2       3 7877   65.00   M    0      2       3 7878   50.00   F    4      2       2 7879   50.00   F    6      2       2 7880   32.00   M    2      2       2 7881   77.00   F    1      2       3 7882   55.00   F    3      2       2 7883   76.00   M    1      2       3 7884   61.00   M    0      2       3 7885   47.00   M    5      2       2 7886   50.00   M    5      2       2 7887   55.00   M    4      2       2 7888   73.00   F    6      2       3 7889   64.00   M    1      2       3 7890   85.00   F    6      2       3 7891   35.00   M    4      2       2 7892   55.00   F    2      2       2 7893   68.00   M    3      2       3 7894   50.00   F    2      2       2 7895   75.00   M    0      2       3 7896   76.00   F    1      2       3 7897   56.00   M    3      2       2 7898   48.00   F   13      2       2 7899   80.00   M    1      2       3 7900   72.00   M    3      2       3 7901   81.00   F    1      2       3 7902    1.00   F    0      2       1 7903   40.00   F    4      2       2 7904   69.00   M    4      2       3 7905   64.00   M    2      2       3 7906   72.00   M    7      2       3 7907   93.00   M    1      2       3 7908   97.00   M    5      2       3 7909   32.00   M    2      2       2 7910   75.00   F    2      2       3 7911   54.00   M    1      2       2 7912   38.00   F    1      2       2 7913   64.00   M    1      2       3 7914   60.00   F    1      2       3 7915   61.00   M   10      2       3 7916   60.00   M    0      2       3 7917   78.00   F    0      2       3 7918   65.00   F    2      2       3 7919   59.00   M    0      2       2 7920   58.00   M    2      2       2 7921   85.00   M    2      2       3 7922   55.00   M    0      2       2 7923   73.00   M    3      2       3 7924   48.00   F    4      2       2 7925   43.00   F    4      2       2 7926   84.00   M    1      2       3 7927   85.00   M    0      2       3 7928   38.00   M    0      2       2 7929   51.00   M    1      2       2 7930   60.00   M    0      2       3 7931   70.00   M    0      2       3 7932   85.00   M   15      2       3 7933   61.00   M   16      2       3 7934   75.00   M   21      2       3 7935   62.00   M   11      2       3 7936   75.00   M   17      2       3 7937   75.00   F   11      2       3 7938   70.00   M    1      2       3 7939   60.00   F   14      2       3 7940   50.00   M    1      2       2 7941   75.00   M   14      2       3 7942   63.00   F   13      2       3 7943   35.00   M   10      2       2 7944   56.00   M    7      2       2 7945   75.00   M    0      2       3 7946   69.00   M    6      2       3 7947   68.00   M    3      2       3 7948   70.00   M    5      2       3 7949   50.00   M   11      2       2 7950   65.00   F    4      2       3 7951   62.00   F    1      2       3 7952   72.00   M    0      2       3 7953   63.00   M    8      2       3 7954   75.00   F    6      2       3 7955   80.00   M    2      2       3 7956   72.00   F    5      2       3 7957   60.00   F    9      2       3 7958   75.00   M    0      2       3 7959   63.00   M   10      2       3 7960   65.00   F    2      2       3 7961   70.00   M    2      2       3 7962   84.00   M    9      2       3 7963   70.00   F    8      2       3 7964   65.00   M    8      2       3 7965   85.00   M   10      2       3 7966   58.00   M    2      2       2 7967   85.00   M    1      2       3 7968   61.00   M    4      2       3 7969   66.00   F    7      2       3 7970   80.00   F    8      2       3 7971   58.00   F    6      2       2 7972   66.00   M   10      2       3 7973   71.00   M    7      2       3 7974   59.00   M    8      2       2 7975   65.00   F    7      2       3 7976   78.00   M    4      2       3 7977   80.00   M    3      2       3 7978   82.00   M    3      2       3 7979   62.00   F    4      2       3 7980   82.00   M    2      2       3 7981   48.00   M    5      2       2 7982   62.00   M    3      2       3 7983   60.00   M    3      2       3 7984   77.00   M    5      2       3 7985   40.00   M    6      2       2 7986   50.00   M    5      2       2 7987   73.00   M    5      2       3 7988   65.00   F    7      2       3 7989   48.00   F    5      2       2 7990   64.00   M    3      2       3 7991   67.00   M    2      2       3 7992   65.00   F    4      2       3 7993   65.00   M    0      2       3 7994   53.00   M    3      2       2 7995   65.00   M    4      2       3 7996   66.00   M    5      2       3 7997   71.00   M    3      2       3 7998   52.00   M    0      2       2 7999   71.00   F    6      2       3 8000   75.00   M    6      2       3 8001   72.00   M    3      2       3 8002   66.00   M    2      2       3 8003   42.00   M    2      2       2 8004   67.00   M    3      2       3 8005   53.00   F    1      2       2 8006   49.00   M    2      2       2 8007   62.00   M   10      2       3 8008   63.00   M    2      2       3 8009   69.00   M    1      2       3 8010   65.00   F    1      2       3 8011   58.00   M    2      2       2 8012   72.00   M    2      2       3 8013   43.00   M    1      2       2 8014   70.00   M    2      2       3 8015   42.00   M    0      2       2 8016   65.00   M    4      2       3 8017   66.00   M    4      2       3 8018   70.00   F    0      2       3 8019   50.00   M    1      2       2 8020   70.00   M    1      2       3 8021   35.00   M    0      2       2 8022   65.00   M    3      2       3 8023   65.00   M    2      2       3 8024   59.00   M   15      2       2 8025   60.00   M    0      2       3 8026   65.00   M    1      2       3 8027   72.00   F    0      2       3 8028   60.00   M    1      2       3 8029   48.00   M    1      2       2 8030   58.00   F    2      2       2 8031   58.00   M    0      2       2 8032   49.00   M    2      2       2 8033   66.00   M    3      2       3 8034   70.00   M    2      2       3 8035   61.00   M    4      2       3 8036   60.00   F    3      2       3 8037   65.00   M    2      2       3 8038   80.00   M   23      2       3 8039   85.00   M    2      2       3 8040   70.00   M    1      2       3 8041   50.00   F    0      2       2 8042   25.00   M    1      2       2 8043   62.00   M    1      2       3 8044   52.00   M    1      2       2 8045   68.00   M    2      2       3 8046   59.00   F    2      2       2 8047   55.00   M    0      2       2 8048   65.00   M    0      2       3 8049   60.00   M    0      2       3 8050   65.00   F    0      2       3 8051   45.00   F    1      2       2 8052   42.00   M    2      2       2 8053   52.00   F    1      2       2 8054   65.00   F   19      2       3 8055   65.00   M   22      2       3 8056   26.00   M   24      2       2 8057   58.00   F    3      2       2 8058   56.00   M   17      2       2 8059   65.00   F   14      2       3 8060   76.00   M   22      2       3 8061   77.00   F   17      2       3 8062   40.00   M    1      2       2 8063   71.00   M    0      2       3 8064   68.00   M    6      2       3 8065   49.00   M    6      2       2 8066   74.00   M    7      2       3 8067   75.00   F   14      2       3 8068   71.00   F    0      2       3 8069   75.00   F    1      2       3 8070   60.00   M    0      2       3 8071   58.00   M    1      2       2 8072   72.00   M    5      2       3 8073   74.00   M    7      2       3 8074   93.00   M    2      2       3 8075   73.00   M    3      2       3 8076   45.00   M   11      2       2 8077   70.00   M    1      2       3 8078   79.00   M    7      2       3 8079   70.00   M    0      2       3 8080   65.00   M    7      2       3 8081   89.00   M    4      2       3 8082   68.00   M    6      2       3 8083   68.00   M    0      2       3 8084   73.00   M    3      2       3 8085   65.00   M    5      2       3 8086   57.00   F    1      2       2 8087   41.00   F   11      2       2 8088   72.00   M    0      2       3 8089   48.00   M    7      2       2 8090   40.00   M    0      2       2 8091   60.00   M    6      2       3 8092   38.00   M    6      2       2 8093   55.00   M    0      2       2 8094   71.00   F    6      2       3 8095   64.00   F    6      2       3 8096   65.00   M    8      2       3 8097   63.00   M    3      2       3 8098   65.00   F    3      2       3 8099   60.00   M    1      2       3 8100   65.00   M    4      2       3 8101   70.00   M    3      2       3 8102   64.00   M    4      2       3 8103   62.00   M    3      2       3 8104   58.00   M    4      2       2 8105   23.00   M    5      2       2 8106   95.00   M    5      2       3 8107   45.00   M    0      2       2 8108   57.00   M    4      2       2 8109   52.00   F    6      2       2 8110   43.00   M    7      2       2 8111   52.00   M    3      2       2 8112   82.00   M    3      2       3 8113   67.00   M    1      2       3 8114   58.00   M    2      2       2 8115   55.00   F    2      2       2 8116   56.00   F    3      2       2 8117   46.00   M    1      2       2 8118   95.00   M    2      2       3 8119   52.00   M    2      2       2 8120   71.00   M    5      2       3 8121   40.00   F    2      2       2 8122   83.00   M    0      2       3 8123   50.00   M    2      2       2 8124   74.00   M    3      2       3 8125   65.00   F    1      2       3 8126   52.00   M    4      2       2 8127   84.00   F    0      2       3 8128   72.00   M    3      2       3 8129   86.00   M    3      2       3 8130   67.00   F    0      2       3 8131    1.00   M    0      2       1 8132   60.00   M    4      2       3 8133   60.00   F    3      2       3 8134   58.00   M    2      2       2 8135   88.00   M    0      2       3 8136   74.00   M    0      2       3 8137   65.00   M   27      2       3 8138   76.00   M   15      2       3 8139   58.00   M    0      2       2 8140   67.00   M    8      2       3 8141   49.00   M    0      2       2 8142   70.00   M    2      2       3 8143   44.00   M   12      2       2 8144   64.00   M    2      2       3 8145   67.00   F    6      2       3 8146   58.00   F    7      2       2 8147   70.00   F    7      2       3 8148   50.00   M    6      2       2 8149   56.00   M    5      2       2 8150   83.00   M    1      2       3 8151   67.00   M    3      2       3 8152   77.00   M   17      2       3 8153   76.00   M    7      2       3 8154   75.00   F    1      2       3 8155   54.00   F    0      2       2 8156   70.00   M    2      2       3 8157   68.00   M    3      2       3 8158   46.00   M    1      2       2 8159   79.00   F    2      2       3 8160   82.00   M    6      2       3 8161   69.00   M    5      2       3 8162   83.00   F    8      2       3 8163   65.00   M    3      2       3 8164   78.00   M    2      2       3 8165   65.00   M    2      2       3 8166   48.00   F    4      2       2 8167   48.00   F    2      2       2 8168   89.00   F    2      2       3 8169   68.00   F    6      2       3 8170   75.00   M    3      2       3 8171   54.00   F    1      2       2 8172   41.00   M    0      2       2 8173   65.00   M    0      2       3 8174   60.00   F    1      2       3 8175   72.00   M   34      2       3 8176   64.00   M   23      2       3 8177   82.00   M   13      2       3 8178   52.00   M   17      2       2 8179   65.00   M    3      2       3 8180   85.00   M   12      2       3 8181   74.00   M   10      2       3 8182   67.00   F   12      2       3 8183   62.00   F    2      2       3 8184   78.00   M   13      2       3 8185   58.00   F    9      2       2 8186   50.00   M   14      2       2 8187   62.00   F    0      2       3 8188   85.00   M    2      2       3 8189   65.00   M    2      2       3 8190   69.00   M   10      2       3 8191   56.00   F    3      2       2 8192   67.00   M    3      2       3 8193   52.00   M    9      2       2 8194   81.00   M   11      2       3 8195   71.00   M    8      2       3 8196   74.00   M    3      2       3 8197   67.00   M    7      2       3 8198   45.00   F    7      2       2 8199   62.00   M   11      2       3 8200   54.00   M    0      2       2 8201   86.00   M    7      2       3 8202   32.00   M    0      2       2 8203   63.00   M    9      2       3 8204   65.00   M    7      2       3 8205   58.00   M    5      2       2 8206   50.00   M    0      2       2 8207   80.00   M    0      2       3 8208   59.00   F    4      2       2 8209   30.00   F    3      2       2 8210   72.00   F    3      2       3 8211   73.00   M    5      2       3 8212   95.00   M    3      2       3 8213   67.00   M    3      2       3 8214   53.00   M    4      2       2 8215   60.00   F    0      2       3 8216   80.00   F    4      2       3 8217   62.00   M    1      2       3 8218   54.00   M    0      2       2 8219   75.00   M    2      2       3 8220   85.00   F    3      2       3 8221   71.00   F    7      2       3 8222   71.00   F    2      2       3 8223   40.00   M    0      2       2 8224   72.00   M    1      2       3 8225   54.00   M    1      2       2 8226   97.00   M    1      2       3 8227   60.00   M    1      2       3 8228   52.00   M    0      2       2 8229   76.00   M    0      2       3 8230   88.00   M    0      2       3 8231   82.00   M    1      2       3 8232   70.00   M    2      2       3 8233   54.00   F    1      2       2 8234   73.00   M    0      2       3 8235   70.00   M    0      2       3 8236   67.00   M    4      2       3 8237   50.00   M    0      2       2 8238   70.00   F    1      2       3 8239   60.00   M    0      2       3 8240   33.00   F    2      2       2 8241   60.00   F    5      2       3 8242   79.00   M    4      2       3 8243   74.00   M   21      2       3 8244   59.00   M   14      2       2 8245   60.00   F   11      2       3 8246   81.00   M    8      2       3 8247   65.00   F    7      2       3 8248   65.00   M   12      2       3 8249   50.00   M   17      2       2 8250   65.00   M   12      2       3 8251   84.00   M   17      2       3 8252   60.00   M   13      2       3 8253   69.00   M   18      2       3 8254   75.00   M   17      2       3 8255   87.00   F   12      2       3 8256   68.00   M   11      2       3 8257   46.00   F    8      2       2 8258   67.00   M   17      2       3 8259   56.00   M    9      2       2 8260   60.00   M   10      2       3 8261   61.00   M    8      2       3 8262   68.00   F    8      2       3 8263   76.00   M    8      2       3 8264   63.00   M   12      2       3 8265   79.00   M    7      2       3 8266   45.00   M   10      2       2 8267   73.00   M   12      2       3 8268   62.00   M    2      2       3 8269   45.00   M    8      2       2 8270   61.00   M    5      2       3 8271   66.00   M   23      2       3 8272   85.00   M   10      2       3 8273   47.00   M    3      2       2 8274   74.00   M   10      2       3 8275   51.00   M    4      2       2 8276   60.00   M    4      2       3 8277   65.00   F    1      2       3 8278   75.00   M    0      2       3 8279   67.00   M    5      2       3 8280   50.00   M    1      2       2 8281   70.00   M    0      2       3 8282   60.00   M    1      2       3 8283   57.00   M    1      2       2 8284   29.00   M    1      2       2 8285   64.00   M    4      2       3 8286   55.00   M    3      2       2 8287   50.00   M    0      2       2 8288   66.00   F    0      2       3 8289   52.00   M    2      2       2 8290   73.00   M    9      2       3 8291   54.00   M    9      2       2 8292   80.00   F    0      2       3 8293   57.00   F    8      2       2 8294   58.00   M    1      2       2 8295   65.00   F    2      2       3 8296   56.00   M    6      2       2 8297   67.00   M    2      2       3 8298   64.00   M    1      2       3 8299   40.00   M   23      2       2 8300   52.00   F    2      2       2 8301   59.00   M    1      2       2 8302   76.00   M    2      2       3 8303   73.00   F    4      2       3 8304   60.00   M    0      2       3 8305   70.00   F    1      2       3 8306   31.00   M    0      2       2 8307   60.00   F    0      2       3 8308   53.00   M    0      2       2 8309   74.00   M    1      2       3 8310   85.00   M    0      2       3 8311   60.00   M    1      2       3 8312   80.00   M    0      2       3 8313   60.00   M    1      2       3 8314   67.00   M    2      2       3 8315   40.00   M    3      2       2 8316   64.00   M    4      2       3 8317   35.00   M    1      2       2 8318   85.00   M    1      2       3 8319   45.00   M    0      2       2 8320   65.00   F    4      2       3 8321   70.00   M    0      2       3 8322   70.00   M    0      2       3 8323   65.00   M    1      2       3 8324   70.00   M    0      2       3 8325   56.00   M    3      2       2 8326   71.00   M   28      2       3 8327   45.00   M   18      2       2 8328   75.00   M   16      2       3 8329   72.00   F   21      2       3 8330   74.00   M    9      2       3 8331   69.00   M   16      2       3 8332   65.00   M    9      2       3 8333   83.00   F    6      2       3 8334   65.00   M   14      2       3 8335   40.00   M   12      2       2 8336   58.00   M   11      2       2 8337   56.00   M   11      2       2 8338   60.00   M    8      2       3 8339   83.00   M    5      2       3 8340   75.00   M    1      2       3 8341   63.00   M    8      2       3 8342   45.00   M   12      2       2 8343   65.00   F   10      2       3 8344   60.00   M    9      2       3 8345   50.00   M   10      2       2 8346   85.00   M    2      2       3 8347   80.00   M    4      2       3 8348   70.00   F    9      2       3 8349   76.00   F   10      2       3 8350   63.00   M    7      2       3 8351   65.00   M   11      2       3 8352   62.00   F    3      2       3 8353   47.00   M    1      2       2 8354   66.00   M   10      2       3 8355   62.00   M    9      2       3 8356   57.00   M    3      2       2 8357   65.00   M    2      2       3 8358   75.00   M    9      2       3 8359   78.00   M    4      2       3 8360   85.00   M    3      2       3 8361   48.00   M    4      2       2 8362   42.00   M   10      2       2 8363   35.00   M    8      2       2 8364   82.00   F    5      2       3 8365   43.00   M    2      2       2 8366   72.00   F    2      2       3 8367   55.00   M    4      2       2 8368   60.00   M    2      2       3 8369   65.00   F    8      2       3 8370   72.00   F    3      2       3 8371   74.00   M    2      2       3 8372   56.00   M    3      2       2 8373   66.00   F    1      2       3 8374   62.00   F   10      2       3 8375   58.00   F    0      2       2 8376   65.00   M    6      2       3 8377   75.00   F    6      2       3 8378   60.00   M    2      2       3 8379   62.00   M    0      2       3 8380   65.00   M    3      2       3 8381   66.00   F    3      2       3 8382   74.00   M    2      2       3 8383   72.00   F    3      2       3 8384   66.00   M    2      2       3 8385   76.00   M    7      2       3 8386   24.00   M    1      2       2 8387   62.00   M    1      2       3 8388   59.00   F    4      2       2 8389   50.00   M    6      2       2 8390   75.00   M    5      2       3 8391   60.00   M    1      2       3 8392   55.00   F    1      2       2 8393   61.00   M   17      2       3 8394   34.00   M    0      2       2 8395   74.00   M    3      2       3 8396   67.00   F    0      2       3 8397   62.00   F    3      2       3 8398   79.00   M    5      2       3 8399   65.00   F    2      2       3 8400   68.00   M    1      2       3 8401   57.00   M    0      2       2 8402   40.00   F    2      2       2 8403   65.00   M    1      2       3 8404   70.00   F    0      2       3 8405   40.00   F    3      2       2 8406   45.00   F    1      2       2 8407   38.00   M    1      2       2 8408   70.00   F    2      2       3 8409   40.00   M    1      2       2 8410   58.00   M    0      2       2 8411   65.00   F    0      2       3 8412   50.00   M    0      2       2 8413   68.00   M   16      2       3 8414   56.00   M   12      2       2 8415   68.00   M    8      2       3 8416   79.00   M   12      2       3 8417   84.00   F   13      2       3 8418   43.00   M   15      2       2 8419   80.00   F    7      2       3 8420   60.00   M    5      2       3 8421   52.00   M   13      2       2 8422   68.00   M    2      2       3 8423   82.00   M    3      2       3 8424   68.00   F    8      2       3 8425   57.00   M    8      2       2 8426   70.00   M   10      2       3 8427   48.00   M   10      2       2 8428   65.00   M    8      2       3 8429   50.00   F    3      2       2 8430   60.00   F    5      2       3 8431   72.00   M    7      2       3 8432   78.00   M   13      2       3 8433   62.00   M    8      2       3 8434   61.00   M    7      2       3 8435   62.00   F    6      2       3 8436   75.00   M    3      2       3 8437   56.00   F    7      2       2 8438   60.00   F    2      2       3 8439   28.00   M   13      2       2 8440   62.00   M    4      2       3 8441   40.00   F    4      2       2 8442   74.00   M    9      2       3 8443   67.00   F    0      2       3 8444   56.00   M    5      2       2 8445   55.00   M    6      2       2 8446   78.00   M    1      2       3 8447   80.00   M    5      2       3 8448   46.00   M    5      2       2 8449   90.00   M    3      2       3 8450   46.00   M    4      2       2 8451   45.00   M    5      2       2 8452   48.00   F    2      2       2 8453   72.00   F    6      2       3 8454   65.00   M    1      2       3 8455   66.00   F    3      2       3 8456   70.00   M    0      2       3 8457   48.00   F   11      2       2 8458   84.00   F    5      2       3 8459   94.00   F    2      2       3 8460   65.00   M    1      2       3 8461   40.00   M    0      2       2 8462   76.00   M    7      2       3 8463   47.00   M    7      2       2 8464   76.00   F    4      2       3 8465   71.00   M    4      2       3 8466   34.00   M    0      2       2 8467   80.00   F    2      2       3 8468   52.00   F    2      2       2 8469   88.00   F    0      2       3 8470   65.00   M    1      2       3 8471   55.00   M    1      2       2 8472   30.00   F    3      2       2 8473   60.00   F    3      2       3 8474   61.00   M    1      2       3 8475   73.00   M    1      2       3 8476   75.00   M    0      2       3 8477   73.00   M    2      2       3 8478   62.00   M    1      2       3 8479   59.00   F    1      2       2 8480   76.00   M    1      2       3 8481   68.00   M    3      2       3 8482   70.00   M    0      2       3 8483   80.00   M   10      2       3 8484   45.00   F    0      2       2 8485   37.00   M    2      2       2 8486   80.00   M    2      2       3 8487   55.00   M    1      2       2 8488   72.00   F    1      2       3 8489   75.00   M    0      2       3 8490   46.00   F    1      2       2 8491   72.00   F    0      2       3 8492   80.00   M    9      2       3 8493   83.00   M   19      2       3 8494   36.00   M   13      2       2 8495   55.00   M    0      2       2 8496   62.00   M   18      2       3 8497   58.00   F    7      2       2 8498   68.00   M    8      2       3 8499   72.00   F   10      2       3 8500   65.00   M    2      2       3 8501   55.00   M    8      2       2 8502   72.00   M    9      2       3 8503   63.00   M   10      2       3 8504   56.00   M    1      2       2 8505   70.00   F   12      2       3 8506   40.00   M    0      2       2 8507   62.00   M   11      2       3 8508   70.00   M    0      2       3 8509   72.00   M    7      2       3 8510   85.00   F    9      2       3 8511   65.00   M    1      2       3 8512   60.00   F    9      2       3 8513   45.00   F    7      2       2 8514   56.00   F    9      2       2 8515   63.00   M    2      2       3 8516   72.00   M    4      2       3 8517   41.00   F    0      2       2 8518   57.00   M    4      2       2 8519   70.00   M    0      2       3 8520   57.00   F    0      2       2 8521   91.00   M    9      2       3 8522   75.00   M    0      2       3 8523   58.00   M    4      2       2 8524   70.00   F    0      2       3 8525   46.00   M    5      2       2 8526   63.00   M    4      2       3 8527   71.00   F    3      2       3 8528   35.00   F    3      2       2 8529   55.00   M    1      2       2 8530   46.00   M    3      2       2 8531   65.00   M    6      2       3 8532   67.00   M    5      2       3 8533   52.00   F    4      2       2 8534   62.00   M    2      2       3 8535   50.00   F    2      2       2 8536   58.00   F    0      2       2 8537   54.00   M    2      2       2 8538   65.00   M    1      2       3 8539   81.00   F    4      2       3 8540   80.00   M    1      2       3 8541   35.00   M    1      2       2 8542   50.00   M   19      2       2 8543   88.00   M    0      2       3 8544   52.00   F    1      2       2 8545   65.00   M    1      2       3 8546   46.00   M    1      2       2 8547   60.00   M    0      2       3 8548   56.00   M    0      2       2 8549   55.00   F    1      2       2 8550   84.00   F    1      2       3 8551   41.00   M    0      2       2 8552   51.00   M    0      2       2 8553   23.00   F    0      2       2 8554   71.00   F    1      2       3 8555   60.00   F    8      2       3 8556   52.00   M    4      2       2 8557   56.00   M    0      2       2 8558   61.00   M    1      2       3 8559   50.00   M    1      2       2 8560   52.00   M    0      2       2 8561   59.00   M    0      2       2 8562   71.00   M    3      2       3 8563   88.00   F    5      2       3 8564   52.00   F    0      2       2 8565   54.00   F    1      2       2 8566   48.00   M    0      2       2 8567   40.00   F    1      2       2 8568   58.00   F    0      2       2 8569   59.00   M    0      2       2 8570   37.00   M    1      2       2 8571   47.00   M    1      2       2 8572   63.00   M    3      2       3 8573   63.00   F    0      2       3 8574   46.00   M    0      2       2 8575   64.00   M    2      2       3 8576   54.00   M    0      2       2 8577   71.00   M    0      2       3 8578   64.00   M    4      2       3 8579   44.00   M    2      2       2 8580   74.00   F    2      2       3 8581   83.00   M    0      2       3 8582   65.00   F    2      2       3 8583   70.00   F    0      2       3 8584   58.00   F    2      2       2 8585   45.00   M    0      2       2 8586   62.00   M   10      2       3 8587   54.00   M    0      2       2 8588   40.00   M    0      2       2 8589   45.00   M   26      2       2 8590   52.00   M   23      2       2 8591   47.00   M    1      2       2 8592   58.00   M    1      2       2 8593   66.00   M    4      2       3 8594   79.00   F    7      2       3 8595   52.00   M    8      2       2 8596   59.00   M    4      2       2 8597   60.00   F    0      2       3 8598   45.00   F   20      2       2 8599   62.00   F    6      2       3 8600   84.00   M    9      2       3 8601   56.00   F    0      2       2 8602   77.00   F    2      2       3 8603   63.00   M    8      2       3 8604   68.00   F    4      2       3 8605   60.00   M   14      2       3 8606   69.00   F   16      2       3 8607   85.00   F   12      2       3 8608   45.00   M   14      2       2 8609   55.00   M    0      2       2 8610   66.00   M    9      2       3 8611   60.00   F    0      2       3 8612   55.00   M    0      2       2 8613   81.00   M   11      2       3 8614   62.00   M    8      2       3 8615   42.00   M   15      2       2 8616   69.00   M   19      2       3 8617   75.00   M    5      2       3 8618   68.00   F    4      2       3 8619   64.00   M    4      2       3 8620   60.00   M    0      2       3 8621   40.00   M   16      2       2 8622   40.00   F    5      2       2 8623   61.00   M    9      2       3 8624   23.00   M    1      2       2 8625   65.00   M    3      2       3 8626   69.00   M    9      2       3 8627   62.00   M   10      2       3 8628   60.00   M    1      2       3 8629   74.00   M    0      2       3 8630   85.00   M    3      2       3 8631   65.00   M    6      2       3 8632   61.00   M    8      2       3 8633   66.00   M    4      2       3 8634   55.00   M    8      2       2 8635   65.00   F    7      2       3 8636   69.00   M    9      2       3 8637   65.00   F   12      2       3 8638   62.00   M    7      2       3 8639   55.00   M    3      2       2 8640   75.00   M    9      2       3 8641   53.00   F   15      2       2 8642   78.00   M    6      2       3 8643   76.00   F    8      2       3 8644   42.00   M    7      2       2 8645   70.00   M    0      2       3 8646   50.00   M    2      2       2 8647   56.00   M    5      2       2 8648   70.00   M    4      2       3 8649   47.00   F    1      2       2 8650   65.00   M    1      2       3 8651   78.00   F    5      2       3 8652   74.00   M    0      2       3 8653    5.00   M    4      2       1 8654   92.00   M    1      2       3 8655   76.00   M    5      2       3 8656   69.00   F    4      2       3 8657   67.00   M    4      2       3 8658   78.00   M    1      2       3 8659   60.00   F    6      2       3 8660   55.00   M    4      2       2 8661   65.00   M    4      2       3 8662   47.00   M    3      2       2 8663   60.00   M    2      2       3 8664   82.00   M    0      2       3 8665   58.00   M    2      2       2 8666   71.00   M    3      2       3 8667   84.00   M    2      2       3 8668   59.00   M    0      2       2 8669   72.00   M    1      2       3 8670   42.00   M    1      2       2 8671   60.00   M    1      2       3 8672   82.00   F    1      2       3 8673   48.00   F    2      2       2 8674   68.00   M    3      2       3 8675   86.00   M    2      2       3 8676   75.00   F    1      2       3 8677   23.00   M    0      2       2 8678   68.00   F    2      2       3 8679   90.00   M    1      2       3 8680   70.00   F    1      2       3 8681   50.00   M    4      2       2 8682   45.00   F    4      2       2 8683   68.00   M    4      2       3 8684   73.00   M    1      2       3 8685   81.00   M   18      2       3 8686   84.00   F    7      2       3 8687   73.00   M   22      2       3 8688   67.00   M   10      2       3 8689   80.00   F   20      2       3 8690   52.00   F   16      2       2 8691   62.00   M    2      2       3 8692   73.00   F    7      2       3 8693   66.00   M    0      2       3 8694   70.00   F   11      2       3 8695   42.00   M    1      2       2 8696   66.00   M    6      2       3 8697   65.00   M   11      2       3 8698   67.00   M    7      2       3 8699   55.00   F    8      2       2 8700   65.00   M    0      2       3 8701   40.00   M    1      2       2 8702   55.00   M    9      2       2 8703   66.00   M    0      2       3 8704   62.00   M    2      2       3 8705   78.00   F    7      2       3 8706   78.00   M    0      2       3 8707   70.00   M    7      2       3 8708   75.00   M   10      2       3 8709   50.00   M    0      2       2 8710   45.00   F    2      2       2 8711   68.00   F    4      2       3 8712   53.00   M    7      2       2 8713   59.00   M    5      2       2 8714   68.00   M    0      2       3 8715   75.00   M    7      2       3 8716   70.00   M    7      2       3 8717   63.00   M    9      2       3 8718   90.00   M    6      2       3 8719   78.00   M    8      2       3 8720   82.00   F    6      2       3 8721   56.00   M    4      2       2 8722   40.00   M    1      2       2 8723   48.00   M    3      2       2 8724   73.00   M    3      2       3 8725   76.00   M    6      2       3 8726   59.00   M    5      2       2 8727   35.00   M    7      2       2 8728   59.00   M    4      2       2 8729   48.00   M    0      2       2 8730   52.00   M    3      2       2 8731   55.00   M    7      2       2 8732   65.00   F    3      2       3 8733   82.00   F    2      2       3 8734   72.00   M    2      2       3 8735   76.00   M    4      2       3 8736   60.00   F    9      2       3 8737   70.00   M    8      2       3 8738   53.00   F    0      2       2 8739   67.00   F    4      2       3 8740   74.00   M    5      2       3 8741   74.00   M    1      2       3 8742   75.00   M    3      2       3 8743   42.00   M    2      2       2 8744   82.00   M    0      2       3 8745   58.00   M    3      2       2 8746   63.00   M    0      2       3 8747   79.00   M   32      2       3 8748   85.00   F    2      2       3 8749   79.00   M    5      2       3 8750   69.00   M    2      2       3 8751   47.00   F    5      2       2 8752   65.00   M    1      2       3 8753   80.00   M    0      2       3 8754   72.00   M    2      2       3 8755   60.00   M    3      2       3 8756   36.00   M    2      2       2 8757   62.00   M    4      2       3 8758   50.00   M    0      2       2 8759   67.00   F    0      2       3 8760   62.00   M    3      2       3 8761   68.00   M    3      2       3 8762   70.00   F    2      2       3 8763   85.00   M    2      2       3 8764   74.00   F    1      2       3 8765   51.00   M    2      2       2 8766   54.00   M    1      2       2 8767   52.00   M    0      2       2 8768   86.00   M    2      2       3 8769   77.00   M    2      2       3 8770   45.00   M    0      2       2 8771   51.00   M    1      2       2 8772   70.00   F    3      2       3 8773   58.00   M   23      2       2 8774   28.00   M    9      2       2 8775   58.00   M    0      2       2 8776   48.00   M    1      2       2 8777   48.00   M    7      2       2 8778   59.00   M    5      2       2 8779   90.00   M    2      2       3 8780   60.00   F    8      2       3 8781   74.00   M    4      2       3 8782   72.00   F    1      2       3 8783   65.00   F    2      2       3 8784   70.00   M   14      2       3 8785   77.00   M    0      2       3 8786   52.00   M   10      2       2 8787   77.00   M    3      2       3 8788   66.00   M   18      2       3 8789   64.00   M   24      2       3 8790   79.00   M   12      2       3 8791   85.00   M    9      2       3 8792   81.00   M   12      2       3 8793   70.00   M   16      2       3 8794   51.00   M    4      2       2 8795   54.00   M   10      2       2 8796   52.00   M    0      2       2 8797   46.00   M   18      2       2 8798   80.00   F   16      2       3 8799   58.00   M    8      2       2 8800   65.00   M    5      2       3 8801   87.00   F    8      2       3 8802   74.00   M    2      2       3 8803    2.00   F    3      2       1 8804   88.00   F    4      2       3 8805   56.00   M    6      2       2 8806   42.00   M   10      2       2 8807   68.00   M   13      2       3 8808   45.00   M    0      2       2 8809   62.00   M   14      2       3 8810   59.00   F    5      2       2 8811   65.00   M    5      2       3 8812   70.00   M   10      2       3 8813   68.00   F    0      2       3 8814   70.00   M   13      2       3 8815   57.00   F    3      2       2 8816   64.00   M    9      2       3 8817   79.00   F    0      2       3 8818   63.00   F    9      2       3 8819   70.00   F    1      2       3 8820   55.00   M   12      2       2 8821   90.00   M   11      2       3 8822   52.00   F   10      2       2 8823   27.00   M    7      2       2 8824   84.00   F    6      2       3 8825   64.00   F    2      2       3 8826   75.00   M    8      2       3 8827   70.00   M    9      2       3 8828   45.00   M    8      2       2 8829   72.00   M    4      2       3 8830   55.00   M    6      2       2 8831   40.00   M    0      2       2 8832   78.00   M   10      2       3 8833   65.00   F    2      2       3 8834   78.00   M    1      2       3 8835   57.00   F    8      2       2 8836   68.00   F    5      2       3 8837   60.00   M    1      2       3 8838   36.00   M    4      2       2 8839   80.00   M    1      2       3 8840   60.00   F    8      2       3 8841   55.00   F    8      2       2 8842   50.00   M    0      2       2 8843   59.00   M    3      2       2 8844   68.00   M    8      2       3 8845   63.00   M    1      2       3 8846   52.00   M    6      2       2 8847   50.00   F    5      2       2 8848   58.00   F    2      2       2 8849   71.00   F    7      2       3 8850   48.00   M    1      2       2 8851   52.00   M    6      2       2 8852   70.00   M    1      2       3 8853   58.00   F    0      2       2 8854   65.00   M    5      2       3 8855   40.00   M    7      2       2 8856   75.00   M    4      2       3 8857   86.00   M    6      2       3 8858   58.00   M    6      2       2 8859   65.00   F    2      2       3 8860   56.00   M    0      2       2 8861   55.00   M    5      2       2 8862   72.00   M    6      2       3 8863   60.00   F    6      2       3 8864   64.00   M    5      2       3 8865   54.00   M    2      2       2 8866   96.00   F    3      2       3 8867   58.00   M    5      2       2 8868   46.00   M    1      2       2 8869   58.00   M    0      2       2 8870   60.00   F    5      2       3 8871   83.00   F    0      2       3 8872   53.00   M    6      2       2 8873   67.00   M    4      2       3 8874   55.00   M    4      2       2 8875   85.00   M    1      2       3 8876   25.00   F    1      2       2 8877   69.00   M    2      2       3 8878   48.00   M    4      2       2 8879   80.00   M    1      2       3 8880   75.00   M    4      2       3 8881   80.00   F    2      2       3 8882   70.00   M    5      2       3 8883   86.00   M    4      2       3 8884   67.00   F    4      2       3 8885   85.00   F   10      2       3 8886   65.00   F    0      2       3 8887   63.00   M    3      2       3 8888   52.00   M    0      2       2 8889   94.00   F    2      2       3 8890   75.00   M    3      2       3 8891   46.00   M    0      2       2 8892   58.00   M    2      2       2 8893   61.00   M    3      2       3 8894   55.00   M    3      2       2 8895   55.00   F    2      2       2 8896   63.00   F    1      2       3 8897   59.00   F    2      2       2 8898   56.00   M    0      2       2 8899   74.00   M    1      2       3 8900   33.00   M    0      2       2 8901   40.00   F    0      2       2 8902   78.00   M   26      2       3 8903   50.00   M   25      2       2 8904   58.00   F    0      2       2 8905   72.00   M   17      2       3 8906   82.00   M    9      2       3 8907   72.00   M   12      2       3 8908   50.00   M    4      2       2 8909   75.00   M   20      2       3 8910   71.00   M   16      2       3 8911   67.00   F    7      2       3 8912   76.00   M   16      2       3 8913   41.00   M   16      2       2 8914   67.00   M   16      2       3 8915   69.00   F   14      2       3 8916   56.00   M   17      2       2 8917   55.00   M    2      2       2 8918   36.00   M    1      2       2 8919   71.00   F    8      2       3 8920   36.00   M   13      2       2 8921   76.00   F    9      2       3 8922   56.00   M    9      2       2 8923   87.00   F   14      2       3 8924   72.00   M   11      2       3 8925   55.00   M    5      2       2 8926   57.00   F   13      2       2 8927   75.00   M    9      2       3 8928   52.00   M   11      2       2 8929   76.00   M   11      2       3 8930   68.00   M    6      2       3 8931   70.00   M   10      2       3 8932   62.00   M   10      2       3 8933   66.00   M   10      2       3 8934   53.00   F    5      2       2 8935   78.00   M    6      2       3 8936   54.00   M    3      2       2 8937   65.00   M    7      2       3 8938   39.00   M    7      2       2 8939   64.00   M    5      2       3 8940   44.00   M    8      2       2 8941   65.00   M    8      2       3 8942   64.00   M    1      2       3 8943   67.00   M    1      2       3 8944   54.00   F    6      2       2 8945   77.00   F    0      2       3 8946   65.00   F    6      2       3 8947   49.00   M    1      2       2 8948   48.00   M    6      2       2 8949   45.00   M    7      2       2 8950   39.00   M    2      2       2 8951   50.00   M    8      2       2 8952   62.00   F    6      2       3 8953   56.00   M    3      2       2 8954   59.00   M    5      2       2 8955   58.00   M    6      2       2 8956   83.00   M   10      2       3 8957   65.00   M    2      2       3 8958   72.00   M    1      2       3 8959   87.00   F    3      2       3 8960   70.00   M    8      2       3 8961   72.00   M    3      2       3 8962   70.00   M    3      2       3 8963   51.00   M    7      2       2 8964   72.00   M    3      2       3 8965   65.00   F    5      2       3 8966   37.00   M    6      2       2 8967   70.00   M    0      2       3 8968   35.00   M    5      2       2 8969   57.00   M    6      2       2 8970   82.00   F    6      2       3 8971   66.00   F    1      2       3 8972   24.00   M    2      2       2 8973   70.00   M    3      2       3 8974   63.00   M    3      2       3 8975   64.00   M    6      2       3 8976   68.00   M    4      2       3 8977   70.00   F    9      2       3 8978   78.00   M    0      2       3 8979   60.00   M    0      2       3 8980   40.00   M    0      2       2 8981   65.00   M    5      2       3 8982   74.00   M    3      2       3 8983   47.00   F    4      2       2 8984   75.00   M    6      2       3 8985   47.00   F    3      2       2 8986   45.00   M    4      2       2 8987   72.00   M    4      2       3 8988   58.00   M    4      2       2 8989   81.00   F    2      2       3 8990   20.00   M    2      2       2 8991   78.00   M    3      2       3 8992   73.00   M    2      2       3 8993   80.00   M    0      2       3 8994   54.00   M    2      2       2 8995   61.00   M    5      2       3 8996   55.00   M    3      2       2 8997   46.00   M    3      2       2 8998   47.00   M    1      2       2 8999   45.00   F    2      2       2 9000   73.00   M    7      2       3 9001   89.00   F    2      2       3 9002   53.00   M    7      2       2 9003   57.00   F    2      2       2 9004   65.00   M    0      2       3 9005   42.00   M    0      2       2 9006   55.00   M    1      2       2 9007   34.00   M    2      2       2 9008   50.00   M    2      2       2 9009   61.00   F    4      2       3 9010   77.00   F    4      2       3 9011   85.00   M    0      2       3 9012   80.00   F    0      2       3 9013   32.00   F    2      2       2 9014   75.00   F    1      2       3 9015   62.00   M    1      2       3 9016   37.00   F    2      2       2 9017   35.00   F    0      2       2 9018   33.00   M    1      2       2 9019   67.00   M    1      2       3 9020   57.00   M    0      2       2 9021   82.00   M    1      2       3 9022   70.00   M    0      2       3 9023   36.00   M    7      2       2 9024   42.00   F    3      2       2 9025   55.00   F    3      2       2 9026   82.00   F    1      2       3 9027   58.00   M    1      2       2 9028   38.00   F   23      2       2 9029   78.00   M   16      2       3 9030   75.00   M   18      2       3 9031   79.00   F   17      2       3 9032   66.00   F   20      2       3 9033   60.00   M    3      2       3 9034   65.00   M    8      2       3 9035   50.00   M    5      2       2 9036   62.00   M    5      2       3 9037   65.00   M    0      2       3 9038   61.00   F   12      2       3 9039   82.00   M    5      2       3 9040   72.00   M    7      2       3 9041   62.00   M    6      2       3 9042   81.00   M    8      2       3 9043   68.00   M    8      2       3 9044   57.00   M    3      2       2 9045   89.00   F   13      2       3 9046   75.00   M   20      2       3 9047   72.00   M   11      2       3 9048   50.00   F   10      2       2 9049   60.00   M    4      2       3 9050   40.00   M    1      2       2 9051   62.00   F    0      2       3 9052   64.00   F    9      2       3 9053   73.00   M   10      2       3 9054   83.00   M    2      2       3 9055   85.00   M    8      2       3 9056   70.00   M   10      2       3 9057   76.00   F    8      2       3 9058   68.00   M    8      2       3 9059   50.00   F    4      2       2 9060   72.00   M    2      2       3 9061   55.00   M   11      2       2 9062   75.00   M   10      2       3 9063   75.00   M    4      2       3 9064   55.00   M    0      2       2 9065   43.00   M    2      2       2 9066   66.00   F    7      2       3 9067   91.00   M    5      2       3 9068   40.00   M    3      2       2 9069   55.00   M    4      2       2 9070   44.00   M    8      2       2 9071   55.00   F    7      2       2 9072   65.00   M    4      2       3 9073   54.00   M    0      2       2 9074   61.00   M    2      2       3 9075   85.00   M    5      2       3 9076   66.00   M    5      2       3 9077   61.00   M    7      2       3 9078   78.00   M    3      2       3 9079   57.00   M    1      2       2 9080   77.00   F    5      2       3 9081   57.00   M    5      2       2 9082   76.00   F    5      2       3 9083   62.00   M    3      2       3 9084   66.00   M    5      2       3 9085   63.00   M    4      2       3 9086   55.00   M    3      2       2 9087   61.00   F    4      2       3 9088   56.00   M    4      2       2 9089   80.00   F    4      2       3 9090   65.00   M    3      2       3 9091   46.00   M    0      2       2 9092   62.00   F    7      2       3 9093   74.00   M    5      2       3 9094   60.00   F    5      2       3 9095   52.00   M    8      2       2 9096   65.00   M    3      2       3 9097   82.00   M    0      2       3 9098   55.00   F    1      2       2 9099   74.00   M    1      2       3 9100   36.00   F    1      2       2 9101   67.00   M    3      2       3 9102   87.00   M    2      2       3 9103   89.00   M    2      2       3 9104   65.00   F    4      2       3 9105   38.00   M    4      2       2 9106   52.00   F    1      2       2 9107   74.00   M    1      2       3 9108   45.00   M    2      2       2 9109   65.00   M    5      2       3 9110   85.00   M    3      2       3 9111   50.00   M    0      2       2 9112   38.00   M    1      2       2 9113   50.00   M    3      2       2 9114   70.00   M    5      2       3 9115   39.00   M    5      2       2 9116   60.00   M    0      2       3 9117   38.00   M    5      2       2 9118   70.00   M    0      2       3 9119   70.00   M    3      2       3 9120   64.00   M    4      2       3 9121   80.00   F    1      2       3 9122   75.00   M    2      2       3 9123   73.00   M    0      2       3 9124   67.00   M   42      2       3 9125   65.00   M    0      2       3 9126   80.00   M    0      2       3 9127   62.00   M   49      2       3 9128   65.00   M    7      2       3 9129   70.00   M   16      2       3 9130   73.00   M   13      2       3 9131   23.00   M    2      2       2 9132   42.00   F   10      2       2 9133   75.00   F   18      2       3 9134   17.00   M    0      2       1 9135   55.00   M    8      2       2 9136   49.00   M   18      2       2 9137   49.00   M    0      2       2 9138   60.00   M    3      2       3 9139   73.00   F    4      2       3 9140   62.00   M   10      2       3 9141   87.00   F    0      2       3 9142   33.00   M    5      2       2 9143   60.00   F    5      2       3 9144   57.00   M    0      2       2 9145   47.00   M   11      2       2 9146   58.00   M    7      2       2 9147   73.00   F    2      2       3 9148   65.00   F   12      2       3 9149   78.00   M    5      2       3 9150   59.00   F   12      2       2 9151   75.00   M   11      2       3 9152   62.00   M    8      2       3 9153   38.00   F    0      2       2 9154   93.00   M   11      2       3 9155   70.00   F    7      2       3 9156   24.00   M   11      2       2 9157   50.00   M    6      2       2 9158   45.00   M    7      2       2 9159   86.00   M    8      2       3 9160   42.00   M    7      2       2 9161   60.00   M    5      2       3 9162   36.00   M    6      2       2 9163   50.00   F    9      2       2 9164   61.00   F    5      2       3 9165   65.00   M    6      2       3 9166   59.00   M    5      2       2 9167   62.00   M    5      2       3 9168   86.00   F    5      2       3 9169   75.00   M    8      2       3 9170   70.00   F    1      2       3 9171   70.00   M    5      2       3 9172   60.00   M    3      2       3 9173   75.00   F    3      2       3 9174   53.00   M    3      2       2 9175   83.00   M    4      2       3 9176   65.00   M    2      2       3 9177   51.00   M    0      2       2 9178   77.00   F    5      2       3 9179   78.00   M    1      2       3 9180   48.00   M    2      2       2 9181   78.00   M    3      2       3 9182   62.00   M   13      2       3 9183   85.00   M    2      2       3 9184   75.00   M    2      2       3 9185   52.00   F    0      2       2 9186   75.00   F    4      2       3 9187   70.00   M    0      2       3 9188   83.00   M    0      2       3 9189   70.00   M    1      2       3 9190   55.00   M    2      2       2 9191   53.00   M    1      2       2 9192   75.00   M    0      2       3 9193   60.00   F    0      2       3 9194   41.00   M   21      2       2 9195   96.00   F   20      2       3 9196   54.00   F    4      2       2 9197   53.00   M   15      2       2 9198   43.00   M    7      2       2 9199   66.00   M   11      2       3 9200   45.00   F   13      2       2 9201   55.00   F   15      2       2 9202   47.00   M   11      2       2 9203   65.00   M    1      2       3 9204   77.00   M   12      2       3 9205   84.00   F   13      2       3 9206   53.00   M   13      2       2 9207   45.00   M    9      2       2 9208   76.00   M    6      2       3 9209   80.00   F    3      2       3 9210   48.00   F    8      2       2 9211   35.00   M   10      2       2 9212   71.00   M   11      2       3 9213   56.00   M   10      2       2 9214   58.00   M   10      2       2 9215   50.00   M    3      2       2 9216   55.00   F    8      2       2 9217   70.00   F    4      2       3 9218   80.00   M    5      2       3 9219   45.00   M    7      2       2 9220   63.00   M    6      2       3 9221   64.00   M    9      2       3 9222   32.00   M    9      2       2 9223   43.00   M    7      2       2 9224   65.00   M    4      2       3 9225   55.00   F    6      2       2 9226   58.00   F    9      2       2 9227   36.00   M    6      2       2 9228   69.00   F    1      2       3 9229   45.00   M    4      2       2 9230   48.00   F    0      2       2 9231   60.00   M    8      2       3 9232   24.00   M    4      2       2 9233   55.00   M    5      2       2 9234   70.00   M    8      2       3 9235   93.00   M    4      2       3 9236   60.00   M    5      2       3 9237   85.00   M    8      2       3 9238   65.00   F    6      2       3 9239   54.00   M    6      2       2 9240   66.00   M    3      2       3 9241   70.00   M    4      2       3 9242   37.00   M    1      2       2 9243   35.00   M    6      2       2 9244   70.00   F    2      2       3 9245   35.00   M    6      2       2 9246   60.00   M    5      2       3 9247   86.00   F    0      2       3 9248   89.00   M    2      2       3 9249   55.00   F    2      2       2 9250   55.00   M    5      2       2 9251   48.00   M    3      2       2 9252   57.00   F    0      2       2 9253   62.00   M    3      2       3 9254   75.00   F    3      2       3 9255   83.00   M    2      2       3 9256   70.00   M    5      2       3 9257   73.00   M    0      2       3 9258   40.00   M   19      2       2 9259   72.00   F    4      2       3 9260   76.00   M    0      2       3 9261   75.00   M    0      2       3 9262   43.00   M    1      2       2 9263   32.00   M    1      2       2 9264   76.00   F    2      2       3 9265   95.00   M    0      2       3 9266   45.00   F    1      2       2 9267   62.00   F    0      2       3 9268   89.00   M    1      2       3 9269   74.00   M    0      2       3 9270   60.00   F   13      2       3 9271   55.00   M    7      2       2 9272   56.00   F    1      2       2 9273   63.00   M    0      2       3 9274   48.00   M    1      2       2 9275   68.00   F    0      2       3 9276   75.00   M    0      2       3 9277   81.00   M    0      2       3 9278   65.00   M    2      2       3 9279   54.00   F    0      2       2 9280   76.00   M    1      2       3 9281   70.00   M    0      2       3 9282   75.00   M    6      2       3 9283   35.00   M   16      2       2 9284   72.00   M    9      2       3 9285   62.00   F    7      2       3 9286   46.00   M    0      2       2 9287   48.00   M   14      2       2 9288   65.00   M   14      2       3 9289   81.00   M    8      2       3 9290   43.00   M   14      2       2 9291   63.00   F    2      2       3 9292   62.00   F    1      2       3 9293   77.00   F   10      2       3 9294   75.00   M   12      2       3 9295   70.00   M    3      2       3 9296   45.00   M    3      2       2 9297   73.00   M    3      2       3 9298   58.00   M    4      2       2 9299   80.00   M    7      2       3 9300   64.00   F    6      2       3 9301   72.00   M   12      2       3 9302   88.00   M    2      2       3 9303   58.00   M    4      2       2 9304   39.00   F   11      2       2 9305   84.00   M    4      2       3 9306   56.00   M    5      2       2 9307   36.00   M    9      2       2 9308   68.00   F    2      2       3 9309   63.00   F   10      2       3 9310   47.00   M    5      2       2 9311   73.00   M    5      2       3 9312   70.00   F    8      2       3 9313   72.00   M    8      2       3 9314   80.00   F    6      2       3 9315   72.00   M    8      2       3 9316   68.00   F    8      2       3 9317   85.00   F    8      2       3 9318   51.00   M    3      2       2 9319   58.00   M    9      2       2 9320   70.00   M   10      2       3 9321   70.00   M    7      2       3 9322   53.00   M    4      2       2 9323   74.00   M    3      2       3 9324   98.00   M   14      2       3 9325   74.00   M    6      2       3 9326   55.00   F   10      2       2 9327   45.00   M   14      2       2 9328   72.00   M    9      2       3 9329   64.00   F   11      2       3 9330   72.00   M    4      2       3 9331   50.00   F    3      2       2 9332   40.00   M   26      2       2 9333   84.00   M   16      2       3 9334   55.00   F   17      2       2 9335   66.00   M   12      2       3 9336   52.00   M   19      2       2 9337   54.00   F   23      2       2 9338   70.00   M    3      2       3 9339   78.00   M   24      2       3 9340   55.00   M   43      2       2 9341   85.00   M    1      2       3 9342   62.00   F   51      2       3 9343   50.00   M    3      2       2 9344   72.00   M   20      2       3 9345   80.00   M    4      2       3 9346   78.00   M    1      2       3 9347   56.00   F    1      2       2 9348   40.00   M    0      2       2 9349   50.00   F    4      2       2 9350   47.00   M    2      2       2 9351   65.00   M    1      2       3 9352   63.00   M    7      2       3 9353   73.00   M    4      2       3 9354   67.00   M    4      2       3 9355   65.00   F    3      2       3 9356   70.00   M    4      2       3 9357   62.00   M    2      2       3 9358   50.00   M    3      2       2 9359   72.00   M    4      2       3 9360   72.00   M    0      2       3 9361   58.00   M    1      2       2 9362   66.00   M    6      2       3 9363   29.00   M    3      2       2 9364   77.00   M    1      2       3 9365   48.00   F    0      2       2 9366   77.00   M    1      2       3 9367   55.00   F   31      2       2 9368   56.00   M    1      2       2 9369   64.00   M    0      2       3 9370   26.00   M    0      2       2 9371   58.00   M    0      2       2 9372   56.00   M    1      2       2 9373   51.00   F    5      2       2 9374   75.00   M    0      2       3 9375   48.00   M    5      2       2 9376   82.00   M    4      2       3 9377   64.00   M    0      2       3 9378   60.00   F    4      2       3 9379   53.00   F    7      2       2 9380   76.00   M   13      2       3 9381   28.00   M   10      2       2 9382   70.00   M   20      2       3 9383   68.00   M   20      2       3 9384   70.00   F    1      2       3 9385   72.00   M    5      2       3 9386   65.00   M    4      2       3 9387   60.00   F    4      2       3 9388   88.00   M    4      2       3 9389   56.00   M    3      2       2 9390   55.00   F   13      2       2 9391   73.00   M    3      2       3 9392   66.00   M   10      2       3 9393   72.00   M   11      2       3 9394   64.00   M    4      2       3 9395   52.00   M    0      2       2 9396   46.00   M    6      2       2 9397   72.00   M    0      2       3 9398   65.00   F   11      2       3 9399   68.00   M   11      2       3 9400   44.00   F    3      2       2 9401   73.00   M    3      2       3 9402   75.00   M    2      2       3 9403   66.00   M    7      2       3 9404   64.00   F   13      2       3 9405   72.00   M   11      2       3 9406   68.00   F    5      2       3 9407   87.00   M    2      2       3 9408   71.00   M    8      2       3 9409   63.00   M   11      2       3 9410   56.00   M   12      2       2 9411   55.00   F   13      2       2 9412   42.00   M   13      2       2 9413   72.00   M    9      2       3 9414   69.00   M    7      2       3 9415   71.00   F    1      2       3 9416   58.00   F    0      2       2 9417   57.00   M    9      2       2 9418   75.00   M    8      2       3 9419   85.00   M    7      2       3 9420   71.00   M    7      2       3 9421   62.00   M    9      2       3 9422   68.00   M    4      2       3 9423   67.00   M   15      2       3 9424   69.00   M    5      2       3 9425   75.00   M    8      2       3 9426   79.00   M    5      2       3 9427   57.00   M    7      2       2 9428   40.00   M    5      2       2 9429   86.00   M    0      2       3 9430   75.00   M    6      2       3 9431   64.00   M    6      2       3 9432   76.00   F    1      2       3 9433   82.00   M    6      2       3 9434   86.00   M    6      2       3 9435   59.00   M   10      2       2 9436   50.00   M    4      2       2 9437   40.00   M    7      2       2 9438   47.00   M    0      2       2 9439   89.00   M    5      2       3 9440   58.00   M    4      2       2 9441   52.00   M    7      2       2 9442   67.00   M    4      2       3 9443   48.00   M    5      2       2 9444   60.00   F    0      2       3 9445   58.00   M    7      2       2 9446   48.00   F    4      2       2 9447   75.00   M    1      2       3 9448   73.00   M    3      2       3 9449   57.00   F    4      2       2 9450   83.00   M    3      2       3 9451   42.00   M    2      2       2 9452   70.00   M    4      2       3 9453   80.00   M    3      2       3 9454   62.00   M    1      2       3 9455   85.00   M    4      2       3 9456   58.00   M    3      2       2 9457   70.00   F    3      2       3 9458   72.00   F    4      2       3 9459   51.00   M    3      2       2 9460   68.00   M    3      2       3 9461   30.00   F    1      2       2 9462   70.00   M    1      2       3 9463   60.00   M    3      2       3 9464   70.00   M    5      2       3 9465   72.00   M    1      2       3 9466   68.00   F    9      2       3 9467   51.00   F    0      2       2 9468   80.00   M    2      2       3 9469   60.00   M    0      2       3 9470   38.00   M    0      2       2 9471   84.00   F    4      2       3 9472   76.00   M    4      2       3 9473   50.00   F    3      2       2 9474   50.00   M    0      2       2 9475   30.00   M    1      2       2 9476   58.00   M    2      2       2 9477   65.00   F    1      2       3 9478   65.00   F    4      2       3 9479   40.00   M    1      2       2 9480   55.00   M    1      2       2 9481   72.00   M    8      2       3 9482   52.00   F    0      2       2 9483   42.00   M    0      2       2 9484   63.00   F    0      2       3 9485   58.00   M   23      2       2 9486   45.00   M    0      2       2 9487   40.00   F    4      2       2 9488   39.00   M    1      2       2 9489   57.00   M   12      2       2 9490   70.00   F    4      2       3 9491   68.00   M    3      2       3 9492   72.00   F    4      2       3 9493   71.00   M   14      2       3 9494   75.00   M    0      2       3 9495   64.00   M   15      2       3 9496   80.00   M    2      2       3 9497   65.00   M    0      2       3 9498   59.00   M    0      2       2 9499   47.00   M    0      2       2 9500   75.00   M    0      2       3 9501   60.00   M    0      2       3 9502   43.00   M    0      2       2 9503   71.00   M    1      2       3 9504   62.00   M    6      2       3 9505   70.00   M    1      2       3 9506   60.00   M   20      2       3 9507   65.00   M    2      2       3 9508   87.00   F    6      2       3 9509   49.00   M    4      2       2 9510   65.00   F   29      2       3 9511   34.00   M   12      2       2 9512   84.00   M    0      2       3 9513   59.00   M    9      2       2 9514   76.00   M   12      2       3 9515   65.00   M    9      2       3 9516   44.00   M   12      2       2 9517   52.00   M    6      2       2 9518   68.00   F   12      2       3 9519   72.00   M    0      2       3 9520   74.00   M   12      2       3 9521   68.00   M   12      2       3 9522   64.00   M    7      2       3 9523   60.00   M    6      2       3 9524   73.00   M   11      2       3 9525   71.00   M    9      2       3 9526   65.00   M   11      2       3 9527   73.00   M   10      2       3 9528   61.00   M    5      2       3 9529   76.00   M   12      2       3 9530   75.00   M    7      2       3 9531   52.00   M    8      2       2 9532   58.00   M    9      2       2 9533   75.00   M    3      2       3 9534   54.00   F    8      2       2 9535   68.00   F    8      2       3 9536   82.00   M    8      2       3 9537   46.00   M    3      2       2 9538   75.00   F    0      2       3 9539   50.00   M   11      2       2 9540   60.00   M   11      2       3 9541   62.00   M    0      2       3 9542   63.00   F    9      2       3 9543   71.00   F    8      2       3 9544   42.00   M    4      2       2 9545   84.00   F    3      2       3 9546   70.00   F    7      2       3 9547   73.00   M    0      2       3 9548   68.00   F    6      2       3 9549   52.00   M    1      2       2 9550   45.00   M    6      2       2 9551   59.00   M    7      2       2 9552   90.00   M    4      2       3 9553   69.00   F    4      2       3 9554   54.00   M    3      2       2 9555   77.00   M    9      2       3 9556   61.00   M    8      2       3 9557   70.00   F    5      2       3 9558   46.00   M    6      2       2 9559   82.00   M    6      2       3 9560   65.00   F    2      2       3 9561   55.00   M    4      2       2 9562   80.00   M    2      2       3 9563   40.00   M    3      2       2 9564   66.00   M    0      2       3 9565   54.00   M    0      2       2 9566   78.00   M   34      2       3 9567   66.00   M    0      2       3 9568   62.00   M    2      2       3 9569   40.00   F    3      2       2 9570   62.00   M    3      2       3 9571   54.00   M    1      2       2 9572   55.00   M    0      2       2 9573   60.00   F    3      2       3 9574   45.00   F    0      2       2 9575   60.00   F    1      2       3 9576   45.00   M    1      2       2 9577   65.00   F    4      2       3 9578   60.00   F    1      2       3 9579   47.00   M    1      2       2 9580   70.00   M    0      2       3 9581   55.00   F    0      2       2 9582   74.00   M    0      2       3 9583   50.00   M   17      2       2 9584   60.00   F    0      2       3 9585   70.00   M   13      2       3 9586   86.00   M   23      2       3 9587   60.00   M    1      2       3 9588   87.00   M    8      2       3 9589   70.00   M    9      2       3 9590   56.00   M    1      2       2 9591   65.00   M    8      2       3 9592   80.00   M    6      2       3 9593   75.00   F    5      2       3 9594   80.00   M    0      2       3 9595   60.00   M    7      2       3 9596   58.00   F    0      2       2 9597   40.00   M    0      2       2 9598   90.00   M    1      2       3 9599   72.00   M   10      2       3 9600   71.00   M   15      2       3 9601   55.00   M   11      2       2 9602   57.00   M    3      2       2 9603   70.00   M    3      2       3 9604   61.00   M    3      2       3 9605   60.00   M   12      2       3 9606   67.00   M    7      2       3 9607   58.00   F   11      2       2 9608   57.00   M    2      2       2 9609   67.00   M    0      2       3 9610   75.00   M   12      2       3 9611   35.00   F   10      2       2 9612   74.00   F    3      2       3 9613   65.00   M    8      2       3 9614   63.00   M   11      2       3 9615   80.00   F    1      2       3 9616   78.00   M    3      2       3 9617   57.00   M    6      2       2 9618   41.00   M   11      2       2 9619   48.00   M   11      2       2 9620   94.00   M    9      2       3 9621   29.00   F    6      2       2 9622   55.00   M   12      2       2 9623   86.00   M   10      2       3 9624   70.00   F   12      2       3 9625   24.00   M   10      2       2 9626   72.00   M   10      2       3 9627   64.00   M   12      2       3 9628   50.00   F    7      2       2 9629   82.00   F   10      2       3 9630   74.00   M    7      2       3 9631   55.00   M    0      2       2 9632   53.00   M    8      2       2 9633   60.00   F    2      2       3 9634   62.00   M    8      2       3 9635   68.00   M    9      2       3 9636   80.00   M    8      2       3 9637   75.00   M   10      2       3 9638   55.00   M    4      2       2 9639   53.00   M   11      2       2 9640   50.00   M    1      2       2 9641   52.00   M    6      2       2 9642   49.00   M    8      2       2 9643   70.00   M    2      2       3 9644   80.00   F    7      2       3 9645   68.00   M    8      2       3 9646   57.00   M    3      2       2 9647   60.00   M    9      2       3 9648   72.00   M    2      2       3 9649   76.00   M    9      2       3 9650   63.00   M   11      2       3 9651   60.00   M    5      2       3 9652   62.00   M    7      2       3 9653   47.00   M    4      2       2 9654   57.00   F    7      2       2 9655   86.00   M    0      2       3 9656   35.00   F    4      2       2 9657   67.00   F    4      2       3 9658   44.00   M    2      2       2 9659   44.00   M    4      2       2 9660   45.00   F    5      2       2 9661   73.00   M    3      2       3 9662   60.00   F    4      2       3 9663   68.00   M    3      2       3 9664   71.00   M    7      2       3 9665   80.00   F    4      2       3 9666   85.00   M    2      2       3 9667   56.00   F    2      2       2 9668   66.00   M    3      2       3 9669   69.00   M    4      2       3 9670   40.00   F    1      2       2 9671   76.00   F    3      2       3 9672   77.00   M    2      2       3 9673   30.00   M    1      2       2 9674   57.00   M    3      2       2 9675   65.00   M    3      2       3 9676   54.00   M    1      2       2 9677   70.00   M    1      2       3 9678   71.00   M    0      2       3 9679   73.00   M    3      2       3 9680   61.00   M    0      2       3 9681   65.00   F    1      2       3 9682   80.00   M    1      2       3 9683   67.00   M    0      2       3 9684   62.00   M    2      2       3 9685   34.00   M    1      2       2 9686   58.00   M    0      2       2 9687   60.00   F    1      2       3 9688   66.00   M    1      2       3 9689   71.00   M    1      2       3 9690   62.00   M    0      2       3 9691   64.00   M    1      2       3 9692   70.00   M    1      2       3 9693   58.00   M    1      2       2 9694   50.00   F    0      2       2 9695   63.00   M    0      2       3 9696   62.00   M    0      2       3 9697   74.00   M    0      2       3 9698   27.00   M    1      2       2 9699   43.00   M   20      2       2 9700   43.00   F   22      2       2 9701   65.00   F   19      2       3 9702   53.00   M   19      2       2 9703   50.00   F    9      2       2 9704   64.00   M    4      2       3 9705   50.00   M    7      2       2 9706   64.00   M    6      2       3 9707   85.00   M    0      2       3 9708   58.00   M    2      2       2 9709   67.00   M    3      2       3 9710   71.00   F    1      2       3 9711   74.00   M    5      2       3 9712   75.00   M    2      2       3 9713   24.00   F    3      2       2 9714   55.00   F   16      2       2 9715   76.00   F   11      2       3 9716   58.00   F    0      2       2 9717   74.00   M    3      2       3 9718   60.00   M    1      2       3 9719   67.00   M   12      2       3 9720   65.00   M   13      2       3 9721   80.00   M    6      2       3 9722   70.00   M    8      2       3 9723   65.00   M    2      2       3 9724   47.00   M   11      2       2 9725   45.00   F    1      2       2 9726   65.00   M   11      2       3 9727   50.00   F    2      2       2 9728   48.00   F    0      2       2 9729   70.00   F   10      2       3 9730   82.00   M    3      2       3 9731   75.00   F   12      2       3 9732   64.00   M   11      2       3 9733   55.00   M   12      2       2 9734   71.00   M    9      2       3 9735   55.00   M    4      2       2 9736   65.00   M   25      2       3 9737   57.00   M   11      2       2 9738   80.00   M   10      2       3 9739   40.00   M    1      2       2 9740   56.00   M    4      2       2 9741   56.00   M    1      2       2 9742   77.00   M   16      2       3 9743   70.00   F    2      2       3 9744   35.00   M    8      2       2 9745   64.00   M    8      2       3 9746   90.00   M    0      2       3 9747   52.00   M   10      2       2 9748   93.00   M    4      2       3 9749   55.00   M    5      2       2 9750   51.00   M    1      2       2 9751   57.00   M    7      2       2 9752   52.00   M    9      2       2 9753   75.00   F    6      2       3 9754   64.00   M    1      2       3 9755   66.00   M    2      2       3 9756   84.00   F    8      2       3 9757   71.00   F    7      2       3 9758   70.00   F    5      2       3 9759   55.00   F    4      2       2 9760   48.00   F    3      2       2 9761   46.00   F    5      2       2 9762   50.00   F    6      2       2 9763   56.00   M    6      2       2 9764   60.00   M    5      2       3 9765   86.00   F    4      2       3 9766   83.00   M    6      2       3 9767   82.00   F    4      2       3 9768   70.00   M    0      2       3 9769   85.00   M    3      2       3 9770   84.00   M    2      2       3 9771   75.00   M    4      2       3 9772   58.00   M    2      2       2 9773   51.00   M    2      2       2 9774   58.00   F    1      2       2 9775   60.00   M    2      2       3 9776   62.00   F    2      2       3 9777   73.00   M    3      2       3 9778   84.00   M    3      2       3 9779   65.00   F    3      2       3 9780   60.00   M    2      2       3 9781   84.00   M    3      2       3 9782   42.00   M    2      2       2 9783   70.00   M    6      2       3 9784   61.00   M    1      2       3 9785   65.00   F    1      2       3 9786   78.00   F    4      2       3 9787   36.00   M    9      2       2 9788   64.00   M    1      2       3 9789   48.00   M    1      2       2 9790   34.00   M    1      2       2 9791   55.00   F    1      2       2 9792   65.00   F    4      2       3 9793   46.00   M    0      2       2 9794   86.00   F    1      2       3 9795   75.00   F    2      2       3 9796   80.00   M    1      2       3 9797   25.00   F   30      2       2 9798   85.00   M    3      2       3 9799   60.00   M   20      2       3 9800   76.00   F    6      2       3 9801   70.00   M    4      2       3 9802   59.00   M   18      2       2 9803   72.00   M   15      2       3 9804   60.00   F    4      2       3 9805   85.00   M   13      2       3 9806   55.00   M   10      2       2 9807   72.00   M   12      2       3 9808   53.00   M   15      2       2 9809   70.00   M   13      2       3 9810   53.00   M    3      2       2 9811   60.00   M    7      2       3 9812   53.00   F   11      2       2 9813   76.00   M   13      2       3 9814   97.00   F    4      2       3 9815   60.00   F    0      2       3 9816   68.00   F   10      2       3 9817   70.00   F    5      2       3 9818   59.00   M    1      2       2 9819   60.00   M    0      2       3 9820   73.00   M    6      2       3 9821   71.00   F    9      2       3 9822   75.00   M    7      2       3 9823   83.00   M    9      2       3 9824   75.00   M    9      2       3 9825   52.00   F    4      2       2 9826   63.00   F   23      2       3 9827   63.00   M   26      2       3 9828   95.00   M    9      2       3 9829   70.00   F    8      2       3 9830   73.00   M    8      2       3 9831   52.00   M    8      2       2 9832   50.00   M    1      2       2 9833   55.00   F    0      2       2 9834   84.00   M    6      2       3 9835   80.00   F    9      2       3 9836   92.00   M    4      2       3 9837   74.00   M    7      2       3 9838   36.00   M   26      2       2 9839   60.00   M    6      2       3 9840   50.00   M    3      2       2 9841   74.00   F    4      2       3 9842   65.00   F    1      2       3 9843   78.00   M    2      2       3 9844   60.00   M    6      2       3 9845   65.00   M    3      2       3 9846   60.00   M    6      2       3 9847   68.00   M    5      2       3 9848   70.00   M    4      2       3 9849   40.00   M    4      2       2 9850   70.00   F    0      2       3 9851   75.00   M    1      2       3 9852   65.00   F    9      2       3 9853   55.00   M    7      2       2 9854   62.00   F    3      2       3 9855   63.00   M    3      2       3 9856   39.00   F    4      2       2 9857   55.00   F    3      2       2 9858   70.00   M    1      2       3 9859   64.00   M    1      2       3 9860   72.00   F    0      2       3 9861   60.00   F    1      2       3 9862   65.00   F    1      2       3 9863   56.00   M    3      2       2 9864   40.00   F    2      2       2 9865   82.00   M   10      2       3 9866   84.00   M    3      2       3 9867   53.00   F    2      2       2 9868   59.00   M    1      2       2 9869   76.00   M    3      2       3 9870   40.00   M    0      2       2 9871   67.00   M   32      2       3 9872   56.00   M    1      2       2 9873   47.00   M    0      2       2 9874   61.00   M    5      2       3 9875   65.00   M    5      2       3 9876   70.00   M    1      2       3 9877   53.00   M   11      2       2 9878   46.00   M    0      2       2 9879   55.00   M    4      2       2 9880   56.00   M    4      2       2 9881   79.00   M   15      2       3 9882   51.00   F    5      2       2 9883   56.00   F    6      2       2 9884   66.00   M   12      2       3 9885   51.00   M    4      2       2 9886   67.00   F   10      2       3 9887   65.00   M    9      2       3 9888   85.00   M    3      2       3 9889   72.00   M    8      2       3 9890   50.00   M   11      2       2 9891   65.00   M    1      2       3 9892   82.00   M   12      2       3 9893   57.00   M   10      2       2 9894   53.00   F    1      2       2 9895   80.00   F   12      2       3 9896   71.00   M    8      2       3 9897   53.00   M    8      2       2 9898   70.00   F    6      2       3 9899   46.00   F    7      2       2 9900   74.00   M    7      2       3 9901   55.00   F    8      2       2 9902   64.00   M    9      2       3 9903   47.00   M   10      2       2 9904   65.00   M   10      2       3 9905   76.00   F    3      2       3 9906   87.00   M   39      2       3 9907   70.00   F    5      2       3 9908   61.00   F    2      2       3 9909   80.00   M    3      2       3 9910   65.00   M    2      2       3 9911   48.00   M    2      2       2 9912   84.00   M    6      2       3 9913   82.00   M    1      2       3 9914   69.00   F    6      2       3 9915   61.00   F   12      2       3 9916   60.00   M    2      2       3 9917   64.00   F    1      2       3 9918   80.00   F    3      2       3 9919   86.00   F    4      2       3 9920   75.00   F    4      2       3 9921   65.00   F    6      2       3 9922   70.00   M    3      2       3 9923   75.00   F    6      2       3 9924   72.00   M    3      2       3 9925   78.00   F   16      2       3 9926   58.00   M    0      2       2 9927   73.00   M    5      2       3 9928   70.00   M    2      2       3 9929   65.00   F   15      2       3 9930   71.00   F    3      2       3 9931   76.00   M    2      2       3 9932   80.00   M    0      2       3 9933   80.00   M    0      2       3 9934   32.00   M   10      2       2 9935   58.00   M    5      2       2 9936   55.00   F    1      2       2 9937   50.00   M    0      2       2 9938   60.00   M    2      2       3 9939   70.00   M    1      2       3 9940   65.00   M    0      2       3 9941   50.00   F    2      2       2 9942   54.00   M    0      2       2 9943   57.00   M    0      2       2 9944   48.00   F   31      2       2 9945   58.00   F    5      2       2 9946   44.00   M   28      2       2 9947   71.00   M   10      2       3 9948   75.00   M   25      2       3 9949   62.00   F    2      2       3 9950   59.00   M   37      2       2 9951   60.00   M    1      2       3 9952   60.00   M    4      2       3 9953   65.00   M    7      2       3 9954   69.00   M   17      2       3 9955   70.00   M    3      2       3 9956   60.00   F    6      2       3 9957   74.00   M    0      2       3 9958   52.00   M   13      2       2 9959   54.00   F    0      2       2 9960   65.00   M    8      2       3 9961   71.00   M   10      2       3 9962   72.00   F    7      2       3 9963   65.00   F    8      2       3 9964   51.00   M    9      2       2 9965   71.00   M    4      2       3 9966   45.00   F   15      2       2 9967   75.00   M    2      2       3 9968   67.00   M   10      2       3 9969   77.00   M    8      2       3 9970   74.00   F    8      2       3 9971   51.00   M   10      2       2 9972   73.00   M    3      2       3 9973   45.00   M    7      2       2 9974   61.00   M    5      2       3 9975   49.00   M    0      2       2 9976   58.00   M    2      2       2 9977   60.00   M    7      2       3 9978   47.00   F    7      2       2 9979   45.00   F    4      2       2 9980   53.00   M    7      2       2 9981   83.00   M    7      2       3 9982   59.00   M    6      2       2 9983   36.00   F    6      2       2 9984   31.00   F    4      2       2 9985   65.00   M    7      2       3 9986   81.00   M    3      2       3 9987   46.00   F    5      2       2 9988   80.00   F    7      2       3 9989   70.00   M    1      2       3 9990   66.00   M    8      2       3 9991   53.00   F    0      2       2 9992   65.00   M    0      2       3 9993   83.00   M   11      2       3 9994   60.00   F    2      2       3 9995   76.00   F    5      2       3 9996   65.00   F    2      2       3 9997   70.00   F    5      2       3 9998   68.00   M    3      2       3 9999   75.00   M    5      2       3 10000  67.00   M    7      2       3 10001  67.00   M    6      2       3 10002  70.00   M    1      2       3 10003  59.00   M    5      2       2 10004  81.00   M    2      2       3 10005  53.00   M    4      2       2 10006  65.00   F    2      2       3 10007  55.00   F    0      2       2 10008  52.00   M    4      2       2 10009  40.00   M    1      2       2 10010  82.00   M    3      2       3 10011  67.00   M    3      2       3 10012  58.00   M    2      2       2 10013  52.00   F    2      2       2 10014  51.00   M    2      2       2 10015  62.00   M    4      2       3 10016  70.00   M    2      2       3 10017  54.00   M    2      2       2 10018  59.00   F   53      2       2 10019  70.00   M    2      2       3 10020  56.00   M    2      2       2 10021  71.00   M    2      2       3 10022  74.00   F    2      2       3 10023  70.00   M    2      2       3 10024  60.00   F    1      2       3 10025  42.00   F    1      2       2 10026  65.00   M    1      2       3 10027  64.00   M    1      2       3 10028  60.00   M    0      2       3 10029  65.00   F    6      2       3 10030  24.00   M    4      2       2 10031  63.00   M   11      2       3 10032  54.00   M   10      2       2 10033  71.00   M    6      2       3 10034  70.00   M    9      2       3 10035  61.00   M   23      2       3 10036  65.00   M   16      2       3 10037  82.00   M   16      2       3 10038  65.00   M    7      2       3 10039  52.00   F   18      2       2 10040  66.00   M   17      2       3 10041  60.00   F    1      2       3 10042  59.00   M    7      2       2 10043  53.00   M   11      2       2 10044  69.00   M    8      2       3 10045  58.00   M   10      2       2 10046  81.00   M    9      2       3 10047  62.00   M   11      2       3 10048  85.00   F   11      2       3 10049  65.00   M    8      2       3 10050  77.00   M    9      2       3 10051  72.00   F    9      2       3 10052  85.00   M    8      2       3 10053  76.00   M    6      2       3 10054  67.00   M    8      2       3 10055  58.00   M    0      2       2 10056  53.00   M    3      2       2 10057  84.00   M    8      2       3 10058  60.00   F    7      2       3 10059  70.00   M    7      2       3 10060  75.00   M    9      2       3 10061  53.00   M    3      2       2 10062  60.00   M    8      2       3 10063  54.00   M    7      2       2 10064  77.00   M    2      2       3 10065  74.00   F    8      2       3 10066  59.00   M    7      2       2 10067  80.00   M    7      2       3 10068  54.00   M    9      2       2 10069  50.00   M    5      2       2 10070  50.00   M    7      2       2 10071  74.00   M    8      2       3 10072  75.00   F    5      2       3 10073  65.00   M    3      2       3 10074  80.00   M    3      2       3 10075  65.00   M    6      2       3 10076  77.00   M    5      2       3 10077  70.00   M    3      2       3 10078  74.00   M    5      2       3 10079  62.00   M    6      2       3 10080  65.00   M    1      2       3 10081  67.00   M    3      2       3 10082  38.00   F    4      2       2 10083  72.00   M    5      2       3 10084  61.00   F    6      2       3 10085  60.00   M    4      2       3 10086  75.00   M    4      2       3 10087  58.00   M    1      2       2 10088  45.00   M    1      2       2 10089  42.00   M    1      2       2 10090  63.00   M    2      2       3 10091  68.00   M    0      2       3 10092  32.00   F    3      2       2 10093  61.00   F    4      2       3 10094  67.00   F    1      2       3 10095  68.00   M    1      2       3 10096  55.00   F    2      2       2 10097  20.00   F    2      2       2 10098  72.00   F    0      2       3 10099  68.00   M    1      2       3 10100  43.00   M    0      2       2 10101  67.00   M    2      2       3 10102  32.00   M    1      2       2 10103  70.00   F    2      2       3 10104  65.00   F    2      2       3 10105  35.00   F    1      2       2 10106  76.00   M    1      2       3 10107  64.00   M   20      2       3 10108  55.00   F   17      2       2 10109  80.00   M   15      2       3 10110  65.00   M   11      2       3 10111  60.00   F    1      2       3 10112  54.00   M   17      2       2 10113  58.00   M    1      2       2 10114  51.00   M   13      2       2 10115  76.00   M   13      2       3 10116  58.00   M   12      2       2 10117  62.00   F   18      2       3 10118  65.00   M    6      2       3 10119  80.00   F    6      2       3 10120  68.00   M   11      2       3 10121  82.00   M    8      2       3 10122  63.00   M    8      2       3 10123  67.00   M    7      2       3 10124  44.00   M    9      2       2 10125  46.00   M    8      2       2 10126  79.00   M    9      2       3 10127  52.00   F    6      2       2 10128  60.00   F    3      2       3 10129  60.00   M    7      2       3 10130  39.00   M    8      2       2 10131  46.00   F    4      2       2 10132  65.00   M    2      2       3 10133  55.00   F    7      2       2 10134  77.00   M   10      2       3 10135  73.00   M    8      2       3 10136  48.00   M    6      2       2 10137  65.00   M   36      2       3 10138  79.00   F    7      2       3 10139  76.00   M    6      2       3 10140  81.00   M    6      2       3 10141  61.00   F    8      2       3 10142  68.00   M    3      2       3 10143  75.00   M    7      2       3 10144  80.00   M    6      2       3 10145  65.00   F    6      2       3 10146  30.00   M    8      2       2 10147  32.00   F    8      2       2 10148  82.00   M    9      2       3 10149  92.00   F    8      2       3 10150  60.00   M    3      2       3 10151  58.00   F    5      2       2 10152  59.00   M    5      2       2 10153  56.00   M    6      2       2 10154  61.00   M    6      2       3 10155  33.00   M   24      2       2 10156  57.00   F    1      2       2 10157  62.00   M    7      2       3 10158  70.00   F    0      2       3 10159  90.00   M   12      2       3 10160  32.00   F    3      2       2 10161  82.00   M   16      2       3 10162  85.00   M    5      2       3 10163  39.00   M    2      2       2 10164  77.00   F    4      2       3 10165  58.00   M    4      2       2 10166  70.00   M    2      2       3 10167  75.00   F    1      2       3 10168  88.00   F    3      2       3 10169  76.00   F    3      2       3 10170  80.00   M    6      2       3 10171  90.00   M   16      2       3 10172  66.00   M    6      2       3 10173  72.00   M    1      2       3 10174  48.00   F    0      2       2 10175  68.00   F   23      2       3 10176  18.00   F    2      2       2 10177  52.00   M    6      2       2 10178  62.00   F    3      2       3 10179  48.00   M    1      2       2 10180  49.00   M    1      2       2 10181  62.00   F    0      2       3 10182  45.00   F    1      2       2 10183  60.00   F    0      2       3 10184  52.00   F    3      2       2 10185  56.00   F    2      2       2 10186  45.00   F    2      2       2 10187  53.00   M    0      2       2 10188  70.00   M    1      2       3 10189  78.00   F    0      2       3 10190  75.00   M    0      2       3 10191  68.00   M    8      2       3 10192  41.00   F   22      2       2 10193  86.00   M   15      2       3 10194  76.00   M   23      2       3 10195  60.00   M   11      2       3 10196  72.00   M    9      2       3 10197  61.00   M   14      2       3 10198  68.00   M    0      2       3 10199  84.00   M   12      2       3 10200  45.00   M    2      2       2 10201  52.00   M    4      2       2 10202  87.00   F   15      2       3 10203  70.00   F   12      2       3 10204  64.00   M    2      2       3 10205  80.00   M   11      2       3 10206  53.00   F   13      2       2 10207  62.00   M   11      2       3 10208  63.00   M    5      2       3 10209  58.00   M   16      2       2 10210  52.00   M   12      2       2 10211  75.00   M    0      2       3 10212  92.00   M    5      2       3 10213  66.00   M    9      2       3 10214  58.00   M    5      2       2 10215  80.00   M   11      2       3 10216  70.00   M   10      2       3 10217  65.00   M   11      2       3 10218  60.00   M   22      2       3 10219  75.00   M   10      2       3 10220  74.00   M    6      2       3 10221  81.00   M    7      2       3 10222  72.00   F    6      2       3 10223  78.00   M    7      2       3 10224  40.00   F    8      2       2 10225  71.00   M    5      2       3 10226  70.00   M    7      2       3 10227  43.00   M    8      2       2 10228  47.00   M    6      2       2 10229  78.00   M    1      2       3 10230  57.00   M    7      2       2 10231  82.00   F    7      2       3 10232  76.00   M    1      2       3 10233  71.00   M    7      2       3 10234  60.00   M    4      2       3 10235  60.00   M    2      2       3 10236  55.00   F    2      2       2 10237  75.00   F    3      2       3 10238  70.00   M    2      2       3 10239  65.00   F    2      2       3 10240  57.00   M    4      2       2 10241  89.00   M    1      2       3 10242  75.00   M    0      2       3 10243  48.00   M   33      2       2 10244  65.00   M   26      2       3 10245  62.00   F    5      2       3 10246  79.00   F    4      2       3 10247  45.00   F    8      2       2 10248  37.00   F    1      2       2 10249  75.00   F    1      2       3 10250  45.00   F    0      2       2 10251  46.00   M    2      2       2 10252  68.00   M    5      2       3 10253  75.00   M    4      2       3 10254  75.00   M    2      2       3 10255  83.00   M    2      2       3 10256  60.00   F    4      2       3 10257  60.00   M    4      2       3 10258  63.00   F    2      2       3 10259  64.00   M    2      2       3 10260  72.00   M    2      2       3 10261  72.00   M    3      2       3 10262  78.00   M    0      2       3 10263  78.00   M    2      2       3 10264  68.00   M    2      2       3 10265  77.00   M    8      2       3 10266  70.00   M    2      2       3 10267  56.00   M   15      2       2 10268  63.00   M    3      2       3 10269  64.00   M    4      2       3 10270  64.00   F   13      2       3 10271  60.00   M   16      2       3 10272  62.00   M   15      2       3 10273  72.00   M   11      2       3 10274  61.00   M    1      2       3 10275  60.00   M   13      2       3 10276  38.00   M   11      2       2 10277  70.00   M    9      2       3 10278  84.00   M    9      2       3 10279  74.00   F   11      2       3 10280  39.00   M   12      2       2 10281  70.00   F    8      2       3 10282  81.00   M   11      2       3 10283  61.00   F   10      2       3 10284  70.00   M    6      2       3 10285  64.00   M   11      2       3 10286  40.00   F    2      2       2 10287  62.00   F   12      2       3 10288  75.00   M   10      2       3 10289  61.00   M    2      2       3 10290  62.00   M    5      2       3 10291  55.00   F    7      2       2 10292  65.00   M    3      2       3 10293  26.00   M    9      2       2 10294  72.00   M    4      2       3 10295  56.00   M    7      2       2 10296  76.00   M    6      2       3 10297  57.00   F    8      2       2 10298  78.00   F    9      2       3 10299  64.00   M    7      2       3 10300  52.00   M    5      2       2 10301  72.00   M    4      2       3 10302  70.00   F    7      2       3 10303  70.00   M    8      2       3 10304  74.00   M    8      2       3 10305  58.00   M    5      2       2 10306  55.00   M    6      2       2 10307  94.00   M    6      2       3 10308  86.00   M    7      2       3 10309  75.00   M    5      2       3 10310  62.00   F    6      2       3 10311  75.00   M    6      2       3 10312  65.00   M    1      2       3 10313  46.00   M    0      2       2 10314  49.00   M    8      2       2 10315  72.00   M    1      2       3 10316  45.00   F    1      2       2 10317  67.00   F    1      2       3 10318  54.00   M    4      2       2 10319  68.00   M    5      2       3 10320  52.00   F    3      2       2 10321  55.00   F    5      2       2 10322  71.00   M   32      2       3 10323  52.00   F    3      2       2 10324  48.00   M    2      2       2 10325  25.00   F    1      2       2 10326  80.00   M    2      2       3 10327  87.00   F    2      2       3 10328  74.00   M    5      2       3 10329  60.00   M    1      2       3 10330  65.00   F    3      2       3 10331  80.00   M    1      2       3 10332  68.00   M    1      2       3 10333  65.00   F    0      2       3 10334  38.00   M    1      2       2 10335  55.00   F    2      2       2 10336  24.00   F    0      2       2 10337  57.00   M    2      2       2 10338  70.00   F    4      2       3 10339  51.00   F   18      2       2 10340  50.00   M   17      2       2 10341  39.00   F   16      2       2 10342  54.00   M   15      2       2 10343  65.00   F    6      2       3 10344  69.00   M   14      2       3 10345  84.00   M    9      2       3 10346  70.00   M   11      2       3 10347  49.00   F    2      2       2 10348  69.00   M   10      2       3 10349  60.00   M    9      2       3 10350  75.00   M    8      2       3 10351  72.00   M    8      2       3 10352  59.00   F    7      2       2 10353  52.00   M    6      2       2 10354  73.00   M    7      2       3 10355  74.00   M    4      2       3 10356  60.00   M    1      2       3 10357  75.00   M   13      2       3 10358  37.00   M    6      2       2 10359  65.00   M   13      2       3 10360  67.00   F    5      2       3 10361  71.00   M    3      2       3 10362  70.00   F    4      2       3 10363  57.00   F    2      2       2 10364  70.00   M    4      2       3 10365  75.00   F    3      2       3 10366  44.00   M    4      2       2 10367  53.00   M    2      2       2 10368  43.00   F    4      2       2 10369  75.00   M    5      2       3 10370  60.00   M    4      2       3 10371  73.00   M    2      2       3 10372  79.00   M    2      2       3 10373  72.00   M    1      2       3 10374  68.00   M    3      2       3 10375  60.00   F    1      2       3 10376  61.00   M    2      2       3 10377  36.00   M    1      2       2 10378  86.00   M    1      2       3 10379  78.00   M    0      2       3 10380  64.00   M    2      2       3 10381  66.00   M    1      2       3 10382  52.00   F    1      2       2 10383  48.00   M   11      2       2 10384  50.00   M    7      2       2 10385  60.00   M    1      2       3 10386  56.00   F    1      2       2 10387  65.00   M    5      2       3 10388  67.00   M    4      2       3 10389  52.00   M    4      2       2 10390  75.00   F   11      2       3 10391  46.00   F   16      2       2 10392  71.00   F   14      2       3 10393  62.00   M   13      2       3 10394  75.00   M   12      2       3 10395  52.00   F   15      2       2 10396  70.00   M   12      2       3 10397  52.00   M    0      2       2 10398  82.00   M    6      2       3 10399  80.00   F   14      2       3 10400  75.00   F    6      2       3 10401  60.00   F   12      2       3 10402  57.00   M    4      2       2 10403  90.00   M    0      2       3 10404  47.00   M    9      2       2 10405  75.00   M    8      2       3 10406  86.00   M    7      2       3 10407  71.00   M    0      2       3 10408  65.00   M    9      2       3 10409  35.00   M    4      2       2 10410  68.00   M   11      2       3 10411  69.00   F    5      2       3 10412  60.00   F    3      2       3 10413  62.00   F    8      2       3 10414  68.00   F   25      2       3 10415  62.00   M    0      2       3 10416  85.00   M    2      2       3 10417  46.00   M   11      2       2 10418  65.00   M    3      2       3 10419  65.00   M    6      2       3 10420  64.00   F    6      2       3 10421  83.00   M    0      2       3 10422  73.00   M    8      2       3 10423  52.00   F    6      2       2 10424  55.00   F    3      2       2 10425  49.00   M    1      2       2 10426  80.00   M    2      2       3 10427  58.00   F    3      2       2 10428  63.00   M    2      2       3 10429  52.00   F    5      2       2 10430  73.00   M    2      2       3 10431  35.00   F    3      2       2 10432  58.00   M    0      2       2 10433  61.00   M    4      2       3 10434  53.00   F    3      2       2 10435  85.00   M    3      2       3 10436  59.00   M    3      2       2 10437  35.00   M    0      2       2 10438  60.00   F    1      2       3 10439  50.00   M    1      2       2 10440  75.00   M    1      2       3 10441  75.00   F    3      2       3 10442  84.00   F    2      2       3 10443  26.00   M    0      2       2 10444  80.00   M    8      2       3 10445  45.00   M    4      2       2 10446  79.00   M   11      2       3 10447  56.00   M   15      2       2 10448  75.00   M    0      2       3 10449  70.00   M    5      2       3 10450  52.00   M    8      2       2 10451  73.00   M    0      2       3 10452  60.00   M    4      2       3 10453  71.00   M   16      2       3 10454  28.00   M   13      2       2 10455  38.00   M   10      2       2 10456  85.00   M    7      2       3 10457  60.00   F   12      2       3 10458  65.00   F    8      2       3 10459  61.00   F    4      2       3 10460  62.00   M   11      2       3 10461  70.00   F    8      2       3 10462  70.00   M    5      2       3 10463  67.00   M    8      2       3 10464  46.00   F    9      2       2 10465  76.00   M    9      2       3 10466  54.00   M   10      2       2 10467  85.00   M    7      2       3 10468  65.00   F    8      2       3 10469  62.00   F   10      2       3 10470  65.00   M    3      2       3 10471  80.00   F   10      2       3 10472  80.00   M    9      2       3 10473  75.00   F    8      2       3 10474  90.00   F    8      2       3 10475  84.00   M    7      2       3 10476  63.00   F    3      2       3 10477  70.00   F    7      2       3 10478  80.00   F    9      2       3 10479  77.00   M    6      2       3 10480  79.00   M    6      2       3 10481  60.00   M    6      2       3 10482  62.00   F    9      2       3 10483  70.00   M    6      2       3 10484  40.00   M    5      2       2 10485  68.00   M    0      2       3 10486  58.00   F    2      2       2 10487  65.00   F    5      2       3 10488  67.00   M    4      2       3 10489  72.00   M    7      2       3 10490  68.00   F    7      2       3 10491  25.00   M    0      2       2 10492  66.00   F    3      2       3 10493  30.00   M    4      2       2 10494  60.00   M    1      2       3 10495  85.00   M    4      2       3 10496  85.00   M    1      2       3 10497  64.00   M    5      2       3 10498  63.00   M    3      2       3 10499  56.00   M    1      2       2 10500  60.00   M    6      2       3 10501  52.00   F    0      2       2 10502  64.00   M    1      2       3 10503  45.00   F   11      2       2 10504  49.00   M    1      2       2 10505  50.00   F    0      2       2 10506  71.00   F    2      2       3 10507  53.00   F    1      2       2 10508  65.00   M    1      2       3 10509  46.00   M    0      2       2 10510  55.00   M    2      2       2 10511  70.00   M    0      2       3 10512  43.00   M    3      2       2 10513  75.00   F    1      2       3 10514  75.00   F    0      2       3 10515  70.00   F    2      2       3 10516  42.00   M    0      2       2 10517  65.00   F    5      2       3 10518  75.00   M   19      2       3 10519  37.00   M   18      2       2 10520  51.00   F    5      2       2 10521  60.00   M    1      2       3 10522  70.00   M   12      2       3 10523  65.00   M   14      2       3 10524  67.00   F    3      2       3 10525  56.00   F   13      2       2 10526  70.00   M   13      2       3 10527  85.00   M   15      2       3 10528  65.00   F    6      2       3 10529  64.00   M    4      2       3 10530  71.00   F   11      2       3 10531  38.00   M    5      2       2 10532  61.00   F   12      2       3 10533  69.00   M   18      2       3 10534  70.00   M   10      2       3 10535  89.00   M    7      2       3 10536  62.00   M    1      2       3 10537  55.00   F    1      2       2 10538  46.00   M    5      2       2 10539  65.00   M   10      2       3 10540  50.00   M   10      2       2 10541  79.00   M    8      2       3 10542  82.00   M    2      2       3 10543  60.00   M    4      2       3 10544  70.00   F    5      2       3 10545  59.00   F    9      2       2 10546  71.00   M    7      2       3 10547  48.00   F    3      2       2 10548  85.00   F    7      2       3 10549  64.00   M    3      2       3 10550  87.00   F    7      2       3 10551  62.00   M    7      2       3 10552  65.00   M    4      2       3 10553  76.00   M    3      2       3 10554  69.00   M    4      2       3 10555  55.00   F    8      2       2 10556  75.00   F    7      2       3 10557  65.00   M    5      2       3 10558  59.00   F    8      2       2 10559  70.00   M    5      2       3 10560  38.00   M    7      2       2 10561  66.00   M    7      2       3 10562  66.00   M    6      2       3 10563  41.00   M    7      2       2 10564  65.00   M    8      2       3 10565  63.00   M    6      2       3 10566  40.00   F    2      2       2 10567  50.00   F    1      2       2 10568  72.00   M    5      2       3 10569  64.00   M    8      2       3 10570  22.00   M    2      2       2 10571  65.00   M    2      2       3 10572  70.00   M    7      2       3 10573  58.00   F    0      2       2 10574  35.00   M    8      2       2 10575  42.00   F    4      2       2 10576  75.00   M    8      2       3 10577  60.00   F    5      2       3 10578  65.00   M    4      2       3 10579  70.00   F    3      2       3 10580  66.00   M    4      2       3 10581  60.00   F    4      2       3 10582  56.00   M    1      2       2 10583  55.00   F    2      2       2 10584  49.00   F    4      2       2 10585  61.00   M    4      2       3 10586  72.00   M    6      2       3 10587  51.00   M    3      2       2 10588  61.00   M    3      2       3 10589  62.00   F    5      2       3 10590  60.00   M    0      2       3 10591  74.00   F    1      2       3 10592  59.00   M    1      2       2 10593  75.00   F    5      2       3 10594  77.00   M    2      2       3 10595  67.00   F    2      2       3 10596  70.00   M    1      2       3 10597  70.00   M    2      2       3 10598  49.00   F    2      2       2 10599  55.00   M    4      2       2 10600  56.00   F    2      2       2 10601  60.00   F    4      2       3 10602  60.00   F    2      2       3 10603  72.00   M    8      2       3 10604  54.00   F    8      2       2 10605  59.00   M   33      2       2 10606  64.00   F   36      2       3 10607  53.00   F    3      2       2 10608  63.00   M    2      2       3 10609  72.00   F    6      2       3 10610  78.00   M    2      2       3 10611  73.00   M   14      2       3 10612  85.00   M    1      2       3 10613  42.00   M    7      2       2 10614  77.00   M   13      2       3 10615  89.00   M   14      2       3 10616  72.00   M    7      2       3 10617  37.00   M    7      2       2 10618  78.00   M   12      2       3 10619  69.00   M   14      2       3 10620  65.00   F    0      2       3 10621  51.00   M    3      2       2 10622  74.00   M    3      2       3 10623  32.00   F    0      2       2 10624  51.00   M   11      2       2 10625  74.00   M    9      2       3 10626  52.00   M    4      2       2 10627  68.00   F   11      2       3 10628  66.00   M   10      2       3 10629  71.00   M   11      2       3 10630  65.00   M   10      2       3 10631  43.00   M    7      2       2 10632  80.00   M   10      2       3 10633  65.00   M    4      2       3 10634  76.00   M    8      2       3 10635  56.00   M    3      2       2 10636  60.00   F    7      2       3 10637  51.00   M   14      2       2 10638  64.00   M   11      2       3 10639  75.00   M    1      2       3 10640  57.00   F    4      2       2 10641  80.00   M    2      2       3 10642  81.00   M   12      2       3 10643  75.00   F   11      2       3 10644  88.00   M    8      2       3 10645  75.00   M    8      2       3 10646  65.00   M    7      2       3 10647  86.00   M    7      2       3 10648  76.00   M    5      2       3 10649  67.00   M    6      2       3 10650  20.00   M    2      2       2 10651  70.00   F    5      2       3 10652  50.00   M    3      2       2 10653  70.00   M    6      2       3 10654  80.00   M    4      2       3 10655  72.00   M    3      2       3 10656  55.00   F    3      2       2 10657  60.00   F    2      2       3 10658  66.00   M    5      2       3 10659  50.00   M    4      2       2 10660  55.00   M    3      2       2 10661  75.00   M    4      2       3 10662  68.00   M    0      2       3 10663  69.00   F    1      2       3 10664  59.00   M    2      2       2 10665  75.00   F    4      2       3 10666  82.00   M    2      2       3 10667  68.00   M    2      2       3 10668  52.00   M    0      2       2 10669  58.00   F    3      2       2 10670  62.00   M    2      2       3 10671  55.00   F    0      2       2 10672  70.00   F    4      2       3 10673  53.00   M    3      2       2 10674  50.00   M    2      2       2 10675  54.00   M    3      2       2 10676  45.00   M    0      2       2 10677  87.00   M    0      2       3 10678  81.00   M   18      2       3 10679  85.00   M    4      2       3 10680  54.00   M    1      2       2 10681  55.00   F   15      2       2 10682  64.00   M    4      2       3 10683  58.00   F   18      2       2 10684  65.00   F   13      2       3 10685  60.00   F   14      2       3 10686  60.00   M    5      2       3 10687  60.00   M    0      2       3 10688  76.00   M    1      2       3 10689  63.00   F   11      2       3 10690  70.00   M   11      2       3 10691  55.00   M    4      2       2 10692  81.00   M    1      2       3 10693  73.00   F    2      2       3 10694  49.00   F   12      2       2 10695  80.00   F    7      2       3 10696  65.00   M    4      2       3 10697  71.00   M   11      2       3 10698  60.00   M    1      2       3 10699  55.00   M    8      2       2 10700  65.00   M    9      2       3 10701  56.00   M    7      2       2 10702  76.00   M    8      2       3 10703  68.00   F    7      2       3 10704  85.00   M    6      2       3 10705  62.00   M   17      2       3 10706  70.00   F    6      2       3 10707  66.00   M    6      2       3 10708  60.00   M   10      2       3 10709  50.00   M    4      2       2 10710  95.00   M    5      2       3 10711  65.00   M    5      2       3 10712  60.00   M    5      2       3 10713  51.00   M    6      2       2 10714  63.00   M    7      2       3 10715  34.00   M    4      2       2 10716  64.00   M    5      2       3 10717  52.00   M    0      2       2 10718  65.00   M   12      2       3 10719  85.00   M    2      2       3 10720  67.00   M    1      2       3 10721  57.00   F    1      2       2 10722  63.00   M    1      2       3 10723  82.00   F    5      2       3 10724  50.00   M    4      2       2 10725  63.00   M    3      2       3 10726  52.00   M    1      2       2 10727  58.00   M    0      2       2 10728  81.00   M    2      2       3 10729  55.00   M   16      2       2 10730  68.00   M    9      2       3 10731  77.00   F   16      2       3 10732  75.00   F   18      2       3 10733  89.00   M   14      2       3 10734  68.00   M   15      2       3 10735  65.00   M    8      2       3 10736  66.00   M   15      2       3 10737  28.00   M    4      2       2 10738  74.00   M    5      2       3 10739  59.00   F   13      2       2 10740  77.00   M    4      2       3 10741  79.00   F   13      2       3 10742  75.00   M    9      2       3 10743  82.00   M    8      2       3 10744  41.00   F    1      2       2 10745  56.00   M    7      2       2 10746  63.00   M    3      2       3 10747  70.00   M   10      2       3 10748  83.00   F   11      2       3 10749  68.00   M   13      2       3 10750  88.00   M    7      2       3 10751  60.00   M    0      2       3 10752  70.00   M   10      2       3 10753  51.00   M    2      2       2 10754  47.00   M   10      2       2 10755  60.00   M    1      2       3 10756  65.00   M    7      2       3 10757  57.00   M    7      2       2 10758  84.00   F    7      2       3 10759  60.00   F    6      2       3 10760  54.00   F    4      2       2 10761  73.00   M    5      2       3 10762  80.00   F    6      2       3 10763  68.00   F    2      2       3 10764  60.00   M   16      2       3 10765  54.00   F    2      2       2 10766  58.00   M    1      2       2 10767  83.00   M    4      2       3 10768  75.00   M    3      2       3 10769  62.00   F    3      2       3 10770  59.00   M    0      2       2 10771  78.00   F    0      2       3 10772  63.00   F    1      2       3 10773  55.00   F    3      2       2 10774  74.00   F    0      2       3 10775  84.00   M   10      2       3 10776  84.00   M    4      2       3 10777  81.00   M    1      2       3 10778  38.00   F    0      2       2 10779  60.00   M    6      2       3 10780  57.00   F    7      2       2 10781  72.00   M   21      2       3 10782  42.00   M   15      2       2 10783  72.00   M   17      2       3 10784  72.00   M   13      2       3 10785  58.00   F   13      2       2 10786  70.00   M   11      2       3 10787  68.00   M    0      2       3 10788  47.00   M   11      2       2 10789  81.00   F   41      2       3 10790  75.00   F    9      2       3 10791  87.00   M   10      2       3 10792  90.00   F    8      2       3 10793  78.00   M    9      2       3 10794  51.00   M    5      2       2 10795  70.00   M    8      2       3 10796  63.00   F    7      2       3 10797  70.00   F    8      2       3 10798  57.00   F    9      2       2 10799  70.00   M    7      2       3 10800  62.00   M    6      2       3 10801  82.00   F    1      2       3 10802  80.00   F    5      2       3 10803  68.00   M    5      2       3 10804  75.00   M    7      2       3 10805  80.00   F    0      2       3 10806  62.00   M    4      2       3 10807  67.00   F    5      2       3 10808  64.00   F    0      2       3 10809  56.00   M    3      2       2 10810  59.00   M    1      2       2 10811  63.00   M    0      2       3 10812  53.00   M    7      2       2 10813  59.00   M    4      2       2 10814  56.00   M    5      2       2 10815  65.00   M    5      2       3 10816  65.00   F    9      2       3 10817  32.00   M    0      2       2 10818  65.00   F   17      2       3 10819  54.00   M    7      2       2 10820  80.00   M   14      2       3 10821  64.00   M   12      2       3 10822  54.00   M   11      2       2 10823  82.00   M    9      2       3 10824  70.00   M    0      2       3 10825  66.00   F    3      2       3 10826  84.00   M   10      2       3 10827  71.00   M   15      2       3 10828  92.00   M    8      2       3 10829  60.00   M   11      2       3 10830  58.00   F    8      2       2 10831  80.00   M    8      2       3 10832  74.00   M    8      2       3 10833  83.00   F   10      2       3 10834  70.00   F    7      2       3 10835  86.00   M   10      2       3 10836  38.00   M    7      2       2 10837  70.00   F    8      2       3 10838  80.00   M    7      2       3 10839  70.00   F    5      2       3 10840  47.00   F    9      2       2 10841  75.00   M    4      2       3 10842  59.00   M    7      2       2 10843  68.00   M    3      2       3 10844  82.00   F    5      2       3 10845  73.00   M    2      2       3 10846  87.00   M    3      2       3 10847  70.00   M   14      2       3 10848  48.00   F    3      2       2 10849  78.00   M   11      2       3 10850  52.00   F    0      2       2 10851  47.00   M    4      2       2 10852  70.00   M    2      2       3 10853  60.00   F    4      2       3 10854  78.00   M    0      2       3 10855  70.00   F    2      2       3 10856  64.00   F    2      2       3 10857  57.00   M    0      2       2 10858  56.00   M   13      2       2 10859  55.00   M   14      2       2 10860  67.00   M    4      2       3 10861  58.00   M   14      2       2 10862   3.00   F   12      2       1 10863  63.00   F   14      2       3 10864  48.00   F   12      2       2 10865  44.00   M    6      2       2 10866  77.00   M   11      2       3 10867  71.00   F    7      2       3 10868  55.00   M    9      2       2 10869  65.00   M    4      2       3 10870  68.00   M    9      2       3 10871  80.00   F   11      2       3 10872  78.00   M    9      2       3 10873  84.00   M    8      2       3 10874  75.00   M    6      2       3 10875  55.00   M    7      2       2 10876  70.00   F    2      2       3 10877  80.00   F    9      2       3 10878  70.00   M    8      2       3 10879  80.00   F    6      2       3 10880  56.00   M    5      2       2 10881  58.00   M    3      2       2 10882  60.00   F    1      2       3 10883  65.00   M    4      2       3 10884  88.00   M    6      2       3 10885  70.00   F    5      2       3 10886  72.00   M    3      2       3 10887  66.00   M    5      2       3 10888  90.00   M    6      2       3 10889  54.00   M    2      2       2 10890  76.00   F    2      2       3 10891  63.00   F    3      2       3 10892  57.00   M    2      2       2 10893  58.00   F    7      2       2 10894  84.00   M    1      2       3 10895  35.00   F    2      2       2 10896  31.00   M    1      2       2 10897  19.00   M    9      2       2 10898  58.00   M    0      2       2 10899  72.00   M   12      2       3 10900  76.00   M    9      2       3 10901  57.00   F   12      2       2 10902  57.00   M   22      2       2 10903  66.00   M   18      2       3 10904  62.00   M    6      2       3 10905  72.00   M   21      2       3 10906  60.00   F    8      2       3 10907  55.00   M    2      2       2 10908  80.00   M   10      2       3 10909  65.00   M   12      2       3 10910  65.00   M   16      2       3 10911  60.00   F    4      2       3 10912  72.00   M   10      2       3 10913  75.00   M   12      2       3 10914  75.00   M   11      2       3 10915  80.00   M    8      2       3 10916  65.00   M   12      2       3 10917  63.00   M   10      2       3 10918  72.00   F    9      2       3 10919  69.00   M   11      2       3 10920  93.00   F    9      2       3 10921  40.00   M   11      2       2 10922  75.00   M   17      2       3 10923  57.00   M    3      2       2 10924  79.00   M   11      2       3 10925  58.00   M   11      2       2 10926  54.00   M    6      2       2 10927  75.00   M    2      2       3 10928  55.00   M    5      2       2 10929  68.00   M    7      2       3 10930  54.00   M   26      2       2 10931  65.00   F    0      2       3 10932  74.00   M    4      2       3 10933  52.00   M    5      2       2 10934  63.00   M    6      2       3 10935  80.00   F    0      2       3 10936  65.00   F    6      2       3 10937  76.00   M    6      2       3 10938  57.00   F    3      2       2 10939  64.00   M    2      2       3 10940  66.00   M    1      2       3 10941  60.00   M    2      2       3 10942  80.00   M    3      2       3 10943  76.00   M    5      2       3 10944  61.00   M    5      2       3 10945  82.00   M    3      2       3 10946  34.00   M    0      2       2 10947  89.00   M    2      2       3 10948  80.00   M    2      2       3 10949  80.00   F    2      2       3 10950  74.00   M    3      2       3 10951  53.00   F    2      2       2 10952  66.00   M    0      2       3 10953  78.00   M    3      2       3 10954  55.00   M    1      2       2 10955  52.00   M    3      2       2 10956  33.00   M   14      2       2 10957  65.00   F    0      2       3 10958  46.00   M    2      2       2 10959  46.00   F   12      2       2 10960  68.00   F   19      2       3 10961  29.00   M   12      2       2 10962  63.00   M   10      2       3 10963  69.00   M   11      2       3 10964  42.00   M    4      2       2 10965  70.00   M    1      2       3 10966  86.00   M   11      2       3 10967  77.00   F    4      2       3 10968  67.00   M    8      2       3 10969  68.00   M    7      2       3 10970  73.00   M   10      2       3 10971  41.00   M   11      2       2 10972  83.00   M    8      2       3 10973  68.00   M    6      2       3 10974  79.00   M    7      2       3 10975  65.00   M    3      2       3 10976  55.00   M    2      2       2 10977  60.00   M    5      2       3 10978  45.00   M    2      2       2 10979  25.00   F    0      2       2 10980  89.00   M    6      2       3 10981  57.00   F    6      2       2 10982  51.00   F    6      2       2 10983  85.00   F    2      2       3 10984  84.00   M    5      2       3 10985  78.00   M    7      2       3 10986  58.00   M    3      2       2 10987  78.00   F    3      2       3 10988  65.00   M    2      2       3 10989  68.00   M    9      2       3 10990  78.00   F    4      2       3 10991  75.00   F    0      2       3 10992  78.00   M    5      2       3 10993  67.00   F    2      2       3 10994  63.00   M    3      2       3 10995  83.00   M    2      2       3 10996  11.00   M    3      2       1 10997  68.00   F    0      2       3 10998  77.00   M    2      2       3 10999  24.00   M    8      2       2 11000  75.00   F   19      2       3 11001  56.00   F   25      2       2 11002  56.00   M    8      2       2 11003  24.00   F   16      2       2 11004  65.00   M   15      2       3 11005  70.00   M   15      2       3 11006  31.00   F    1      2       2 11007  76.00   M    7      2       3 11008  55.00   F    9      2       2 11009  65.00   M   14      2       3 11010  61.00   M   13      2       3 11011  65.00   M    0      2       3 11012  62.00   M    4      2       3 11013  74.00   M    1      2       3 11014  78.00   M   10      2       3 11015  58.00   M    3      2       2 11016  67.00   M   11      2       3 11017  62.00   M    9      2       3 11018  70.00   F   10      2       3 11019  65.00   F    2      2       3 11020  75.00   M    5      2       3 11021  55.00   M    9      2       2 11022  92.00   M    8      2       3 11023  74.00   M    1      2       3 11024  91.00   F    6      2       3 11025  29.00   M    4      2       2 11026  81.00   M    7      2       3 11027  82.00   M    7      2       3 11028  46.00   M    2      2       2 11029  76.00   M    6      2       3 11030  74.00   M    5      2       3 11031  60.00   F   36      2       3 11032  30.00   F    2      2       2 11033  48.00   F    1      2       2 11034  64.00   M    2      2       3 11035  75.00   M    1      2       3 11036  73.00   M    1      2       3 11037  71.00   M    1      2       3 11038  86.00   F    1      2       3 11039  59.00   M    2      2       2 11040  79.00   M    2      2       3 11041  70.00   M    2      2       3 11042  87.00   F    0      2       3 11043  56.00   F    1      2       2 11044  25.00   M    0      2       2 11045  66.00   M   11      2       3 11046  73.00   M    1      2       3 11047  58.00   F    1      2       2 11048  60.00   F    6      2       3 11049  75.00   M    7      2       3 11050  60.00   M   11      2       3 11051  75.00   M   11      2       3 11052  70.00   F    8      2       3 11053  93.00   M    7      2       3 11054  66.00   M   16      2       3 11055  48.00   M   10      2       2 11056  68.00   M   11      2       3 11057  37.00   M   13      2       2 11058  74.00   F   12      2       3 11059  65.00   M    9      2       3 11060  61.00   M    9      2       3 11061  62.00   F    8      2       3 11062  34.00   M    7      2       2 11063  70.00   F    4      2       3 11064  72.00   M    4      2       3 11065  82.00   M    6      2       3 11066  70.00   M    7      2       3 11067  60.00   F    6      2       3 11068  69.00   F    0      2       3 11069  79.00   M    8      2       3 11070  34.00   M    1      2       2 11071  90.00   M    1      2       3 11072  52.00   M    0      2       2 11073  25.00   M    0      2       2 11074  44.00   M    0      2       2 11075  73.00   M    0      2       3 11076  60.00   M   43      2       3 11077  56.00   M    4      2       2 11078  59.00   M    2      2       2 11079  60.00   F   13      2       3 11080  85.00   M   11      2       3 11081  65.00   M    7      2       3 11082  78.00   M   10      2       3 11083  92.00   M    8      2       3 11084  68.00   M    8      2       3 11085  63.00   M    5      2       3 11086  58.00   F    8      2       2 11087  65.00   M    9      2       3 11088  80.00   F    7      2       3 11089 100.00   F    3      2       3 11090  48.00   M    2      2       2 11091  45.00   M    5      2       2 11092  62.00   M    2      2       3 11093  69.00   M    1      2       3 11094  60.00   F    4      2       3 11095  80.00   F    3      2       3 11096  29.00   M    2      2       2 11097  78.00   M   14      2       3 11098  64.00   M    1      2       3 11099  52.00   M    0      2       2 11100  68.00   M    1      2       3 11101  74.00   F   16      2       3 11102  64.00   M   12      2       3 11103  92.00   F    6      2       3 11104  65.00   F   12      2       3 11105  56.00   M   21      2       2 11106  68.00   F    8      2       3 11107  73.00   M    9      2       3 11108  58.00   M    7      2       2 11109  85.00   M    9      2       3 11110  33.00   M    9      2       2 11111  63.00   M    8      2       3 11112  46.00   M    4      2       2 11113  74.00   M    6      2       3 11114  62.00   M    6      2       3 11115  55.00   F    6      2       2 11116  50.00   F    0      2       2 11117  65.00   M    5      2       3 11118  63.00   F    3      2       3 11119  84.00   M    1      2       3 11120  67.00   M    2      2       3 11121  48.00   F    1      2       2 11122  65.00   F    1      2       3 11123  76.00   F    2      2       3 11124  65.00   M    1      2       3 11125  65.00   F   21      2       3 11126  41.00   M    0      2       2 11127  30.00   M    3      2       2 11128  76.00   F    2      2       3 11129  65.00   F    2      2       3 11130  76.00   F    7      2       3 11131  30.00   M    0      2       2 11132  92.00   M   14      2       3 11133  65.00   F   11      2       3 11134  55.00   F    5      2       2 11135  75.00   F    9      2       3 11136  60.00   M    2      2       3 11137  93.00   F    9      2       3 11138  72.00   M    3      2       3 11139  84.00   M    0      2       3 11140  82.00   M    5      2       3 11141  64.00   F    2      2       3 11142  65.00   M   21      2       3 11143  78.00   M    4      2       3 11144  60.00   M    3      2       3 11145  58.00   M    4      2       2 11146  35.00   F    2      2       2 11147  67.00   M    4      2       3 11148  79.00   M    1      2       3 11149  74.00   M    8      2       3 11150  56.00   F    0      2       2 11151  55.00   M    0      2       2 11152  80.00   M    2      2       3 11153  67.00   M    3      2       3 11154  68.00   M    1      2       3 11155  67.00   M    7      2       3 11156  82.00   M   13      2       3 11157  77.00   M    4      2       3 11158  60.00   F    0      2       3 11159  70.00   M    1      2       3 11160  84.00   F    0      2       3 11161  57.00   M    2      2       2 11162  29.00   F    1      2       2 11163  84.00   M   11      2       3 11164  82.00   F    9      2       3 11165  57.00   F   11      2       2 11166  48.00   F   12      2       2 11167  61.00   M   11      2       3 11168  59.00   M    9      2       2 11169  62.00   F    8      2       3 11170  81.00   M   11      2       3 11171  72.00   F    7      2       3 11172  45.00   M    8      2       2 11173  63.00   M    0      2       3 11174  74.00   M    9      2       3 11175  32.00   F    8      2       2 11176  55.00   F   10      2       2 11177  58.00   F    2      2       2 11178  84.00   F    5      2       3 11179  63.00   M    5      2       3 11180  61.00   M    4      2       3 11181  68.00   M    5      2       3 11182  90.00   F    5      2       3 11183  71.00   M    2      2       3 11184  82.00   M    3      2       3 11185  60.00   F   14      2       3 11186  76.00   M    3      2       3 11187  74.00   M    2      2       3 11188  86.00   M    1      2       3 11189  56.00   F    6      2       2 11190  74.00   F    4      2       3 11191   7.00   M   23      2       1 11192  65.00   M    0      2       3 11193  56.00   M    3      2       2 11194  60.00   M   12      2       3 11195  40.00   M   14      2       2 11196  58.00   F    2      2       2 11197  52.00   M   11      2       2 11198  66.00   F   13      2       3 11199  56.00   M    5      2       2 11200  60.00   M    0      2       3 11201  67.00   M    2      2       3 11202  54.00   F    2      2       2 11203  66.00   F    6      2       3 11204  47.00   M    7      2       2 11205  67.00   F    2      2       3 11206  45.00   F    3      2       2 11207  35.00   F    2      2       2 11208  70.00   M    1      2       3 11209  79.00   M    3      2       3 11210  53.00   M    3      2       2 11211  50.00   F    3      2       2 11212  48.00   M    1      2       2 11213  65.00   F    4      2       3 11214  72.00   M    2      2       3 11215  70.00   F    1      2       3 11216  50.00   M    2      2       2 11217  57.00   M    1      2       2 11218  70.00   F    1      2       3 11219  38.00   M    1      2       2 11220  70.00   M    4      2       3 11221  56.00   M   17      2       2 11222  84.00   F    0      2       3 11223  88.00   M   17      2       3 11224  49.00   M    1      2       2 11225  58.00   M    6      2       2 11226  78.00   F    5      2       3 11227  70.00   M   12      2       3 11228  51.00   M    3      2       2 11229  75.00   M    9      2       3 11230  70.00   F    0      2       3 11231  54.00   M    9      2       2 11232  55.00   M    1      2       2 11233  78.00   M    8      2       3 11234  73.00   M    9      2       3 11235  72.00   M   12      2       3 11236  41.00   M    8      2       2 11237  75.00   F    5      2       3 11238  58.00   F    5      2       2 11239  78.00   M    6      2       3 11240  66.00   M    3      2       3 11241  68.00   M    5      2       3 11242  62.00   F    4      2       3 11243  73.00   M    0      2       3 11244  71.00   M    1      2       3 11245  75.00   M    3      2       3 11246  60.00   F    2      2       3 11247  34.00   F    3      2       2 11248  70.00   M    3      2       3 11249  54.00   M    1      2       2 11250  41.00   F    1      2       2 11251  53.00   M    0      2       2 11252  60.00   F    1      2       3 11253  68.00   M    0      2       3 11254  70.00   M    1      2       3 11255  54.00   M   19      2       2 11256  50.00   M    1      2       2 11257  65.00   F    0      2       3 11258  67.00   F   17      2       3 11259  59.00   M   17      2       2 11260  78.00   M    3      2       3 11261  62.00   F    0      2       3 11262  20.00   F   14      2       2 11263  77.00   M    4      2       3 11264  32.00   M    6      2       2 11265  75.00   M    0      2       3 11266  75.00   M    8      2       3 11267  61.00   M    6      2       3 11268  75.00   M    0      2       3 11269  65.00   M    2      2       3 11270  70.00   M    2      2       3 11271  44.00   M    4      2       2 11272  63.00   F    2      2       3 11273  81.00   M    2      2       3 11274  72.00   F    2      2       3 11275  66.00   M    2      2       3 11276  55.00   M    0      2       2 11277  62.00   M    0      2       3 11278  71.00   M   25      2       3 11279  50.00   F   10      2       2 11280  80.00   F    2      2       3 11281  65.00   M    0      2       3 11282  74.00   M    9      2       3 11283  48.00   M   12      2       2 11284  79.00   F    0      2       3 11285  79.00   M    9      2       3 11286  40.00   M    4      2       2 11287  83.00   M    6      2       3 11288  67.00   M    5      2       3 11289  65.00   F    3      2       3 11290  73.00   M    2      2       3 11291  60.00   M    1      2       3 11292  57.00   M    5      2       2 11293  65.00   F    0      2       3 11294  65.00   M    1      2       3 11295  70.00   F    2      2       3 11296  50.00   M    0      2       2 11297  89.00   M    5      2       3 11298  49.00   F    3      2       2 11299  88.00   M   10      2       3 11300  68.00   F    6      2       3 11301  67.00   M   23      2       3 11302  41.00   M    0      2       2 11303  65.00   M    8      2       3 11304  70.00   M    0      2       3 11305  51.00   M    9      2       2 11306  63.00   M    6      2       3 11307  70.00   M   10      2       3 11308  82.00   M    7      2       3 11309  30.00   F    8      2       2 11310  48.00   F    0      2       2 11311  73.00   F    8      2       3 11312  79.00   M    5      2       3 11313  68.00   F    1      2       3 11314  84.00   M    5      2       3 11315  58.00   M    3      2       2 11316  55.00   F    8      2       2 11317  58.00   M    1      2       2 11318  72.00   F    3      2       3 11319  32.00   M    7      2       2 11320  80.00   F    0      2       3 11321  68.00   M    0      2       3 11322  75.00   F    8      2       3 11323  71.00   M   12      2       3 11324  50.00   M   12      2       2 11325  58.00   M    7      2       2 11326  73.00   F   10      2       3 11327  75.00   F    8      2       3 11328  47.00   M    7      2       2 11329  68.00   M    0      2       3 11330  50.00   M    8      2       2 11331  48.00   F    8      2       2 11332  45.00   F    4      2       2 11333  49.00   M    3      2       2 11334  52.00   M    3      2       2 11335  51.00   M    3      2       2 11336  84.00   M   12      2       3 11337  29.00   M    1      2       2 11338  72.00   M    0      2       3 11339  41.00   F    5      2       2 11340  96.00   M    2      2       3 11341  58.00   M    8      2       2 11342  77.00   M   17      2       3 11343  50.00   M    0      2       2 11344  51.00   M    8      2       2 11345  90.00   F    8      2       3 11346  72.00   M    4      2       3 11347  44.00   M    5      2       2 11348  74.00   M    1      2       3 11349  60.00   M    7      2       3 11350  60.00   M    4      2       3 11351  66.00   M    5      2       3 11352  35.00   M    6      2       2 11353  59.00   M    4      2       2 11354  72.00   M    5      2       3 11355  65.00   F    4      2       3 11356  50.00   F    0      2       2 11357  72.00   M    2      2       3 11358  68.00   M    2      2       3 11359  65.00   F    0      2       3 11360  40.00   M    0      2       2 11361  54.00   M    2      2       2 11362  62.00   M    0      2       3 11363  78.00   F    4      2       3 11364  80.00   M    2      2       3 11365  48.00   M    0      2       2 11366  75.00   M    0      2       3 11367  58.00   M    1      2       2 11368  89.00   M    5      2       3 11369  27.00   M   18      2       2 11370  78.00   M    1      2       3 11371  60.00   F    9      2       3 11372  80.00   F    7      2       3 11373  40.00   M    1      2       2 11374  60.00   F    5      2       3 11375  52.00   M    4      2       2 11376  72.00   M    5      2       3 11377  61.00   M    5      2       3 11378  40.00   F    1      2       2 11379  41.00   M    4      2       2 11380  41.00   M    1      2       2 11381  65.00   M    0      2       3 11382  69.00   M   13      2       3 11383  40.00   M    5      2       2 11384  60.00   M    0      2       3 11385  53.00   F    0      2       2 11386  66.00   M    3      2       3 11387  66.00   M   12      2       3 11388  80.00   F   12      2       3 11389  65.00   M   11      2       3 11390  80.00   M    2      2       3 11391  60.00   F    3      2       3 11392  77.00   M    4      2       3 11393  48.00   M    4      2       2 11394  66.00   F    3      2       3 11395  45.00   M    3      2       2 11396  70.00   M    4      2       3 11397  45.00   F    1      2       2 11398  58.00   M    0      2       2 11399  78.00   F   19      2       3 11400  59.00   M    6      2       2 11401  70.00   M    4      2       3 11402  53.00   F   12      2       2 11403  56.00   M    4      2       2 11404  88.00   F    0      2       3 11405  49.00   F    0      2       2 11406  79.00   M    0      2       3 11407  70.00   M    1      2       3 11408  62.00   M    0      2       3 11409  85.00   M    9      2       3 11410  56.00   M    9      2       2 11411  64.00   M    2      2       3 11412  54.00   M    7      2       2 11413  17.00   F    6      2       1 11414  80.00   F    4      2       3 11415  65.00   M    2      2       3 11416  60.00   M   23      2       3 11417  73.00   M    6      2       3 11418  30.00   F    8      2       2 11419  52.00   M    5      2       2 11420  68.00   M    0      2       3 11421  74.00   M    7      2       3 11422  80.00   M   10      2       3 11423  65.00   M    8      2       3 11424  82.00   F    1      2       3 11425  65.00   F    6      2       3 11426  70.00   M    6      2       3 11427  36.00   M    2      2       2 11428  56.00   F    5      2       2 11429  17.00   M    1      2       1 11430  38.00   M    1      2       2 11431  68.00   M    3      2       3 11432  68.00   M    3      2       3 11433  76.00   M    3      2       3 11434  68.00   M    2      2       3 11435  38.00   F    0      2       2 11436  60.00   M    0      2       3 11437  65.00   F    0      2       3 11438  74.00   F   11      2       3 11439  75.00   M    7      2       3 11440  79.00   F   17      2       3 11441  65.00   M   11      2       3 11442  48.00   F   10      2       2 11443  81.00   M    1      2       3 11444  77.00   M    3      2       3 11445  70.00   M    0      2       3 11446  65.00   F    1      2       3 11447  89.00   M    1      2       3 11448  55.00   M    0      2       2 11449  68.00   M    4      2       3 11450  65.00   M    4      2       3 11451  70.00   M   10      2       3 11452  70.00   M    0      2       3 11453  82.00   M   11      2       3 11454  69.00   M   13      2       3 11455  60.00   M    2      2       3 11456  74.00   M    9      2       3 11457  48.00   F    3      2       2 11458  45.00   M    3      2       2 11459  67.00   M    6      2       3 11460  53.00   F    3      2       2 11461  73.00   M    4      2       3 11462  54.00   M    0      2       2 11463  60.00   M    2      2       3 11464  55.00   M    0      2       2 11465  48.00   M    3      2       2 11466  65.00   M   13      2       3 11467  65.00   F   11      2       3 11468  32.00   M    2      2       2 11469  60.00   M    5      2       3 11470  64.00   M   10      2       3 11471  87.00   F    9      2       3 11472  72.00   M   10      2       3 11473  62.00   M   10      2       3 11474  40.00   M    2      2       2 11475  71.00   M    4      2       3 11476  63.00   M    5      2       3 11477  82.00   M    6      2       3 11478  76.00   F    5      2       3 11479  78.00   M    5      2       3 11480  65.00   M    3      2       3 11481  75.00   M    3      2       3 11482  70.00   M    3      2       3 11483  52.00   F    5      2       2 11484  27.00   F    0      2       2 11485  75.00   M   20      2       3 11486  43.00   M    0      2       2 11487  60.00   M   20      2       3 11488  65.00   F    9      2       3 11489  70.00   F    4      2       3 11490  50.00   M   13      2       2 11491  48.00   M    3      2       2 11492  62.00   M   14      2       3 11493  70.00   M    5      2       3 11494  53.00   M    6      2       2 11495  49.00   M   12      2       2 11496  68.00   F    2      2       3 11497  45.00   M    8      2       2 11498  43.00   M    9      2       2 11499  85.00   M    9      2       3 11500  46.00   M    7      2       2 11501  35.00   M    9      2       2 11502  65.00   F    7      2       3 11503  91.00   F    6      2       3 11504  86.00   M    7      2       3 11505  94.00   M    1      2       3 11506  67.00   M    2      2       3 11507  55.00   F    2      2       2 11508  75.00   M    1      2       3 11509  36.00   M    1      2       2 11510  55.00   F   30      2       2 11511  41.00   M   18      1       2 11512   0.00   F   14      1       1 11513  13.00   F   17      1       1 11514   0.00   M   15      1       1 11515  26.00   M    7      1       2 11516   0.00   M   22      1       1 11517  32.00   M   14      1       2 11518  63.00   M   15      1       3 11519  20.00   F   15      1       2 11520  67.00   F   13      1       3 11521  25.00   M   13      1       2 11522  56.00   F   19      1       2 11523  35.00   M   18      1       2 11524  35.00   M   20      1       2 11525  53.00   F   22      1       2 11526  39.00   M   25      1       2 11527  21.00   M   24      1       2 11528  31.00   M   24      1       2 11529  35.00   M   17      1       2 11530  35.00   M   15      1       2 11531  64.00   F   16      1       3 11532  36.00   F   16      1       2 11533  27.00   M   15      1       2 11534  51.00   M   17      1       2 11535  22.00   M   14      1       2 11536  46.00   M   20      1       2 11537  38.00   M   15      1       2 11538  41.00   M   14      1       2 11539  30.00   F   22      1       2 11540  24.00   M   14      1       2 11541  60.00   M   27      1       3 11542  22.00   M   20      1       2 11543  32.00   M   14      1       2 11544  40.00   M   15      1       2 11545  65.00   M   15      1       3 11546  56.00   F   16      1       2 11547  56.00   F   24      1       2 11548  47.00   M   14      1       2 11549  70.00   F   20      1       3 11550  23.00   M   14      1       2 11551  37.00   F   12      1       2 11552  63.00   M   19      1       3 11553  59.00   F   19      1       2 11554  26.00   M   21      1       2 11555  26.00   M   19      1       2 11556  63.00   F   17      1       3 11557  69.00   M   17      1       3 11558   9.00   F   20      1       1 11559   7.00   F   20      1       1 11560  34.00   M   18      1       2 11561  35.00   M   16      1       2 11562  70.00   F   37      1       3 11563  64.00   M   23      1       3 11564  45.00   M   24      1       2 11565   0.80   M   16      1       1 11566  20.00   M   21      1       2 11567  25.00   F   21      1       2 11568  35.00   F   21      1       2 11569  33.00   F   18      1       2 11570  22.00   M   19      1       2 11571  18.00   M   13      1       2 11572  21.00   M   14      1       2 11573  54.00   F   18      1       2 11574  28.00   F   18      1       2 11575  23.00   F   18      1       2 11576  21.00   M   14      1       2 11577  23.00   M   15      1       2 11578  70.00   M   13      1       3 11579  32.00   F   15      1       2 11580  38.00   M   15      1       2 11581  18.00   M   15      1       2 11582  63.00   F   35      1       3 11583  20.00   M    9      1       2 11584  24.00   M   18      1       2 11585  39.00   M   20      1       2 11586  38.00   M   14      1       2 11587  21.00   M   14      1       2 11588  31.00   M   20      1       2 11589  42.00   M   14      1       2 11590  35.00   M   16      1       2 11591  29.00   M   21      1       2 11592  13.00   M   17      1       1 11593  32.00   M   13      1       2 11594  34.00   M   13      1       2 11595  21.00   M   15      1       2 11596  24.00   M   13      1       2 11597  52.00   M   23      1       2 11598  48.00   F   26      1       2 11599  26.00   F   23      1       2 11600  40.00   M   14      1       2 11601  19.00   M   17      1       2 11602  40.00   F   21      1       2 11603  35.00   M   13      1       2 11604  41.00   M   18      1       2 11605  34.00   M   14      1       2 11606  26.00   M   18      1       2 11607  60.00   F   16      1       3 11608  40.00   M   15      1       2 11609  62.00   F   50      1       3 11610  24.00   M   17      1       2 11611  37.00   M   25      1       2 11612  27.00   M   18      1       2 11613  33.00   M   13      1       2 11614  49.00   M   14      1       2 11615  26.00   M   12      1       2 11616  63.00   M   27      1       3 11617  63.00   M   17      1       3 11618  27.00   F   17      1       2 11619  24.00   M   23      1       2 11620  22.00   M   16      1       2 11621  14.00   M   41      1       1 11622  48.00   M   21      1       2 11623  30.00   M   21      1       2 11624  41.00   M   21      1       2 11625  66.00   M   21      1       3 11626  59.00   M   21      1       2 11627  39.00   M   21      1       2 11628  60.00   M   21      1       3 11629  63.00   M   30      1       3 11630  73.00   M   21      1       3 11631  45.00   M   21      1       2 11632  60.00   F   22      1       3 11633  70.00   M   18      1       3 11634  26.00   M   26      1       2 11635  20.00   M   18      1       2 11636  21.00   M   16      1       2 11637  57.00   M   24      1       2 11638  43.00   M   22      1       2 11639  78.00   M   13      1       3 11640  60.00   M   26      1       3 11641  38.00   M   17      1       2 11642  19.00   M   14      1       2 11643  39.00   M   17      1       2 11644  39.00   M   17      1       2 11645  54.00   M   14      1       2 11646  40.00   M   15      1       2 11647  34.00   M   24      1       2 11648  47.00   F   19      1       2 11649  63.00   F   14      1       3 11650  43.00   M   14      1       2 11651  52.00   M   14      1       2 11652  68.00   M   14      1       3 11653  62.00   F   14      1       3 11654  40.00   M   16      1       2 11655  67.00   F   27      1       3 11656  41.00   F   20      1       2 11657  41.00   M   21      1       2 11658  32.00   M   15      1       2 11659  62.00   F   22      1       3 11660  20.00   M   15      1       2 11661  37.00   M   12      1       2 11662  57.00   M   21      1       2 11663  22.00   M   16      1       2 11664  26.00   M   12      1       2 11665  32.00   F   22      1       2 11666  35.00   M   15      1       2 11667  56.00   F   15      1       2 11668  58.00   M   15      1       2 11669  43.00   M   15      1       2 11670  33.00   M   19      1       2 11671  41.00   F   19      1       2 11672  29.00   M   14      1       2 11673  50.00   M   14      1       2 11674  35.00   M   17      1       2 11675  68.00   M   17      1       3 11676  32.00   M   19      1       2 11677  36.00   M   19      1       2 11678  65.00   M   19      1       3 11679  28.00   F   17      1       2 11680  57.00   M   18      1       2 11681  26.00   F   16      1       2 11682  72.00   F   16      1       3 11683  35.00   M   26      1       2 11684  23.00   M   17      1       2 11685  27.00   F   18      1       2 11686  50.00   M   17      1       2 11687  55.00   M   21      1       2 11688  68.00   M   12      1       3 11689  32.00   M   17      1       2 11690   4.00   M   17      1       1 11691  13.00   M   17      1       1 11692   9.00   F   24      1       1 11693  19.00   F   17      1       2 11694  27.00   M   19      1       2 11695  48.00   F   14      1       2 11696  40.00   F   17      1       2 11697  22.00   M   25      1       2 11698  27.00   M   15      1       2 11699  42.00   M   17      1       2 11700  27.00   M   17      1       2 11701  48.00   M   16      1       2 11702  57.00   M   16      1       2 11703   8.00   M   11      1       1 11704  48.00   F   18      1       2 11705  33.00   M   12      1       2 11706  28.00   F   23      1       2 11707  48.00   F   34      1       2 11708  35.00   M   15      1       2 11709  11.00   F   15      1       1 11710  32.00   M   15      1       2 11711  46.00   M   15      1       2 11712  43.00   M   15      1       2 11713  50.00   F   21      1       2 11714  27.00   M   23      1       2 11715  31.00   M   17      1       2 11716  26.00   M   15      1       2 11717  32.00   M   16      1       2 11718  75.00   F   16      1       3 11719  58.00   M   16      1       2 11720  24.00   F   19      1       2 11721  60.00   F   15      1       3 11722  38.00   F   19      1       2 11723  19.00   M   37      1       2 11724  38.00   M   24      1       2 11725  55.00   M   24      1       2 11726  25.00   M   20      1       2 11727   2.00   M   19      1       1 11728  13.00   M   15      1       1 11729  12.00   F   15      1       1 11730  10.00   M   15      1       1 11731  49.00   M   15      1       2 11732  20.00   F   15      1       2 11733   5.00   M   21      1       1 11734  36.00   M   19      1       2 11735   7.00   F   19      1       1 11736  37.00   M   21      1       2 11737  60.00   F   23      1       3 11738   8.00   F   21      1       1 11739  18.00   M   21      1       2 11740  27.00   M   24      1       2 11741  16.00   F   24      1       1 11742  35.00   F   24      1       2 11743  20.00   M   23      1       2 11744  14.00   F   23      1       1 11745  45.00   F   23      1       2 11746  39.00   M   15      1       2 11747  62.00   M   20      1       3 11748  43.00   M   19      1       2 11749  32.00   F   19      1       2 11750  39.00   F   19      1       2 11751  26.00   M   18      1       2 11752  10.00   F   18      1       1 11753  51.00   M   18      1       2 11754  35.00   F   18      1       2 11755  33.00   M   26      1       2 11756  36.00   M   17      1       2 11757  59.00   M   15      1       2 11758  52.00   M   21      1       2 11759  39.00   M   21      1       2 11760  41.00   M   12      1       2 11761  30.00   M   18      1       2 11762  27.00   M   12      1       2 11763  35.00   M   18      1       2 11764  26.00   M   22      1       2 11765  23.00   M   12      1       2 11766  35.00   M   15      1       2 11767  28.00   M   12      1       2 11768  32.00   M   20      1       2 11769  72.00   M   14      1       3 11770   1.00   M   16      1       1 11771  38.00   F   37      1       2 11772  25.00   M   18      1       2 11773  32.00   F   15      1       2 11774  28.00   F   31      1       2 11775  13.00   M   14      1       1 11776  51.00   F   16      1       2 11777  42.00   M   16      1       2 11778  33.00   M   24      1       2 11779  16.00   F   16      1       1 11780  65.00   F   16      1       3 11781  30.00   F   24      1       2 11782  54.00   F   16      1       2 11783  58.00   F   20      1       2 11784  54.00   F    7      1       2 11785  43.00   M   14      1       2 11786  47.00   M   18      1       2 11787  25.00   M   20      1       2 11788  45.00   F   18      1       2 11789  30.00   M   18      1       2 11790  43.00   M   18      1       2 11791  50.00   M   20      1       2 11792  35.00   M   18      1       2 11793  25.00   M   24      1       2 11794  64.00   M   18      1       3 11795  23.00   F   14      1       2 11796  52.00   M   14      1       2 11797  59.00   F   15      1       2 11798  12.00   M   17      1       1 11799  65.00   M   15      1       3 11800  66.00   M   15      1       3 11801  37.00   M   15      1       2 11802  70.00   F   19      1       3 11803   1.50   F   15      1       1 11804  38.00   M   12      1       2 11805  26.00   F   12      1       2 11806  55.00   F   17      1       2 11807  32.00   M   16      1       2 11808   5.00   M   16      1       1 11809  55.00   M   14      1       2 11810  11.00   F   14      1       1 11811  50.00   F   12      1       2 11812  33.00   M   22      1       2 11813  33.00   M   16      1       2 11814  41.00   F   13      1       2 11815  25.00   M   19      1       2 11816  29.00   M   19      1       2 11817  45.00   M   22      1       2 11818  39.00   M   19      1       2 11819   6.00   M   14      1       1 11820  25.00   F   14      1       2 11821  18.00   M   20      1       2 11822   6.00   M   18      1       1 11823  28.00   F   18      1       2 11824  68.00   F   20      1       3 11825  21.00   M   14      1       2 11826  48.00   F   21      1       2 11827  10.00   F   21      1       1 11828  50.00   M   16      1       2 11829  24.00   M   14      1       2 11830  36.00   M   23      1       2 11831  20.00   M   23      1       2 11832  19.00   M   16      1       2 11833  38.00   M   12      1       2 11834  38.00   M   12      1       2 11835  26.00   M   16      1       2 11836  28.00   M   12      1       2 11837  22.00   M   12      1       2 11838  29.00   M   12      1       2 11839  26.00   M   12      1       2 11840  32.00   M   14      1       2 11841  23.00   M   14      1       2 11842  28.00   M   14      1       2 11843  21.00   M   14      1       2 11844  65.00   F   17      1       3 11845  34.00   M   17      1       2 11846  17.00   M   17      1       1 11847  37.00   M   17      1       2 11848  38.00   M   17      1       2 11849  34.00   M   16      1       2 11850  16.00   M   16      1       1 11851  60.00   M   13      1       3 11852  63.00   M   20      1       3 11853  30.00   M   12      1       2 11854  50.00   M   12      1       2 11855  65.00   M   17      1       3 11856  48.00   F   17      1       2 11857  65.00   M   12      1       3 11858  42.00   M   22      1       2 11859  39.00   M   18      1       2 11860  32.00   M   22      1       2 11861  32.00   M   18      1       2 11862  26.00   F   15      1       2 11863  55.00   F   16      1       2 11864  21.00   F   16      1       2 11865  43.00   M   18      1       2 11866  43.00   M   22      1       2 11867  47.00   M   17      1       2 11868  30.00   M   16      1       2 11869  36.00   M   13      1       2 11870  28.00   M   17      1       2 11871  46.00   M   25      1       2 11872  20.00   M   21      1       2 11873  39.00   M   16      1       2 11874  23.00   F   14      1       2 11875  30.00   F   25      1       2 11876  17.00   M   15      1       1 11877  13.00   F   12      1       1 11878  30.00   F   14      1       2 11879  50.00   M   16      1       2 11880  19.00   M   16      1       2 11881  24.00   M   20      1       2 11882   7.00   F   20      1       1 11883  36.00   M   38      1       2 11884  27.00   F   20      1       2 11885  25.00   F   14      1       2 11886  21.00   F   23      1       2 11887  28.00   M   20      1       2 11888  47.00   F   14      1       2 11889  10.00   M   14      1       1 11890  34.00   F   14      1       2 11891  38.00   F   17      1       2 11892  14.00   M   20      1       1 11893  27.00   M   23      1       2 11894  18.00   F   19      1       2 11895  30.00   F   37      1       2 11896  29.00   M   16      1       2 11897  61.00   M   16      1       3 11898  18.00   F   12      1       2 11899  26.00   M   12      1       2 11900  26.00   M   12      1       2 11901  25.00   F   27      1       2 11902  54.00   M   16      1       2 11903  28.00   F   15      1       2 11904  46.00   F   14      1       2 11905  35.00   F   17      1       2 11906   0.30   M   14      1       1 11907  26.00   F   14      1       2 11908  56.00   M   11      1       2 11909  32.00   M   13      1       2 11910  25.00   F   14      1       2 11911  30.00   F   27      1       2 11912  13.00   F   27      1       1 11913  30.00   M   29      1       2 11914  30.00   M   23      1       2 11915  22.00   M   23      1       2 11916  40.00   M   21      1       2 11917  30.00   M   15      1       2 11918  25.00   M   15      1       2 11919  37.00   M   15      1       2 11920  43.00   M   24      1       2 11921  24.00   M   15      1       2 11922  47.00   M   16      1       2 11923  28.00   F   16      1       2 11924  32.00   M   16      1       2 11925  49.00   M   13      1       2 11926  32.00   M   15      1       2 11927  10.00   F   24      1       1 11928  30.00   M   31      1       2 11929  22.00   M   23      1       2 11930  39.00   M   16      1       2 11931  35.00   M   22      1       2 11932  32.00   M   53      1       2 11933  23.00   M   22      1       2 11934  28.00   M   24      1       2 11935  46.00   M   18      1       2 11936  17.00   M   16      1       1 11937  26.00   M   31      1       2 11938  35.00   M   22      1       2 11939  31.00   M   22      1       2 11940  24.00   M   28      1       2 11941  60.00   M   27      1       3 11942  27.00   M   16      1       2 11943  14.00   M   16      1       1 11944  26.00   M   22      1       2 11945  20.00   M   22      1       2 11946  22.00   M   15      1       2 11947  58.00   M   19      1       2 11948  38.00   M   22      1       2 11949  44.00   M   22      1       2 11950  37.00   M   14      1       2 11951  21.00   M    7      1       2 11952  17.00   M   21      1       1 11953  19.00   F   31      1       2 11954  28.00   M   21      1       2 11955  37.00   M   25      1       2 11956  45.00   M   18      1       2 11957  38.00   M   27      1       2 11958  80.00   F   23      1       3 11959  55.00   F   18      1       2 11960  42.00   F   18      1       2 11961  39.00   F   18      1       2 11962  18.00   M   15      1       2 11963  33.00   F   18      1       2 11964  52.00   F   21      1       2 11965   6.00   M   18      1       1 11966  27.00   F   30      1       2 11967  20.00   M   18      1       2 11968   8.00   M   23      1       1 11969  30.00   F   23      1       2 11970  50.00   F   14      1       2 11971  27.00   M   30      1       2 11972  27.00   M   35      1       2 11973  66.00   F   30      1       3 11974  47.00   F   18      1       2 11975  65.00   F   14      1       3 11976   7.00   M   14      1       1 11977  13.00   M   16      1       1 11978  50.00   M   16      1       2 11979  45.00   M   25      1       2 11980  32.00   F   15      1       2 11981  21.00   F   15      1       2 11982  45.00   M   25      1       2 11983  27.00   M   13      1       2 11984  22.00   F   16      1       2 11985  48.00   M   27      1       2 11986  75.00   M   12      1       3 11987  55.00   M   20      1       2 11988  40.00   F   12      1       2 11989  43.00   M   12      1       2 11990  28.00   F   12      1       2 11991  45.00   F   12      1       2 11992  22.00   F   12      1       2 11993  20.00   F   14      1       2 11994  11.00   M   14      1       1 11995  22.00   M   14      1       2 11996  46.00   F   15      1       2 11997  28.00   M   11      1       2 11998  14.00   F   11      1       1 11999  22.00   M   11      1       2 12000  40.00   F   17      1       2 12001  20.00   M   11      1       2 12002  17.00   F   11      1       1 12003  12.00   F   11      1       1 12004  35.00   F   23      1       2 12005  62.00   M   12      1       3 12006  33.00   F   22      1       2 12007   9.00   M   18      1       1 12008  75.00   F   18      1       3 12009  48.00   F   25      1       2 12010   8.00   F   18      1       1 12011  65.00   F   17      1       3 12012  20.00   M   25      1       2 12013  28.00   M   13      1       2 12014  35.00   F   14      1       2 12015  15.00   M   17      1       1 12016  60.00   F   25      1       3 12017  16.00   F   17      1       1 12018  13.00   F   17      1       1 12019  35.00   M   47      1       2 12020  64.00   F   31      1       3 12021  25.00   M   32      1       2 12022  24.00   F   19      1       2 12023  27.00   M   32      1       2 12024  30.00   F   17      1       2 12025  19.00   M   12      1       2 12026  32.00   F   17      1       2 12027  13.00   M   12      1       1 12028  12.00   M   12      1       1 12029  55.00   M   31      1       2 12030  54.00   M   27      1       2 12031  69.00   M   17      1       3 12032  62.00   M   20      1       3 12033  31.00   F   19      1       2 12034  26.00   F   21      1       2 12035  34.00   M   33      1       2 12036   1.00   M   19      1       1 12037  18.00   F   19      1       2 12038  16.00   M   19      1       1 12039  40.00   F   17      1       2 12040  20.00   F   16      1       2 12041  57.00   M   22      1       2 12042  40.00   M   16      1       2 12043  29.00   F   16      1       2 12044  54.00   M   15      1       2 12045  22.00   M   23      1       2 12046  45.00   M   12      1       2 12047  23.00   M   17      1       2 12048  45.00   M   16      1       2 12049  38.00   F   15      1       2 12050  26.00   F   23      1       2 12051  30.00   M   15      1       2 12052  13.00   F   14      1       1 12053  54.00   M   14      1       2 12054  41.00   M   19      1       2 12055  68.00   M   23      1       3 12056  60.00   F   23      1       3 12057  23.00   F   21      1       2 12058  24.00   M   22      1       2 12059  35.00   F   16      1       2 12060  78.00   M   15      1       3 12061  22.00   M   14      1       2 12062  45.00   F   27      1       2 12063  24.00   F   28      1       2 12064  45.00   F   28      1       2 12065  30.00   M   24      1       2 12066  52.00   F   16      1       2 12067  38.00   M   16      1       2 12068  32.00   F   17      1       2 12069  35.00   M   26      1       2 12070  32.00   F   17      1       2 12071  12.00   F   20      1       1 12072   7.00   M   19      1       1 12073  38.00   M   17      1       2 12074  49.00   F   20      1       2 12075  27.00   M   27      1       2 12076  25.00   M   24      1       2 12077  33.00   M   20      1       2 12078  62.00   F   17      1       3 12079  34.00   F   31      1       2 12080  20.00   F   21      1       2 12081  22.00   F   21      1       2 12082   6.00   M   20      1       1 12083  17.00   M   24      1       1 12084  42.00   M   24      1       2 12085  11.00   M   16      1       1 12086  30.00   M   22      1       2 12087  20.00   F   25      1       2 12088  19.00   F   16      1       2 12089  32.00   M   21      1       2 12090  36.00   F   15      1       2 12091  37.00   M   19      1       2 12092  35.00   F   15      1       2 12093  46.00   F   15      1       2 12094  50.00   M   15      1       2 12095  16.00   M   15      1       1 12096  72.00   F   15      1       3 12097  22.00   M   15      1       2 12098  60.00   M   15      1       3 12099  40.00   M    4      1       2 12100  30.00   F    3      1       2 12101  34.00   M   26      1       2 12102  45.00   M   17      1       2 12103  29.00   F   13      1       2 12104  29.00   M   13      1       2 12105  51.00   M   45      1       2 12106  18.00   F   20      1       2 12107  30.00   M   17      1       2 12108  25.00   M   17      1       2 12109  51.00   M   19      1       2 12110  48.00   M   16      1       2 12111  52.00   F   17      1       2 12112  36.00   F   17      1       2 12113  15.00   F   19      1       1 12114  19.00   F   20      1       2 12115  27.00   M   19      1       2 12116  42.00   F   19      1       2 12117   5.00   M   19      1       1 12118  40.00   M   19      1       2 12119  26.00   M   31      1       2 12120  11.00   F   15      1       1 12121  35.00   F   44      1       2 12122  16.00   F   15      1       1 12123  40.00   F   25      1       2 12124  25.00   M   19      1       2 12125  52.00   M   16      1       2 12126  18.00   F   15      1       2 12127  45.00   M   15      1       2 12128  55.00   F   15      1       2 12129  26.00   M   16      1       2 12130  47.00   M   19      1       2 12131  30.00   F   15      1       2 12132  15.00   M   28      1       1 12133  40.00   F   15      1       2 12134  23.00   M   18      1       2 12135   0.80   M   15      1       1 12136  32.00   M   18      1       2 12137  30.00   M   22      1       2 12138  16.00   F   19      1       1 12139  35.00   F   19      1       2 12140  53.00   F   17      1       2 12141  40.00   F   15      1       2 12142  35.00   M   15      1       2 12143  36.00   F   15      1       2 12144  41.00   M   15      1       2 12145  13.00   F   18      1       1 12146  49.00   F   12      1       2 12147  55.00   F   15      1       2 12148  80.00   F   23      1       3 12149  19.00   F   19      1       2 12150  35.00   M   20      1       2 12151  23.00   M   14      1       2 12152  35.00   F   23      1       2 12153  22.00   M   14      1       2 12154  19.00   M   17      1       2 12155  40.00   M   23      1       2 12156  25.00   M   14      1       2 12157  58.00   M   17      1       2 12158  30.00   F   17      1       2 12159  20.00   M   17      1       2 12160  16.00   F   20      1       1 12161  21.00   F   24      1       2 12162  45.00   M   17      1       2 12163  38.00   F   17      1       2 12164  20.00   F   24      1       2 12165  23.00   M   17      1       2 12166  20.00   M   24      1       2 12167  10.00   M   16      1       1 12168  20.00   M   16      1       2 12169  18.00   F   16      1       2 12170  27.00   F   20      1       2 12171  18.00   F   16      1       2 12172   6.00   M   19      1       1 12173   9.00   M   16      1       1 12174  36.00   M   16      1       2 12175  32.00   F   15      1       2 12176   3.00   F   20      1       1 12177  13.00   F   15      1       1 12178   8.00   M   15      1       1 12179  38.00   F   20      1       2 12180  34.00   F   17      1       2 12181  25.00   F   16      1       2 12182  18.00   F   16      1       2 12183  11.00   F   16      1       1 12184  39.00   F   16      1       2 12185  33.00   M   16      1       2 12186  75.00   F   21      1       3 12187  12.00   F   16      1       1 12188  83.00   M   16      1       3 12189   0.40   F   21      1       1 12190   3.00   F   16      1       1 12191  60.00   M   16      1       3 12192  22.00   F   18      1       2 12193  64.00   M   16      1       3 12194  64.00   M   16      1       3 12195  64.00   M   23      1       3 12196  20.00   F   21      1       2 12197  45.00   M   16      1       2 12198  24.00   M   19      1       2 12199  24.00   M   14      1       2 12200  26.00   M   14      1       2 12201  36.00   M   16      1       2 12202  60.00   M   21      1       3 12203  55.00   F   16      1       2 12204  20.00   F   25      1       2 12205  40.00   F   16      1       2 12206  45.00   M   24      1       2 12207  20.00   F   25      1       2 12208  40.00   M   22      1       2 12209  24.00   M   21      1       2 12210  37.00   F   22      1       2 12211  11.00   F   13      1       1 12212  30.00   M   18      1       2 12213  12.00   F   18      1       1 12214  30.00   F   18      1       2 12215  46.00   M   16      1       2 12216  19.00   F   16      1       2 12217  31.00   F   22      1       2 12218  48.00   M   19      1       2 12219  30.00   M   41      1       2 12220  60.00   F   41      1       3 12221  70.00   F   41      1       3 12222   2.50   F   15      1       1 12223  65.00   M   15      1       3 12224  50.00   M   15      1       2 12225  68.00   M   10      1       3 12226   1.50   M   15      1       1 12227  17.00   F   17      1       1 12228  23.00   F   15      1       2 12229  34.00   M   15      1       2 12230  26.00   M   15      1       2 12231  17.00   M   22      1       1 12232  71.00   M   23      1       3 12233  32.00   F   15      1       2 12234  40.00   M   20      1       2 12235  27.00   M   24      1       2 12236  38.00   M   24      1       2 12237  29.00   M   37      1       2 12238  50.00   F   14      1       2 12239  15.00   M   14      1       1 12240  16.00   M   14      1       1 12241  42.00   M   14      1       2 12242  21.00   F   16      1       2 12243  31.00   M   16      1       2 12244  60.00   M   14      1       3 12245  22.00   M   39      1       2 12246  35.00   M   19      1       2 12247  30.00   M   19      1       2 12248  72.00   M   13      1       3 12249  65.00   M   17      1       3 12250  65.00   M   17      1       3 12251  18.00   M   22      1       2 12252  56.00   M   19      1       2 12253  43.00   M   19      1       2 12254  25.00   M   17      1       2 12255  27.00   M   19      1       2 12256  20.00   M   17      1       2 12257  33.00   M   16      1       2 12258  50.00   F   28      1       2 12259  29.00   F   40      1       2 12260   2.00   F   16      1       1 12261  38.00   M   22      1       2 12262  17.00   F   22      1       1 12263  60.00   F   16      1       3 12264  10.00   M   28      1       1 12265  14.00   F   22      1       1 12266  40.00   F   16      1       2 12267  63.00   M   16      1       3 12268  20.00   M   16      1       2 12269  56.00   F   33      1       2 12270  29.00   M   16      1       2 12271  60.00   F   22      1       3 12272  25.00   F   16      1       2 12273   3.00   F   22      1       1 12274  46.00   F   22      1       2 12275  50.00   F   22      1       2 12276  30.00   F   33      1       2 12277   8.00   F   22      1       1 12278  12.00   F   16      1       1 12279  22.00   M   36      1       2 12280  75.00   F   16      1       3 12281  12.00   M   16      1       1 12282  14.00   M   16      1       1 12283  27.00   F   22      1       2 12284   8.00   F   16      1       1 12285   3.00   M   22      1       1 12286   6.00   M   16      1       1 12287  22.00   M   14      1       2 12288  35.00   M   15      1       2 12289  38.00   M   14      1       2 12290  33.00   M   13      1       2 12291  30.00   F   17      1       2 12292  56.00   F   12      1       2 12293  26.00   M   16      1       2 12294  20.00   M   23      1       2 12295  28.00   M   14      1       2 12296  20.00   F   14      1       2 12297  50.00   M   19      1       2 12298  27.00   M   26      1       2 12299  55.00   F   16      1       2 12300  26.00   M   34      1       2 12301  30.00   F   18      1       2 12302  30.00   M   15      1       2 12303  46.00   M   18      1       2 12304  55.00   M   14      1       2 12305  14.00   M   17      1       1 12306  33.00   F   20      1       2 12307  38.00   M   20      1       2 12308  28.00   M   11      1       2 12309  16.00   M   14      1       1 12310  14.00   M   14      1       1 12311  33.00   M   14      1       2 12312  21.00   M   14      1       2 12313  19.00   M   14      1       2 12314  19.00   M   14      1       2 12315  34.00   M   14      1       2 12316  26.00   M   16      1       2 12317  50.00   F   16      1       2 12318  55.00   M   21      1       2 12319  31.00   M   21      1       2 12320  25.00   M   21      1       2 12321  70.00   M   21      1       3 12322  26.00   M   21      1       2 12323  18.00   M   21      1       2 12324  19.00   M   21      1       2 12325  20.00   M   21      1       2 12326  27.00   M   21      1       2 12327  33.00   F   18      1       2 12328  38.00   F   18      1       2 12329  38.00   F   19      1       2 12330  23.00   M   18      1       2 12331  30.00   M   18      1       2 12332  30.00   M   14      1       2 12333  17.00   M   14      1       1 12334  32.00   M   14      1       2 12335  20.00   M   14      1       2 12336  18.00   M   14      1       2 12337  29.00   M   18      1       2 12338  20.00   M   14      1       2 12339  36.00   M   22      1       2 12340  27.00   F   22      1       2 12341   7.00   F   28      1       1 12342  45.00   M   17      1       2 12343   4.00   F   28      1       1 12344  36.00   M   22      1       2 12345  22.00   F   18      1       2 12346  70.00   F   18      1       3 12347  22.00   M   18      1       2 12348  27.00   M   18      1       2 12349  21.00   M   18      1       2 12350  35.00   M   18      1       2 12351  61.00   M   14      1       3 12352  65.00   M   14      1       3 12353  17.00   F   18      1       1 12354  40.00   M   18      1       2 12355  20.00   M   50      1       2 12356  23.00   M   15      1       2 12357  45.00   M   13      1       2 12358  37.00   F   14      1       2 12359   2.00   F   13      1       1 12360  18.00   F   20      1       2 12361  16.00   F   24      1       1 12362  30.00   M   15      1       2 12363  18.00   M   15      1       2 12364  26.00   M   13      1       2 12365  38.00   F   15      1       2 12366  38.00   F   21      1       2 12367  17.00   M   23      1       1 12368  14.00   F   24      1       1 12369  43.00   M   27      1       2 12370  42.00   F   16      1       2 12371  30.00   F   12      1       2 12372  19.00   M   16      1       2 12373  28.00   F   16      1       2 12374  15.00   F   16      1       1 12375  35.00   F   16      1       2 12376  24.00   M   18      1       2 12377  40.00   M   15      1       2 12378  48.00   F   15      1       2 12379  38.00   M   17      1       2 12380   6.00   M   17      1       1 12381  26.00   M   15      1       2 12382   8.00   M   16      1       1 12383  10.00   M   17      1       1 12384  45.00   M   17      1       2 12385  40.00   F   16      1       2 12386  10.00   M   16      1       1 12387  47.00   M   20      1       2 12388  44.00   M   24      1       2 12389  28.00   M   14      1       2 12390  27.00   F   42      1       2 12391  23.00   M   20      1       2 12392  25.00   M   23      1       2 12393  37.00   M   11      1       2 12394  80.00   F   13      1       3 12395  28.00   M   15      1       2 12396  18.00   M   23      1       2 12397  32.00   F   15      1       2 12398  24.00   M   22      1       2 12399   3.00   F   16      1       1 12400  39.00   M   16      1       2 12401  42.00   M   17      1       2 12402  24.00   F   21      1       2 12403   7.00   M   19      1       1 12404  33.00   M   19      1       2 12405  21.00   F   25      1       2 12406  69.00   F   17      1       3 12407   6.00   M   13      1       1 12408  45.00   M   13      1       2 12409  33.00   F   13      1       2 12410  27.00   F   13      1       2 12411  81.00   M   39      1       3 12412  27.00   M   13      1       2 12413  76.00   M   35      1       3 12414  68.00   F   15      1       3 12415  37.00   M   13      1       2 12416  38.00   M   13      1       2 12417  65.00   M   13      1       3 12418  39.00   M   17      1       2 12419  40.00   M   13      1       2 12420  71.00   M   17      1       3 12421  40.00   M   17      1       2 12422  21.00   M   15      1       2 12423  21.00   M   15      1       2 12424  22.00   M   16      1       2 12425  28.00   M   15      1       2 12426  15.00   M   16      1       1 12427  27.00   M   15      1       2 12428  27.00   M   15      1       2 12429  33.00   M   16      1       2 12430  28.00   M   32      1       2 12431  30.00   M   16      1       2 12432  21.00   M   15      1       2 12433  52.00   M   16      1       2 12434  31.00   M   16      1       2 12435  33.00   F   16      1       2 12436  38.00   F   16      1       2 12437  37.00   M   16      1       2 12438  30.00   M   21      1       2 12439  18.00   M   19      1       2 12440   8.00   M   19      1       1 12441  30.00   F   14      1       2 12442  30.00   F   14      1       2 12443  80.00   M   12      1       3 12444  24.00   M   15      1       2 12445  36.00   M   17      1       2 12446  34.00   M   32      1       2 12447  33.00   M   16      1       2 12448  40.00   F   16      1       2 12449  50.00   M   16      1       2 12450  22.00   M   21      1       2 12451  28.00   M   21      1       2 12452  39.00   F   22      1       2 12453   9.00   F   24      1       1 12454  42.00   M   16      1       2 12455  15.00   M   16      1       1 12456  46.00   M   16      1       2 12457   6.00   F   18      1       1 12458  28.00   M   16      1       2 12459  40.00   M   23      1       2 12460  50.00   M   20      1       2 12461   1.00   F   15      1       1 12462  34.00   M   23      1       2 12463  65.00   F   14      1       3 12464  46.00   M   17      1       2 12465  33.00   M   14      1       2 12466  27.00   M   15      1       2 12467  25.00   M   14      1       2 12468  20.00   M    9      1       2 12469  50.00   M   15      1       2 12470  20.00   M   15      1       2 12471  27.00   M   15      1       2 12472  24.00   M   18      1       2 12473  23.00   M   15      1       2 12474  24.00   M   15      1       2 12475  26.00   M   15      1       2 12476  33.00   M   16      1       2 12477  17.00   M   15      1       1 12478  18.00   M   31      1       2 12479  19.00   M   15      1       2 12480  63.00   M   20      1       3 12481  21.00   F   18      1       2 12482  33.00   F    9      1       2 12483  15.00   F    9      1       1 12484  14.00   F    9      1       1 12485  55.00   M    9      1       2 12486  30.00   F   14      1       2 12487  50.00   F    9      1       2 12488  60.00   M    9      1       3 12489  10.00   F    9      1       1 12490  38.00   F   15      1       2 12491  42.00   M   15      1       2 12492   4.00   F   15      1       1 12493   4.00   F   12      1       1 12494  18.00   M   20      1       2 12495  31.00   M   13      1       2 12496  35.00   F   17      1       2 12497  27.00   M   20      1       2 12498  48.00   M   14      1       2 12499  33.00   F   14      1       2 12500   9.00   F   14      1       1 12501  11.00   F   14      1       1 12502   8.00   F   14      1       1 12503  28.00   F   14      1       2 12504   8.00   M   14      1       1 12505  58.00   M   14      1       2 12506  48.00   F   16      1       2 12507  32.00   M   14      1       2 12508  75.00   F   16      1       3 12509  44.00   M   14      1       2 12510  52.00   M   14      1       2 12511  35.00   F   14      1       2 12512  65.00   F   14      1       3 12513  60.00   F   14      1       3 12514  28.00   F   14      1       2 12515  32.00   F   14      1       2 12516  39.00   M   14      1       2 12517   1.00   M   14      1       1 12518  37.00   F   12      1       2 12519  33.00   M   19      1       2 12520  22.00   F   19      1       2 12521   3.00   F   19      1       1 12522  35.00   F   13      1       2 12523  39.00   M   16      1       2 12524  16.00   M   16      1       1 12525  53.00   M   14      1       2 12526  24.00   M   14      1       2 12527  51.00   M   14      1       2 12528  49.00   M   23      1       2 12529  35.00   F   22      1       2 12530  55.00   M   13      1       2 12531  10.00   M   13      1       1 12532  55.00   M   13      1       2 12533  36.00   M   13      1       2 12534  50.00   F   13      1       2 12535  13.00   M   13      1       1 12536   7.00   M   25      1       1 12537  40.00   M   13      1       2 12538  55.00   M   13      1       2 12539  30.00   M   11      1       2 12540  22.00   M   11      1       2 12541  34.00   M   11      1       2 12542  28.00   F   16      1       2 12543  25.00   M   16      1       2 12544  28.00   M   19      1       2 12545  24.00   M   12      1       2 12546  43.00   M   16      1       2 12547  68.00   M    9      1       3 12548  17.00   M   18      1       1 12549  40.00   F   11      1       2 12550  34.00   M   11      1       2 12551  29.00   F   11      1       2 12552  36.00   M   14      1       2 12553  37.00   F   14      1       2 12554  25.00   F   14      1       2 12555  32.00   F   14      1       2 12556  20.00   M   14      1       2 12557  44.00   M   14      1       2 12558  26.00   M   13      1       2 12559  26.00   M   16      1       2 12560  23.00   M   13      1       2 12561  28.00   M   13      1       2 12562  24.00   M   16      1       2 12563  25.00   M   13      1       2 12564  23.00   M   13      1       2 12565  27.00   M   13      1       2 12566  19.00   M   13      1       2 12567  25.00   M   13      1       2 12568  21.00   M   13      1       2 12569  32.00   M   13      1       2 12570  30.00   M   13      1       2 12571  19.00   M   13      1       2 12572  21.00   M   13      1       2 12573  24.00   M   14      1       2 12574  20.00   F   12      1       2 12575  25.00   M   12      1       2 12576  19.00   F   15      1       2 12577  45.00   M   24      1       2 12578  30.00   M   10      1       2 12579  33.00   M   20      1       2 12580  58.00   M   10      1       2 12581  32.00   M   10      1       2 12582  12.00   M   10      1       1 12583  10.00   M   19      1       1 12584  20.00   F   30      1       2 12585  24.00   M   10      1       2 12586  24.00   F   19      1       2 12587  25.00   F   10      1       2 12588  25.00   M   10      1       2 12589  30.00   M   10      1       2 12590  15.00   M   10      1       1 12591  30.00   M   10      1       2 12592  22.00   F   12      1       2 12593  24.00   F   12      1       2 12594  29.00   M   12      1       2 12595  27.00   F   12      1       2 12596   0.50   F   12      1       1 12597  27.00   M   15      1       2 12598  35.00   F   15      1       2 12599  28.00   F   15      1       2 12600  40.00   M   15      1       2 12601   8.00   M   19      1       1 12602  24.00   M   19      1       2 12603  10.00   F   19      1       1 12604   2.00   F   11      1       1 12605  20.00   M   13      1       2 12606  43.00   M   20      1       2 12607  14.00   M   13      1       1 12608  16.00   M   15      1       1 12609  22.00   M   29      1       2 12610  25.00   F   15      1       2 12611  28.00   M   15      1       2 12612  50.00   F   15      1       2 12613  10.00   M   15      1       1 12614  42.00   M   11      1       2 12615  31.00   M   15      1       2 12616  24.00   F   15      1       2 12617  35.00   M   15      1       2 12618  26.00   M   15      1       2 12619  33.00   M   15      1       2 12620   4.00   M   15      1       1 12621  40.00   F   23      1       2 12622  45.00   F   37      1       2 12623  46.00   M   13      1       2 12624  23.00   M   15      1       2 12625  23.00   F   15      1       2 12626   8.00   M   21      1       1 12627  30.00   M   15      1       2 12628   8.00   F   15      1       1 12629   3.00   F   15      1       1 12630  37.00   M   15      1       2 12631  30.00   M   32      1       2 12632  55.00   F   32      1       2 12633  28.00   F   13      1       2 12634  39.00   M   13      1       2 12635  32.00   M   13      1       2 12636  11.00   M   13      1       1 12637  30.00   F   13      1       2 12638   8.00   F   13      1       1 12639   4.00   F   12      1       1 12640   6.00   F   30      1       1 12641   4.00   M   12      1       1 12642   5.00   F   12      1       1 12643  25.00   F   12      1       2 12644  35.00   F   10      1       2 12645  27.00   M    9      1       2 12646  58.00   F    9      1       2 12647  22.00   F   10      1       2 12648  48.00   M   11      1       2 12649  14.00   M   11      1       1 12650   5.00   M   11      1       1 12651  30.00   M   11      1       2 12652  31.00   M   11      1       2 12653   2.00   M    9      1       1 12654   7.00   M   29      1       1 12655  42.00   M   11      1       2 12656  18.00   F   11      1       2 12657   8.00   F   11      1       1 12658  35.00   F   11      1       2 12659  32.00   M   11      1       2 12660  21.00   F   11      1       2 12661  40.00   M   11      1       2 12662  30.00   F   11      1       2 12663  30.00   F   11      1       2 12664  32.00   M   11      1       2 12665  54.00   F   11      1       2 12666  78.00   M   12      1       3 12667   1.00   M   16      1       1 12668  16.00   F   16      1       1 12669  24.00   F   16      1       2 12670  60.00   F   16      1       3 12671  40.00   F   16      1       2 12672  17.00   M   16      1       1 12673  20.00   F   16      1       2 12674  50.00   M   16      1       2 12675  52.00   F   16      1       2 12676   9.00   M   16      1       1 12677  33.00   F   16      1       2 12678  18.00   M   16      1       2 12679  65.00   M   19      1       3 12680  34.00   M   16      1       2 12681  25.00   M   16      1       2 12682  24.00   M   12      1       2 12683  24.00   F   12      1       2 12684   8.00   M   12      1       1 12685  38.00   M   12      1       2 12686  23.00   F   10      1       2 12687  36.00   F   15      1       2 12688  25.00   F   15      1       2 12689  43.00   M    8      1       2 12690  27.00   F   10      1       2 12691  34.00   F   12      1       2 12692  13.00   M   12      1       1 12693  20.00   M   12      1       2 12694  29.00   M   12      1       2 12695  39.00   F   12      1       2 12696  12.00   M   15      1       1 12697  15.00   F   12      1       1 12698  63.00   M   12      1       3 12699  20.00   F   13      1       2 12700  49.00   F   19      1       2 12701  28.00   M   12      1       2 12702  40.00   F   11      1       2 12703  17.00   F   17      1       1 12704  13.00   M   18      1       1 12705  38.00   F   17      1       2 12706  24.00   M   18      1       2 12707  16.00   F   18      1       1 12708  38.00   M   16      1       2 12709  30.00   F   16      1       2 12710  20.00   M   16      1       2 12711  45.00   M   16      1       2 12712  12.00   M   16      1       1 12713  39.00   F   19      1       2 12714  18.00   F   16      1       2 12715  19.00   F   16      1       2 12716   1.00   F   16      1       1 12717  50.00   F   16      1       2 12718  52.00   M   19      1       2 12719  45.00   F   16      1       2 12720  52.00   M   16      1       2 12721  44.00   M   16      1       2 12722  12.00   F   16      1       1 12723  32.00   F   19      1       2 12724  12.00   F   19      1       1 12725  34.00   M   16      1       2 12726  10.00   M   16      1       1 12727  28.00   F   16      1       2 12728  41.00   M   19      1       2 12729  10.00   M   16      1       1 12730  35.00   F   16      1       2 12731  47.00   F   19      1       2 12732  40.00   M   19      1       2 12733  16.00   F   16      1       1 12734  34.00   F   19      1       2 12735  37.00   M   28      1       2 12736  32.00   M   16      1       2 12737   8.00   F   19      1       1 12738  40.00   M   19      1       2 12739  38.00   M   16      1       2 12740  31.00   F   16      1       2 12741  37.00   M   16      1       2 12742  40.00   F   19      1       2 12743  33.00   F   16      1       2 12744  15.00   F   43      1       1 12745  47.00   M   16      1       2 12746  35.00   M   16      1       2 12747  54.00   M   31      1       2 12748  46.00   M   19      1       2 12749  33.00   F   16      1       2 12750   5.00   M   20      1       1 12751  26.00   M   16      1       2 12752  15.00   M   16      1       1 12753  27.00   F   16      1       2 12754  35.00   M   16      1       2 12755  31.00   F   18      1       2 12756  34.00   F   18      1       2 12757  19.00   M   11      1       2 12758  18.00   M   11      1       2 12759  25.00   M   11      1       2 12760  30.00   F   11      1       2 12761  20.00   M   11      1       2 12762  11.00   M   11      1       1 12763  13.00   F   11      1       1 12764  35.00   F   11      1       2 12765  70.00   M   11      1       3 12766  29.00   F   13      1       2 12767   6.00   F   11      1       1 12768  35.00   F   11      1       2 12769  69.00   M   10      1       3 12770  36.00   F   16      1       2 12771  30.00   M   23      1       2 12772  26.00   M   16      1       2 12773  13.00   F   16      1       1 12774  48.00   M   16      1       2 12775  20.00   M   16      1       2 12776  17.00   F   19      1       1 12777  37.00   F   16      1       2 12778  21.00   M   16      1       2 12779   7.00   M   12      1       1 12780  17.00   F   16      1       1 12781  10.00   M   13      1       1 12782  38.00   M   15      1       2 12783  11.00   F   18      1       1 12784  40.00   M   18      1       2 12785  23.00   M   18      1       2 12786  43.00   M   19      1       2 12787  30.00   M   11      1       2 12788  28.00   M   11      1       2 12789  30.00   M    9      1       2 12790  50.00   M   25      1       2 12791  36.00   F   16      1       2 12792  28.00   F   17      1       2 12793  30.00   F   19      1       2 12794  10.00   M   17      1       1 12795  12.00   F   17      1       1 12796  31.00   F   11      1       2 12797  40.00   M   11      1       2 12798  10.00   F   16      1       1 12799  39.00   M   16      1       2 12800  14.00   F   16      1       1 12801  44.00   M   19      1       2 12802  66.00   F   16      1       3 12803  27.00   F   21      1       2 12804  46.00   M   17      1       2 12805  35.00   F    9      1       2 12806  42.00   M    9      1       2 12807  36.00   M   14      1       2 12808  24.00   F   14      1       2 12809  22.00   M    9      1       2 12810  64.00   M   14      1       3 12811  40.00   M   20      1       2 12812  45.00   F   16      1       2 12813  31.00   F   16      1       2 12814  22.00   M   10      1       2 12815  35.00   M   10      1       2 12816  25.00   M   10      1       2 12817  22.00   F   10      1       2 12818  46.00   M   10      1       2 12819  26.00   M   10      1       2 12820  50.00   M   10      1       2 12821  24.00   F   12      1       2 12822  38.00   F   20      1       2 12823  55.00   M   11      1       2 12824   4.00   F   11      1       1 12825  15.00   F   11      1       1 12826  74.00   M   19      1       3 12827  31.00   F   11      1       2 12828  40.00   F   14      1       2 12829  13.00   M    9      1       1 12830  37.00   M   16      1       2 12831  38.00   M    9      1       2 12832  16.00   M   16      1       1 12833  13.00   M    9      1       1 12834  35.00   F    9      1       2 12835  28.00   F   16      1       2 12836  43.00   M   19      1       2 12837   7.00   F   17      1       1 12838  55.00   F   12      1       2 12839  47.00   F   11      1       2 12840   6.00   F   16      1       1 12841  12.00   F   12      1       1 12842  18.00   M   12      1       2 12843  56.00   M   12      1       2 12844  15.00   M   21      1       1 12845  45.00   M   12      1       2 12846  72.00   M   12      1       3 12847   9.00   F   29      1       1 12848  40.00   F   12      1       2 12849  26.00   F   24      1       2 12850  40.00   F   32      1       2 12851  12.00   F   30      1       1 12852  30.00   F   30      1       2 12853  40.00   F   10      1       2 12854  11.00   M   13      1       1 12855   7.00   F    8      1       1 12856  36.00   M   16      1       2 12857  27.00   M   16      1       2 12858  50.00   F   16      1       2 12859  23.00   M   16      1       2 12860  34.00   M   15      1       2 12861  37.00   M   12      1       2 12862  36.00   F   19      1       2 12863  16.00   M   16      1       1 12864   7.00   F   15      1       1 12865  60.00   M   33      1       3 12866  42.00   M   13      1       2 12867  44.00   M   26      1       2 12868  44.00   M   11      1       2 12869  35.00   M   29      1       2 12870  29.00   M   29      1       2 12871  47.00   M   15      1       2 12872  15.00   M   10      1       1 12873  40.00   F   15      1       2 12874  68.00   F   19      1       3 12875  22.00   M   16      1       2 12876  27.00   M   16      1       2 12877   6.00   F   10      1       1 12878  65.00   F   12      1       3 12879  63.00   F   12      1       3 12880  24.00   M   12      1       2 12881  25.00   M   12      1       2 12882  75.00   M   12      1       3 12883  30.00   M    7      1       2 12884  60.00   M   24      1       3 12885   7.00   F   12      1       1 12886  27.00   M   26      1       2 12887  60.00   F   10      1       3 12888  21.00   F   22      1       2 12889   3.00   M   25      1       1 12890   3.00   F   22      1       1 12891  56.00   M   10      1       2 12892  52.00   M   22      1       2 12893  29.00   F   34      1       2 12894   6.00   F   14      1       1 12895  29.00   F   14      1       2 12896  35.00   M   13      1       2 12897  18.00   M    7      1       2 12898  30.00   M   17      1       2 12899   2.00   M   10      1       1 12900   9.00   M   10      1       1 12901  32.00   F   10      1       2 12902  51.00   M   16      1       2 12903  33.00   M   10      1       2 12904   4.00   F   10      1       1 12905   6.00   M   10      1       1 12906   6.00   M   10      1       1 12907   4.00   M   10      1       1 12908   6.00   M   10      1       1 12909   7.00   F   10      1       1 12910  32.00   M   10      1       2 12911  51.00   F   10      1       2 12912  10.00   M   10      1       1 12913   6.00   M   10      1       1 12914  33.00   M   10      1       2 12915   3.00   F   10      1       1 12916   5.00   F   10      1       1 12917  35.00   F   17      1       2 12918   1.00   M   17      1       1 12919  26.00   F   10      1       2 12920  24.00   M   10      1       2 12921  21.00   M   10      1       2 12922  60.00   F   26      1       3 12923   9.00   M   21      1       1 12924   1.00   F   10      1       1 12925   2.00   M   10      1       1 12926   6.00   F   17      1       1 12927  38.00   F   20      1       2 12928   7.00   F   17      1       1 12929  14.00   M   13      1       1 12930  41.00   M   12      1       2 12931  33.00   M   13      1       2 12932  24.00   M   12      1       2 12933  20.00   F   12      1       2 12934  31.00   F   12      1       2 12935  31.00   F   12      1       2 12936  19.00   F   13      1       2 12937  15.00   F   10      1       1 12938  40.00   M   10      1       2 12939  17.00   F   10      1       1 12940  14.00   M   26      1       1 12941  35.00   F   16      1       2 12942  42.00   M   17      1       2 12943  35.00   F   16      1       2 12944  15.00   M   16      1       1 12945  33.00   F   17      1       2 12946  36.00   M   20      1       2 12947  42.00   M   17      1       2 12948  58.00   M   14      1       2 12949  43.00   F   12      1       2 12950  36.00   F   12      1       2 12951  48.00   F   13      1       2 12952  29.00   F   12      1       2 12953  32.00   M   15      1       2 12954  38.00   M    9      1       2 12955  39.00   M    8      1       2 12956  17.00   M    8      1       1 12957  18.00   F    8      1       2 12958  33.00   M    8      1       2 12959   4.00   M   13      1       1 12960  28.00   F   15      1       2 12961  41.00   M   15      1       2 12962  28.00   M   18      1       2 12963  23.00   M   15      1       2 12964   8.00   M   13      1       1 12965  52.00   M   15      1       2 12966  33.00   M   23      1       2 12967  12.00   M   11      1       1 12968  14.00   M   29      1       1 12969  32.00   F   35      1       2 12970  13.00   F   11      1       1 12971  47.00   F   16      1       2 12972  28.00   M   16      1       2 12973  32.00   M   20      1       2 12974  42.00   M   12      1       2 12975  37.00   F   19      1       2 12976  42.00   M   16      1       2 12977  32.00   F   16      1       2 12978  13.00   M   16      1       1 12979  11.00   M   16      1       1 12980  21.00   F   16      1       2 12981  56.00   F   16      1       2 12982  52.00   M   16      1       2 12983  16.00   M   16      1       1 12984  47.00   M   15      1       2 12985  35.00   M   29      1       2 12986  32.00   F   16      1       2 12987  12.00   M   16      1       1 12988  10.00   F   16      1       1 12989  29.00   M   19      1       2 12990  41.00   M   16      1       2 12991  36.00   M   11      1       2 12992  37.00   M   18      1       2 12993  22.00   F    9      1       2 12994  35.00   F    9      1       2 12995  45.00   F    9      1       2 12996  22.00   M   11      1       2 12997  23.00   M   11      1       2 12998  29.00   F   15      1       2 12999  39.00   M   17      1       2 13000  10.00   M   15      1       1 13001  21.00   M   17      1       2 13002   8.00   M   15      1       1 13003  17.00   F   20      1       1 13004  46.00   F   15      1       2 13005  45.00   M   18      1       2 13006  45.00   M    9      1       2 13007  14.00   M   19      1       1 13008   3.00   M   17      1       1 13009  14.00   M   17      1       1 13010  60.00   M   13      1       3 13011  50.00   M   12      1       2 13012   7.00   M    9      1       1 13013  49.00   M   10      1       2 13014  46.00   M   10      1       2 13015  48.00   F   10      1       2 13016  14.00   F   13      1       1 13017  25.00   M   14      1       2 13018  12.00   M   15      1       1 13019  32.00   F    9      1       2 13020  33.00   M   18      1       2 13021  32.00   M   14      1       2 13022  23.00   M   25      1       2 13023  12.00   M    9      1       1 13024  35.00   F   12      1       2 13025  35.00   M   15      1       2 13026  35.00   M    9      1       2 13027  25.00   F   18      1       2 13028  51.00   M    9      1       2 13029  17.00   F   12      1       1 13030  17.00   M   18      1       1 13031  35.00   M   25      1       2 13032  18.00   M   15      1       2 13033   1.00   F   21      1       1 13034  35.00   F   12      1       2 13035  53.00   F    9      1       2 13036  35.00   F   12      1       2 13037  14.00   F    9      1       1 13038  45.00   M   15      1       2 13039  31.00   F   20      1       2 13040  17.00   F    9      1       1 13041  55.00   M   20      1       2 13042  20.00   M   12      1       2 13043  42.00   F    9      1       2 13044  14.00   F    9      1       1 13045  18.00   M    9      1       2 13046  36.00   F   14      1       2 13047  34.00   M   14      1       2 13048  33.00   M   11      1       2 13049  60.00   M   18      1       3 13050  65.00   M   19      1       3 13051  23.00   F   18      1       2 13052   2.00   M   15      1       1 13053  32.00   M   10      1       2 13054  28.00   M   10      1       2 13055  42.00   M    9      1       2 13056  52.00   M    9      1       2 13057  34.00   M   21      1       2 13058  30.00   M   18      1       2 13059  22.00   F    9      1       2 13060  34.00   F   25      1       2 13061  15.00   M   15      1       1 13062  32.00   M   25      1       2 13063   7.00   M   15      1       1 13064  18.00   M    9      1       2 13065  34.00   F   18      1       2 13066  22.00   F   18      1       2 13067  15.00   M   15      1       1 13068  19.00   F   12      1       2 13069  66.00   M   15      1       3 13070  75.00   M   10      1       3 13071  24.00   F    9      1       2 13072  55.00   F   11      1       2 13073  28.00   F   11      1       2 13074  22.00   M   11      1       2 13075  36.00   M    9      1       2 13076  44.00   M   16      1       2 13077  29.00   F   28      1       2 13078  42.00   F   12      1       2 13079  33.00   F   34      1       2 13080  29.00   F   14      1       2 13081  35.00   M   14      1       2 13082  40.00   F    7      1       2 13083  45.00   M    7      1       2 13084  30.00   M    7      1       2 13085  17.00   M    7      1       1 13086  40.00   F   14      1       2 13087  47.00   M    7      1       2 13088  45.00   M   18      1       2 13089  40.00   M   14      1       2 13090  32.00   F   14      1       2 13091  38.00   M   14      1       2 13092  40.00   M   14      1       2 13093  33.00   F   20      1       2 13094  25.00   F   15      1       2 13095  27.00   F   10      1       2 13096   4.00   M   10      1       1 13097  31.00   M   10      1       2 13098  20.00   M   28      1       2 13099  25.00   F   32      1       2 13100   1.00   M   32      1       1 13101  13.00   F   16      1       1 13102  17.00   F   32      1       1 13103  45.00   M   10      1       2 13104  35.00   F   10      1       2 13105  60.00   F   18      1       3 13106  20.00   F   12      1       2 13107  30.00   M   20      1       2 13108  15.00   F   20      1       1 13109  36.00   F   15      1       2 13110  29.00   F    6      1       2 13111  20.00   M    6      1       2 13112  39.00   M   10      1       2 13113  18.00   M   10      1       2 13114   5.00   F   10      1       1 13115  42.00   F    9      1       2 13116  46.00   M   17      1       2 13117  50.00   M   17      1       2 13118  49.00   F   17      1       2 13119  20.00   F   17      1       2 13120  14.00   F   25      1       1 13121  31.00   M   19      1       2 13122  55.00   M    9      1       2 13123  48.00   F   17      1       2 13124   9.00   M   10      1       1 13125  25.00   M   15      1       2 13126  17.00   M    8      1       1 13127   7.00   F   15      1       1 13128  20.00   M   15      1       2 13129  50.00   F   18      1       2 13130  32.00   F   14      1       2 13131   8.00   M   14      1       1 13132  10.00   F   14      1       1 13133  28.00   F   14      1       2 13134  11.00   M   14      1       1 13135  30.00   F   14      1       2 13136   6.00   F    9      1       1 13137   1.00   F   28      1       1 13138  30.00   F    9      1       2 13139  30.00   M   16      1       2 13140  23.00   M   16      1       2 13141  30.00   M   16      1       2 13142   8.00   F    9      1       1 13143  17.00   F   15      1       1 13144  12.00   M    8      1       1 13145  38.00   M   11      1       2 13146  53.00   M   17      1       2 13147  34.00   M   17      1       2 13148  49.00   F   11      1       2 13149  26.00   M    7      1       2 13150  14.00   M    9      1       1 13151  38.00   M   31      1       2 13152  22.00   F   21      1       2 13153   8.00   F   33      1       1 13154  40.00   M    9      1       2 13155  55.00   M    9      1       2 13156  29.00   M    9      1       2 13157  40.00   M    9      1       2 13158  35.00   M    9      1       2 13159  54.00   F   24      1       2 13160  29.00   M    9      1       2 13161  65.00   F    9      1       3 13162  35.00   F    9      1       2 13163   7.00   M    6      1       1 13164  45.00   F    6      1       2 13165  54.00   M    6      1       2 13166  33.00   F    6      1       2 13167   0.30   M   24      1       1 13168  15.00   M   11      1       1 13169  16.00   M    8      1       1 13170  31.00   F   15      1       2 13171  41.00   M   15      1       2 13172  39.00   M   15      1       2 13173  39.00   F   15      1       2 13174  28.00   M   15      1       2 13175  15.00   F   15      1       1 13176  35.00   F   19      1       2 13177  11.00   M   15      1       1 13178  11.00   M   15      1       1 13179   6.00   M   15      1       1 13180  33.00   F   15      1       2 13181  17.00   F   15      1       1 13182  42.00   M    8      1       2 13183  69.00   F    8      1       3 13184  41.00   F    9      1       2 13185  30.00   M   14      1       2 13186  60.00   F   14      1       3 13187  13.00   M   10      1       1 13188  27.00   F    9      1       2 13189  34.00   M    9      1       2 13190  35.00   F   33      1       2 13191  13.00   F   24      1       1 13192  30.00   M    9      1       2 13193   9.00   M    9      1       1 13194  46.00   M    9      1       2 13195  45.00   M   27      1       2 13196   1.00   F    9      1       1 13197  48.00   M    9      1       2 13198  35.00   F    9      1       2 13199  33.00   F   27      1       2 13200  10.00   M   21      1       1 13201  70.00   F    9      1       3 13202   8.00   F    9      1       1 13203   6.00   F   24      1       1 13204   3.00   F    9      1       1 13205  30.00   M   33      1       2 13206  22.00   F   33      1       2 13207  24.00   M   15      1       2 13208  36.00   M   21      1       2 13209   2.00   F    9      1       1 13210  33.00   F   16      1       2 13211  46.00   M    9      1       2 13212  52.00   F    9      1       2 13213   4.00   M   14      1       1 13214   5.00   F    8      1       1 13215  35.00   M   34      1       2 13216  34.00   M   11      1       2 13217   7.00   M   19      1       1 13218  20.00   M   19      1       2 13219  27.00   M   17      1       2 13220  21.00   M    8      1       2 13221  27.00   M   11      1       2 13222  30.00   M   23      1       2 13223  40.00   M   14      1       2 13224  30.00   F    8      1       2 13225  45.00   M    8      1       2 13226  20.00   F   35      1       2 13227  35.00   F   27      1       2 13228   2.00   M    9      1       1 13229  45.00   M   33      1       2 13230  40.00   F   16      1       2 13231  25.00   M   16      1       2 13232  18.00   F   27      1       2 13233  18.00   F   16      1       2 13234  18.00   M   16      1       2 13235  17.00   F   29      1       1 13236  14.00   M   14      1       1 13237  40.00   F   33      1       2 13238  23.00   M   22      1       2 13239  48.00   F   14      1       2 13240  25.00   M   14      1       2 13241  31.00   M   14      1       2 13242  27.00   M   14      1       2 13243   7.00   F   14      1       1 13244  28.00   M   14      1       2 13245  26.00   F   14      1       2 13246  47.00   M   14      1       2 13247   2.00   F    9      1       1 13248  30.00   M   35      1       2 13249  24.00   M   18      1       2 13250  21.00   F   20      1       2 13251  28.00   M   16      1       2 13252  30.00   F   16      1       2 13253   8.00   F    9      1       1 13254   6.00   F    9      1       1 13255  23.00   M   35      1       2 13256  20.00   M   16      1       2 13257  21.00   F   16      1       2 13258  10.00   F    9      1       1 13259  24.00   M   27      1       2 13260  19.00   M   16      1       2 13261  42.00   M   16      1       2 13262  17.00   M   16      1       1 13263  15.00   M    9      1       1 13264  26.00   M   30      1       2 13265  18.00   M   16      1       2 13266  20.00   F   16      1       2 13267  52.00   M   20      1       2 13268  48.00   F   16      1       2 13269  22.00   F   16      1       2 13270  21.00   M   16      1       2 13271  35.00   F   16      1       2 13272  17.00   M   16      1       1 13273  55.00   M   14      1       2 13274  48.00   F    9      1       2 13275  32.00   M   16      1       2 13276   9.00   F    9      1       1 13277   7.00   M    9      1       1 13278  38.00   M   37      1       2 13279  31.00   M   16      1       2 13280  21.00   F   33      1       2 13281  32.00   F   18      1       2 13282  17.00   F   14      1       1 13283  15.00   F    9      1       1 13284  34.00   F   15      1       2 13285  23.00   M   15      1       2 13286  51.00   M   16      1       2 13287  20.00   F   15      1       2 13288   2.00   M   15      1       1 13289  45.00   M   15      1       2 13290  30.00   M   15      1       2 13291  58.00   F   15      1       2 13292  15.00   F   15      1       1 13293  45.00   M   15      1       2 13294  37.00   F   15      1       2 13295  13.00   M   15      1       1 13296  43.00   M   15      1       2 13297  20.00   F   15      1       2 13298  36.00   M   15      1       2 13299  16.00   F   19      1       1 13300  33.00   M   15      1       2 13301  23.00   F   15      1       2 13302  35.00   M   15      1       2 13303  57.00   M   22      1       2 13304  34.00   F    8      1       2 13305  50.00   F   13      1       2 13306  20.00   F   13      1       2 13307  22.00   F   13      1       2 13308  18.00   M   13      1       2 13309  18.00   F   13      1       2 13310   8.00   F   13      1       1 13311  21.00   M   13      1       2 13312  17.00   M    9      1       1 13313  40.00   M   21      1       2 13314  49.00   M    8      1       2 13315  29.00   F   14      1       2 13316   2.00   M   14      1       1 13317  49.00   F   18      1       2 13318  31.00   M   10      1       2 13319  11.00   M   10      1       1 13320  50.00   F   21      1       2 13321  23.00   M   10      1       2 13322  59.00   F    9      1       2 13323  25.00   F    9      1       2 13324  35.00   F   11      1       2 13325  35.00   F   11      1       2 13326  18.00   M    8      1       2 13327  28.00   M    8      1       2 13328  55.00   F   15      1       2 13329  32.00   M   10      1       2 13330  35.00   F   17      1       2 13331  60.00   F   10      1       3 13332  33.00   F    7      1       2 13333  33.00   F    7      1       2 13334  32.00   M    6      1       2 13335  20.00   M    6      1       2 13336  48.00   M    6      1       2 13337  50.00   F    6      1       2 13338  22.00   M    9      1       2 13339  50.00   M   15      1       2 13340   4.00   F   13      1       1 13341  17.00   F   19      1       1 13342   8.00   M   27      1       1 13343  25.00   M   17      1       2 13344  27.00   F   13      1       2 13345  43.00   M   18      1       2 13346  42.00   M   23      1       2 13347  15.00   F   16      1       1 13348  45.00   F   16      1       2 13349  37.00   F   13      1       2 13350  40.00   M   13      1       2 13351  14.00   M   16      1       1 13352  35.00   F    7      1       2 13353  11.00   F   16      1       1 13354  50.00   M   16      1       2 13355  70.00   F    7      1       3 13356  27.00   M   15      1       2 13357  24.00   M   13      1       2 13358  52.00   F   12      1       2 13359  24.00   F   12      1       2 13360  38.00   F   12      1       2 13361  38.00   M   10      1       2 13362  23.00   M   13      1       2 13363  45.00   M   13      1       2 13364  30.00   M   13      1       2 13365  24.00   F   20      1       2 13366  26.00   M   31      1       2 13367  31.00   M   20      1       2 13368  42.00   M   23      1       2 13369  21.00   F   16      1       2 13370  24.00   F   16      1       2 13371  42.00   F   16      1       2 13372  40.00   F   16      1       2 13373  22.00   M   26      1       2 13374  40.00   M   16      1       2 13375  18.00   F   16      1       2 13376   6.00   M   16      1       1 13377   6.00   M   16      1       1 13378  39.00   F   26      1       2 13379  32.00   M   16      1       2 13380  44.00   M   23      1       2 13381  38.00   M   23      1       2 13382   7.00   F   16      1       1 13383  20.00   F   14      1       2 13384  24.00   M   14      1       2 13385  42.00   M    8      1       2 13386  34.00   M   13      1       2 13387  35.00   F   10      1       2 13388  55.00   M   36      1       2 13389  16.00   M   15      1       1 13390  18.00   M    5      1       2 13391  23.00   M   13      1       2 13392  58.00   M   10      1       2 13393  26.00   F   18      1       2 13394  32.00   F   23      1       2 13395  22.00   M   13      1       2 13396  33.00   M   13      1       2 13397  20.00   M   13      1       2 13398  36.00   M   16      1       2 13399  32.00   F   13      1       2 13400  51.00   M    7      1       2 13401  41.00   M    7      1       2 13402  32.00   M   17      1       2 13403   4.00   M    7      1       1 13404  50.00   M   17      1       2 13405  22.00   F    5      1       2 13406  37.00   M   17      1       2 13407  26.00   M   17      1       2 13408  24.00   F    7      1       2 13409  35.00   F   11      1       2 13410   1.00   M   11      1       1 13411  29.00   M    7      1       2 13412   4.00   M   11      1       1 13413  29.00   F   21      1       2 13414  48.00   M   17      1       2 13415  23.00   F   11      1       2 13416  29.00   F   11      1       2 13417  30.00   M   24      1       2 13418  44.00   F    7      1       2 13419  54.00   M   13      1       2 13420   1.50   F   13      1       1 13421   7.00   M   13      1       1 13422   9.00   M   13      1       1 13423  25.00   M   13      1       2 13424  21.00   M   13      1       2 13425   2.00   M   15      1       1 13426  45.00   M   15      1       2 13427  26.00   M   21      1       2 13428  23.00   F    7      1       2 13429  22.00   F   17      1       2 13430  25.00   F   17      1       2 13431  21.00   F   13      1       2 13432  21.00   F   19      1       2 13433  70.00   F   13      1       3 13434  37.00   F   19      1       2 13435  21.00   F   19      1       2 13436  18.00   F   13      1       2 13437  31.00   M   13      1       2 13438   3.00   F   13      1       1 13439  14.00   M   13      1       1 13440   9.00   F   13      1       1 13441  26.00   M   19      1       2 13442  61.00   F   13      1       3 13443   1.00   F   13      1       1 13444  31.00   M   21      1       2 13445  21.00   M   21      1       2 13446  31.00   M   10      1       2 13447  74.00   M   22      1       3 13448  25.00   M   19      1       2 13449  30.00   F   13      1       2 13450   9.00   F   10      1       1 13451  34.00   M   16      1       2 13452  27.00   M   20      1       2 13453  54.00   F   20      1       2 13454   3.00   F   23      1       1 13455  30.00   F   23      1       2 13456   5.00   M   10      1       1 13457   4.00   M   10      1       1 13458  65.00   M   25      1       3 13459  10.00   M   25      1       1 13460  43.00   M   12      1       2 13461   6.00   F    6      1       1 13462  72.00   F   10      1       3 13463   9.00   F   14      1       1 13464  33.00   F   10      1       2 13465  11.00   F   16      1       1 13466   6.00   F   14      1       1 13467  67.00   F   16      1       3 13468  30.00   M   31      1       2 13469  25.00   M   10      1       2 13470  55.00   M   25      1       2 13471   3.00   F   12      1       1 13472  33.00   M   18      1       2 13473  25.00   M   18      1       2 13474  17.00   F   18      1       1 13475  22.00   F   18      1       2 13476   5.00   M   18      1       1 13477  40.00   M   25      1       2 13478  20.00   F   23      1       2 13479   3.00   M   33      1       1 13480  22.00   F   35      1       2 13481  25.00   F   18      1       2 13482  30.00   M   18      1       2 13483  22.00   F   12      1       2 13484  22.00   F   23      1       2 13485  20.00   F   22      1       2 13486  22.00   M   18      1       2 13487  48.00   M   12      1       2 13488  53.00   F    7      1       2 13489  25.00   M    7      1       2 13490  47.00   F   31      1       2 13491  21.00   F   16      1       2 13492   2.00   M   12      1       1 13493  34.00   M   10      1       2 13494  28.00   F    6      1       2 13495  60.00   M   12      1       3 13496  14.00   M   15      1       1 13497  42.00   M   12      1       2 13498  18.00   M   12      1       2 13499  30.00   M   12      1       2 13500  10.00   M   18      1       1 13501  55.00   F   12      1       2 13502  45.00   M   12      1       2 13503  18.00   M   12      1       2 13504  40.00   M   12      1       2 13505  15.00   F   12      1       1 13506  26.00   M   18      1       2 13507  29.00   F   18      1       2 13508  68.00   M   18      1       3 13509  35.00   F   12      1       2 13510  29.00   M   12      1       2 13511  40.00   F   19      1       2 13512  48.00   M   12      1       2 13513  23.00   M    9      1       2 13514  33.00   F   12      1       2 13515  17.00   M   12      1       1 13516  60.00   M   20      1       3 13517  20.00   F    6      1       2 13518  11.00   F    6      1       1 13519  58.00   F   24      1       2 13520  46.00   M   16      1       2 13521   1.00   F   16      1       1 13522  33.00   M   20      1       2 13523  38.00   M    6      1       2 13524  30.00   M    4      1       2 13525  65.00   F   14      1       3 13526  45.00   M   12      1       2 13527  26.00   F   14      1       2 13528  40.00   M    6      1       2 13529  46.00   M   23      1       2 13530  44.00   M    6      1       2 13531  57.00   M    5      1       2 13532  50.00   M   13      1       2 13533  48.00   M    5      1       2 13534  36.00   M   23      1       2 13535  21.00   F   12      1       2 13536  33.00   M   16      1       2 13537  47.00   M   17      1       2 13538  28.00   M   11      1       2 13539   9.00   M   11      1       1 13540  20.00   F   19      1       2 13541  25.00   M   19      1       2 13542  22.00   F   19      1       2 13543  25.00   M   25      1       2 13544  34.00   M   19      1       2 13545  15.00   M   19      1       1 13546  25.00   M   19      1       2 13547  37.00   M   37      1       2 13548  27.00   M    6      1       2 13549  30.00   M   21      1       2 13550  30.00   M    6      1       2 13551   6.00   M   19      1       1 13552  25.00   F   19      1       2 13553  19.00   M   19      1       2 13554  18.00   M   19      1       2 13555  45.00   M   20      1       2 13556  18.00   M   11      1       2 13557  34.00   M   24      1       2 13558  30.00   M    7      1       2 13559  60.00   M   13      1       3 13560  17.00   M   11      1       1 13561  47.00   F   11      1       2 13562  55.00   F   11      1       2 13563  28.00   M   15      1       2 13564  58.00   M   11      1       2 13565  43.00   M   11      1       2 13566  23.00   M   11      1       2 13567  42.00   F   11      1       2 13568  34.00   M   11      1       2 13569  43.00   M   14      1       2 13570  37.00   F   11      1       2 13571  16.00   M   14      1       1 13572  53.00   M   13      1       2 13573  20.00   F   14      1       2 13574  52.00   M   14      1       2 13575  14.00   M   14      1       1 13576  35.00   F   19      1       2 13577  40.00   F   14      1       2 13578  10.00   M   14      1       1 13579  52.00   M   14      1       2 13580  17.00   F   19      1       1 13581  45.00   M   16      1       2 13582  35.00   F   20      1       2 13583  27.00   M   11      1       2 13584  30.00   M   23      1       2 13585  30.00   F   21      1       2 13586  21.00   M   17      1       2 13587  33.00   M   11      1       2 13588  25.00   M   11      1       2 13589  46.00   M   17      1       2 13590  17.00   M   11      1       1 13591  23.00   M   17      1       2 13592  25.00   M   17      1       2 13593  31.00   M   17      1       2 13594  26.00   M   17      1       2 13595  30.00   M   11      1       2 13596  36.00   M   17      1       2 13597  20.00   M   17      1       2 13598  48.00   M   17      1       2 13599  27.00   M   11      1       2 13600  15.00   M   17      1       1 13601  30.00   M   17      1       2 13602  17.00   M   11      1       1 13603  20.00   M   11      1       2 13604  22.00   M   11      1       2 13605  31.00   M   17      1       2 13606  27.00   M   14      1       2 13607  35.00   M    9      1       2 13608   9.00   F   11      1       1 13609  65.00   F   11      1       3 13610  35.00   M    8      1       2 13611  27.00   F    8      1       2 13612  32.00   M    8      1       2 13613  56.00   M    8      1       2 13614  39.00   F    8      1       2 13615  43.00   M   17      1       2 13616  38.00   F    8      1       2 13617  38.00   F   17      1       2 13618  27.00   F    8      1       2 13619  39.00   M   17      1       2 13620  17.00   F   30      1       1 13621  38.00   M   17      1       2 13622  28.00   F   17      1       2 13623  54.00   M   11      1       2 13624  55.00   F    5      1       2 13625  38.00   M    5      1       2 13626   9.00   M    8      1       1 13627  36.00   F    5      1       2 13628  14.00   M    5      1       1 13629  63.00   F    8      1       3 13630  39.00   M    8      1       2 13631   9.00   M    5      1       1 13632  26.00   F    8      1       2 13633  12.00   M   16      1       1 13634  12.00   F   11      1       1 13635  35.00   M   23      1       2 13636  36.00   F    5      1       2 13637  46.00   M   13      1       2 13638  11.00   F   23      1       1 13639  59.00   F   16      1       2 13640   3.00   F   23      1       1 13641  26.00   M   11      1       2 13642  30.00   M   16      1       2 13643  36.00   F   14      1       2 13644  27.00   F   10      1       2 13645   6.00   F   10      1       1 13646   4.00   M   10      1       1 13647  13.00   M   16      1       1 13648  25.00   F   14      1       2 13649  39.00   M   14      1       2 13650   2.00   F   10      1       1 13651   3.00   M   10      1       1 13652  33.00   M   10      1       2 13653   1.00   F   10      1       1 13654   7.00   F   10      1       1 13655  26.00   F   16      1       2 13656  50.00   M   31      1       2 13657  45.00   M    8      1       2 13658  31.00   M   14      1       2 13659  25.00   F   10      1       2 13660  12.00   M   13      1       1 13661  31.00   F   13      1       2 13662  28.00   M   13      1       2 13663  35.00   M   10      1       2 13664  43.00   M   10      1       2 13665  34.00   M   13      1       2 13666  28.00   F   10      1       2 13667  25.00   M   10      1       2 13668  21.00   M   14      1       2 13669  20.00   M   10      1       2 13670  25.00   M   17      1       2 13671  30.00   M   29      1       2 13672  20.00   F   23      1       2 13673  60.00   F   29      1       3 13674  12.00   F   12      1       1 13675  37.00   F   29      1       2 13676  45.00   F    8      1       2 13677  22.00   M    8      1       2 13678  39.00   M    8      1       2 13679  45.00   M    7      1       2 13680  27.00   M   36      1       2 13681  57.00   M   10      1       2 13682  60.00   M   18      1       3 13683  25.00   F    7      1       2 13684  32.00   M   20      1       2 13685  40.00   M    7      1       2 13686  22.00   M   10      1       2 13687  49.00   M   10      1       2 13688  55.00   F   21      1       2 13689  21.00   M   14      1       2 13690  29.00   M    7      1       2 13691  32.00   M    8      1       2 13692  38.00   M   10      1       2 13693  17.00   F   10      1       1 13694  13.00   M   10      1       1 13695  18.00   F   10      1       2 13696  50.00   M   10      1       2 13697  40.00   M   10      1       2 13698  35.00   F   10      1       2 13699  17.00   M   10      1       1 13700  10.00   M   10      1       1 13701  34.00   M   16      1       2 13702  34.00   M   10      1       2 13703   7.00   F   10      1       1 13704   4.00   F   10      1       1 13705  41.00   M   10      1       2 13706  20.00   M   10      1       2 13707  36.00   M   10      1       2 13708  23.00   M   10      1       2 13709  16.00   M   10      1       1 13710  22.00   M   10      1       2 13711  27.00   M   10      1       2 13712  11.00   F   10      1       1 13713   7.00   M   10      1       1 13714  26.00   M   21      1       2 13715  40.00   M   10      1       2 13716  36.00   M   22      1       2 13717  23.00   F    7      1       2 13718  28.00   F   10      1       2 13719  22.00   F    7      1       2 13720  42.00   M    7      1       2 13721  19.00   M   10      1       2 13722  60.00   M   13      1       3 13723  50.00   F   12      1       2 13724  10.00   F    7      1       1 13725   9.00   F   10      1       1 13726  35.00   M   10      1       2 13727  39.00   M   13      1       2 13728  48.00   M   17      1       2 13729  38.00   F   18      1       2 13730  18.00   M   13      1       2 13731   2.00   F   11      1       1 13732  32.00   M   11      1       2 13733  28.00   M   11      1       2 13734  37.00   F   11      1       2 13735  22.00   F    7      1       2 13736  28.00   F   37      1       2 13737  41.00   M   21      1       2 13738  31.00   F    4      1       2 13739  27.00   M   14      1       2 13740  30.00   M   14      1       2 13741  32.00   M    4      1       2 13742  17.00   F   12      1       1 13743  34.00   M   29      1       2 13744  25.00   M   14      1       2 13745  24.00   M   10      1       2 13746  52.00   M   11      1       2 13747   9.00   M   11      1       1 13748   4.00   M   14      1       1 13749   9.00   M    8      1       1 13750  35.00   M    8      1       2 13751   3.00   F    7      1       1 13752  32.00   M    8      1       2 13753  31.00   M    8      1       2 13754  68.00   F   16      1       3 13755  30.00   F   10      1       2 13756  23.00   F   15      1       2 13757  40.00   F   10      1       2 13758  25.00   M   15      1       2 13759  16.00   F    7      1       1 13760  48.00   M    7      1       2 13761  21.00   F    7      1       2 13762  46.00   M   10      1       2 13763  20.00   M    8      1       2 13764  73.00   F   15      1       3 13765  46.00   M   14      1       2 13766  26.00   M    6      1       2 13767  32.00   M   14      1       2 13768  23.00   M    7      1       2 13769  30.00   F   11      1       2 13770  39.00   M   22      1       2 13771  61.00   M   19      1       3 13772  62.00   F    7      1       3 13773  27.00   M   28      1       2 13774  34.00   F   11      1       2 13775  28.00   M   11      1       2 13776  48.00   M   13      1       2 13777  32.00   M   21      1       2 13778  34.00   F   29      1       2 13779  29.00   F   11      1       2 13780  39.00   M    3      1       2 13781  32.00   M    3      1       2 13782  34.00   M   11      1       2 13783  34.00   M   11      1       2 13784  41.00   M   13      1       2 13785  12.00   M    6      1       1 13786  25.00   M    9      1       2 13787  46.00   M    9      1       2 13788  18.00   F   12      1       2 13789  30.00   F    6      1       2 13790  42.00   F    9      1       2 13791  45.00   M    6      1       2 13792  32.00   M    6      1       2 13793  30.00   M    6      1       2 13794  32.00   M    6      1       2 13795  44.00   M   16      1       2 13796  45.00   F    6      1       2 13797  40.00   M   12      1       2 13798  37.00   M    8      1       2 13799  15.00   M    9      1       1 13800  36.00   M    9      1       2 13801  19.00   M    9      1       2 13802  21.00   M   15      1       2 13803  24.00   M   15      1       2 13804  25.00   M   15      1       2 13805  48.00   M   11      1       2 13806  19.00   M   13      1       2 13807  43.00   M   11      1       2 13808  59.00   F   21      1       2 13809  30.00   F   11      1       2 13810   6.00   F   11      1       1 13811  33.00   F   11      1       2 13812  34.00   M   23      1       2 13813  50.00   M   13      1       2 13814  24.00   F   11      1       2 13815  23.00   M    8      1       2 13816  38.00   M   14      1       2 13817  44.00   M   14      1       2 13818  30.00   M   28      1       2 13819  46.00   M   20      1       2 13820  50.00   M    4      1       2 13821  25.00   M   10      1       2 13822  28.00   M   15      1       2 13823  24.00   M   20      1       2 13824  28.00   M   15      1       2 13825  24.00   F   15      1       2 13826   8.00   M   23      1       1 13827  45.00   M   25      1       2 13828  11.00   M    9      1       1 13829  36.00   M    9      1       2 13830  18.00   F    6      1       2 13831  31.00   F   14      1       2 13832  36.00   M    8      1       2 13833  35.00   F    2      1       2 13834  11.00   F   17      1       1 13835  30.00   F   11      1       2 13836   9.00   M   20      1       1 13837   2.00   M   13      1       1 13838  34.00   M   15      1       2 13839  28.00   F   13      1       2 13840  22.00   M   13      1       2 13841  43.00   M   18      1       2 13842  40.00   M    6      1       2 13843  31.00   F    9      1       2 13844  36.00   M   12      1       2 13845  25.00   M   17      1       2 13846  35.00   M    6      1       2 13847  25.00   F    6      1       2 13848  23.00   M   12      1       2 13849   2.00   M    7      1       1 13850  42.00   M   17      1       2 13851  46.00   F    9      1       2 13852  60.00   M    6      1       3 13853  47.00   M    9      1       2 13854  36.00   M   17      1       2 13855  65.00   M   17      1       3 13856  29.00   M    9      1       2 13857  30.00   F    9      1       2 13858  48.00   M    9      1       2 13859  47.00   M   13      1       2 13860  42.00   M    7      1       2 13861  25.00   F   11      1       2 13862   4.00   M   11      1       1 13863  34.00   M   12      1       2 13864  22.00   M    9      1       2 13865  17.00   M    9      1       1 13866  32.00   F    9      1       2 13867  59.00   M    9      1       2 13868  30.00   M    9      1       2 13869  31.00   F   13      1       2 13870  20.00   F   15      1       2 13871  51.00   M    9      1       2 13872  20.00   M    9      1       2 13873  10.00   M    9      1       1 13874   6.00   M    9      1       1 13875  49.00   M   10      1       2 13876  63.00   M   10      1       3 13877  38.00   F   19      1       2 13878   7.00   M   19      1       1 13879  39.00   M   13      1       2 13880  34.00   M   13      1       2 13881  36.00   M   19      1       2 13882  39.00   M   10      1       2 13883  80.00   F    8      1       3 13884  27.00   F   13      1       2 13885  41.00   M    8      1       2 13886  28.00   M   26      1       2 13887   6.00   M   10      1       1 13888  36.00   M   10      1       2 13889  10.00   M   12      1       1 13890  50.00   M   18      1       2 13891  35.00   M   34      1       2 13892  55.00   F   27      1       2 13893  22.00   F   29      1       2 13894  15.00   F   24      1       1 13895  10.00   F   31      1       1 13896  13.00   M   12      1       1 13897  30.00   M   14      1       2 13898  65.00   F    6      1       3 13899  68.00   F    6      1       3 13900   8.00   M    6      1       1 13901  68.00   F    6      1       3 13902  26.00   M   11      1       2 13903  23.00   F    7      1       2 13904  28.00   M   16      1       2 13905  28.00   M   20      1       2 13906  35.00   F    6      1       2 13907  33.00   M   19      1       2 13908  20.00   M   10      1       2 13909  25.00   F   10      1       2 13910   8.00   F   10      1       1 13911  23.00   M   10      1       2 13912  10.00   F   10      1       1 13913  20.00   M   10      1       2 13914  34.00   F   10      1       2 13915  36.00   F   14      1       2 13916  33.00   F   14      1       2 13917   6.00   M   10      1       1 13918  42.00   M   10      1       2 13919  32.00   F   10      1       2 13920  12.00   F   10      1       1 13921  35.00   M   10      1       2 13922  38.00   M   12      1       2 13923  23.00   M    7      1       2 13924  27.00   M   18      1       2 13925  20.00   M   22      1       2 13926  18.00   F    9      1       2 13927  54.00   M   21      1       2 13928  34.00   M   21      1       2 13929  13.00   M   27      1       1 13930  29.00   M   22      1       2 13931  32.00   M    9      1       2 13932  29.00   F   22      1       2 13933  20.00   M   21      1       2 13934  21.00   M   22      1       2 13935  20.00   M   22      1       2 13936  16.00   M   22      1       1 13937  41.00   F   21      1       2 13938  11.00   F   22      1       1 13939  12.00   M   22      1       1 13940  35.00   F    9      1       2 13941  24.00   F   21      1       2 13942   9.00   F    9      1       1 13943  49.00   M   25      1       2 13944  37.00   M   22      1       2 13945  54.00   F   23      1       2 13946  44.00   F   22      1       2 13947  46.00   M    9      1       2 13948  42.00   M   29      1       2 13949  40.00   M   18      1       2 13950  16.00   M   22      1       1 13951  35.00   F   23      1       2 13952  48.00   M   23      1       2 13953  34.00   F    9      1       2 13954  16.00   F   22      1       1 13955  48.00   F   22      1       2 13956  19.00   M   22      1       2 13957  32.00   M   18      1       2 13958  30.00   F   15      1       2 13959  35.00   M   22      1       2 13960   7.00   M   23      1       1 13961  11.00   M   22      1       1 13962  19.00   F   18      1       2 13963  18.00   M    9      1       2 13964  45.00   M   22      1       2 13965  22.00   M   22      1       2 13966  30.00   F   22      1       2 13967  16.00   F    9      1       1 13968   8.00   M    9      1       1 13969  13.00   M    9      1       1 13970  23.00   F   27      1       2 13971  17.00   F    9      1       1 13972  32.00   M   22      1       2 13973  34.00   M   22      1       2 13974  15.00   M   22      1       1 13975  38.00   F   22      1       2 13976  18.00   F    9      1       2 13977  22.00   M   22      1       2 13978  13.00   M   22      1       1 13979  18.00   M   22      1       2 13980  45.00   M   21      1       2 13981  15.00   M    9      1       1 13982  38.00   M   22      1       2 13983  26.00   M   35      1       2 13984  17.00   M   20      1       1 13985  32.00   M   24      1       2 13986  36.00   M   36      1       2 13987  49.00   F   35      1       2 13988  68.00   M   13      1       3 13989  31.00   M   35      1       2 13990  28.00   F   20      1       2 13991  25.00   M   14      1       2 13992  32.00   F   29      1       2 13993   3.00   M   29      1       1 13994  27.00   F   14      1       2 13995  26.00   M   14      1       2 13996  35.00   M   14      1       2 13997  38.00   M   14      1       2 13998  30.00   F   14      1       2 13999  27.00   M   14      1       2 14000  18.00   F   14      1       2 14001  17.00   F   14      1       1 14002  17.00   M   14      1       1 14003  44.00   M   14      1       2 14004   8.00   F   14      1       1 14005   5.00   M   24      1       1 14006  14.00   M   14      1       1 14007  53.00   M   14      1       2 14008  27.00   M   14      1       2 14009  25.00   M   16      1       2 14010  17.00   M   14      1       1 14011  27.00   M   14      1       2 14012  54.00   M   22      1       2 14013  24.00   F   12      1       2 14014  35.00   M   14      1       2 14015  12.00   M   12      1       1 14016  35.00   M   14      1       2 14017   8.00   M   29      1       1 14018  24.00   M   14      1       2 14019   3.00   F   12      1       1 14020  20.00   F   22      1       2 14021  40.00   F   14      1       2 14022  38.00   M   14      1       2 14023  24.00   M   14      1       2 14024  29.00   M   14      1       2 14025  50.00   M   14      1       2 14026  40.00   M   12      1       2 14027  10.00   F   12      1       1 14028  40.00   M   14      1       2 14029  12.00   M   12      1       1 14030  50.00   F   14      1       2 14031  48.00   M   14      1       2 14032  33.00   F   14      1       2 14033  35.00   F   14      1       2 14034  22.00   F   14      1       2 14035   2.00   M   12      1       1 14036  22.00   M   22      1       2 14037  24.00   F   14      1       2 14038  34.00   M   14      1       2 14039  25.00   F   14      1       2 14040  28.00   F   14      1       2 14041  37.00   M   14      1       2 14042  14.00   F   29      1       1 14043  30.00   M   14      1       2 14044  65.00   M   21      1       3 14045  26.00   M   10      1       2 14046   3.00   M    7      1       1 14047  30.00   M   16      1       2 14048  28.00   M    7      1       2 14049  50.00   F   10      1       2 14050  15.00   F   10      1       1 14051  13.00   F   10      1       1 14052   7.00   M   10      1       1 14053   6.00   M   10      1       1 14054  35.00   F   12      1       2 14055  20.00   F   10      1       2 14056  20.00   F   12      1       2 14057  25.00   M   10      1       2 14058  26.00   F   12      1       2 14059   2.00   F   12      1       1 14060  35.00   M   10      1       2 14061  37.00   F   12      1       2 14062  22.00   F   10      1       2 14063  32.00   M   12      1       2 14064   8.00   M   12      1       1 14065  20.00   F   10      1       2 14066  25.00   M   12      1       2 14067  23.00   M   10      1       2 14068  16.00   F   10      1       1 14069  48.00   M   10      1       2 14070  18.00   M   10      1       2 14071  13.00   F   10      1       1 14072  29.00   F   12      1       2 14073  21.00   M   12      1       2 14074  22.00   M   12      1       2 14075  55.00   M   10      1       2 14076  46.00   F   10      1       2 14077  17.00   F   10      1       1 14078  16.00   F   10      1       1 14079  30.00   M   10      1       2 14080  23.00   F   10      1       2 14081  38.00   M   19      1       2 14082  42.00   M   10      1       2 14083  38.00   F   19      1       2 14084  30.00   M   10      1       2 14085  26.00   F   10      1       2 14086  10.00   F   10      1       1 14087  20.00   M   10      1       2 14088  27.00   M   10      1       2 14089  22.00   F   10      1       2 14090  31.00   M   10      1       2 14091  35.00   M   12      1       2 14092  20.00   M   14      1       2 14093  60.00   F   12      1       3 14094   6.00   F   10      1       1 14095  48.00   M    8      1       2 14096  28.00   M    8      1       2 14097  38.00   M    8      1       2 14098   3.00   M   16      1       1 14099  23.00   M   40      1       2 14100  31.00   M   11      1       2 14101  41.00   F   17      1       2 14102  30.00   M   10      1       2 14103  22.00   M   10      1       2 14104  38.00   F    8      1       2 14105  47.00   M   15      1       2 14106  22.00   M    8      1       2 14107  20.00   F    8      1       2 14108   5.00   F    8      1       1 14109  27.00   F    6      1       2 14110  24.00   M    8      1       2 14111  25.00   F    8      1       2 14112  35.00   M    8      1       2 14113  34.00   F    4      1       2 14114  32.00   M    7      1       2 14115  30.00   M    9      1       2 14116  41.00   M   26      1       2 14117  25.00   M   26      1       2 14118  40.00   M   11      1       2 14119  37.00   M    9      1       2 14120  47.00   M    9      1       2 14121  36.00   M   11      1       2 14122  15.00   M   15      1       1 14123  40.00   M   27      1       2 14124   2.00   F    9      1       1 14125  47.00   F   19      1       2 14126  48.00   M   34      1       2 14127  29.00   M   13      1       2 14128  20.00   M   13      1       2 14129  45.00   M   13      1       2 14130  40.00   M   13      1       2 14131  43.00   M   13      1       2 14132  19.00   M   13      1       2 14133  11.00   M   13      1       1 14134  39.00   M   13      1       2 14135  32.00   M   13      1       2 14136  30.00   F   20      1       2 14137  27.00   F   23      1       2 14138  66.00   M   13      1       3 14139  17.00   M   28      1       1 14140  30.00   M   13      1       2 14141   7.00   F   13      1       1 14142   3.00   F   13      1       1 14143   2.00   M   13      1       1 14144   1.00   F   13      1       1 14145  19.00   M   28      1       2 14146  16.00   F   13      1       1 14147  24.00   M    4      1       2 14148  45.00   F    4      1       2 14149  33.00   F    5      1       2 14150  18.00   M   33      1       2 14151  55.00   F   10      1       2 14152  32.00   F    5      1       2 14153  40.00   M   12      1       2 14154   7.00   F   10      1       1 14155  32.00   M   10      1       2 14156  29.00   F   10      1       2 14157  38.00   F   10      1       2 14158  23.00   M   16      1       2 14159  27.00   M   13      1       2 14160  42.00   M   11      1       2 14161  32.00   M    6      1       2 14162  25.00   F   26      1       2 14163  28.00   M   25      1       2 14164  20.00   M    7      1       2 14165  35.00   M   20      1       2 14166  30.00   F    7      1       2 14167  37.00   F    9      1       2 14168  14.00   F    7      1       1 14169  28.00   M    8      1       2 14170  35.00   F   12      1       2 14171  15.00   M   14      1       1 14172  44.00   M   18      1       2 14173  35.00   F   13      1       2 14174  38.00   M    4      1       2 14175  44.00   M   24      1       2 14176  45.00   M   11      1       2 14177  65.00   M   11      1       3 14178  47.00   M   11      1       2 14179  23.00   M   13      1       2 14180   7.00   M   13      1       1 14181   3.00   F   13      1       1 14182  27.00   F   13      1       2 14183  19.00   M    9      1       2 14184  13.00   M    9      1       1 14185  15.00   M    9      1       1 14186  11.00   M    9      1       1 14187  51.00   F   17      1       2 14188  54.00   M    8      1       2 14189  24.00   M   21      1       2 14190  21.00   M   10      1       2 14191  38.00   M   17      1       2 14192  48.00   M   20      1       2 14193  38.00   F   20      1       2 14194  11.00   F   16      1       1 14195  61.00   M    5      1       3 14196  24.00   M    5      1       2 14197  31.00   M   10      1       2 14198  17.00   M    5      1       1 14199  20.00   M   16      1       2 14200  33.00   F    5      1       2 14201  26.00   M    4      1       2 14202  18.00   F    9      1       2 14203  32.00   M   39      1       2 14204  22.00   F    9      1       2 14205  19.00   M    9      1       2 14206  27.00   M   38      1       2 14207  18.00   M    9      1       2 14208  36.00   M    9      1       2 14209  29.00   M   16      1       2 14210  55.00   M   16      1       2 14211  30.00   M   34      1       2 14212   8.00   M   34      1       1 14213  30.00   M   15      1       2 14214  46.00   M   15      1       2 14215  29.00   M    4      1       2 14216  18.00   M    4      1       2 14217  20.00   M    6      1       2 14218  11.00   F    8      1       1 14219  12.00   M   34      1       1 14220   2.00   F    8      1       1 14221  36.00   F    6      1       2 14222  54.00   M   17      1       2 14223  19.00   F    4      1       2 14224  53.00   M    4      1       2 14225  29.00   M    4      1       2 14226  25.00   F    7      1       2 14227  35.00   F    7      1       2 14228  58.00   M    9      1       2 14229  17.00   F    9      1       1 14230   5.00   F    9      1       1 14231  26.00   F    9      1       2 14232  44.00   F    9      1       2 14233  16.00   F   17      1       1 14234  46.00   M   17      1       2 14235  34.00   M   17      1       2 14236  18.00   M    8      1       2 14237  12.00   F   34      1       1 14238   5.00   M   17      1       1 14239  17.00   M   34      1       1 14240  19.00   F   34      1       2 14241  30.00   M   17      1       2 14242  32.00   F   17      1       2 14243  33.00   F   13      1       2 14244  34.00   F   13      1       2 14245  45.00   M   10      1       2 14246  17.00   F   10      1       1 14247   5.00   F   13      1       1 14248  65.00   M   17      1       3 14249  70.00   M    7      1       3 14250  55.00   M    6      1       2 14251  55.00   F    6      1       2 14252  38.00   M    6      1       2 14253  28.00   F    7      1       2 14254  38.00   M    8      1       2 14255  30.00   M    8      1       2 14256  19.00   F   13      1       2 14257  24.00   M   16      1       2 14258  21.00   M   33      1       2 14259   8.00   M   16      1       1 14260  40.00   F   10      1       2 14261  25.00   F   25      1       2 14262  34.00   M   25      1       2 14263  36.00   M   25      1       2 14264  20.00   M   20      1       2 14265  30.00   M   25      1       2 14266  27.00   F   19      1       2 14267   6.00   M   25      1       1 14268   3.00   M   25      1       1 14269  25.00   M   21      1       2 14270   1.00   M   21      1       1 14271  40.00   F   27      1       2 14272  18.00   M   25      1       2 14273  12.00   M   19      1       1 14274   2.00   M   21      1       1 14275  40.00   M   25      1       2 14276  31.00   M   19      1       2 14277  45.00   M   16      1       2 14278  35.00   F   23      1       2 14279  25.00   F    8      1       2 14280   5.00   M   25      1       1 14281  22.00   F   12      1       2 14282  30.00   F   10      1       2 14283  25.00   M   10      1       2 14284  30.00   F   10      1       2 14285  20.00   M   27      1       2 14286   6.00   M   10      1       1 14287  28.00   M    9      1       2 14288  65.00   F    9      1       3 14289  34.00   F    9      1       2 14290   8.00   F    9      1       1 14291  60.00   F   10      1       3 14292  40.00   M    9      1       2 14293  44.00   F    9      1       2 14294  27.00   F   10      1       2 14295   6.00   F   10      1       1 14296   0.90   M   10      1       1 14297  25.00   M   10      1       2 14298  40.00   M    4      1       2 14299  35.00   F   10      1       2 14300   7.00   F   10      1       1 14301   6.00   F   10      1       1 14302  32.00   F   10      1       2 14303  18.00   M   24      1       2 14304  30.00   M   23      1       2 14305  29.00   M    9      1       2 14306   4.00   M    9      1       1 14307  31.00   M    9      1       2 14308  48.00   M    9      1       2 14309  38.00   F    9      1       2 14310  13.00   M    9      1       1 14311  32.00   M    9      1       2 14312  35.00   F    9      1       2 14313  21.00   F    9      1       2 14314  18.00   F    9      1       2 14315  62.00   M    9      1       3 14316  52.00   F    9      1       2 14317  24.00   M   10      1       2 14318  21.00   M    9      1       2 14319  48.00   M    9      1       2 14320  24.00   M    9      1       2 14321  11.00   F    9      1       1 14322  10.00   F    9      1       1 14323  27.00   M    9      1       2 14324  28.00   M   12      1       2 14325  32.00   F   12      1       2 14326  19.00   F   12      1       2 14327   6.00   F    9      1       1 14328  19.00   M   12      1       2 14329  37.00   F   10      1       2 14330  45.00   M   10      1       2 14331  46.00   F   16      1       2 14332  51.00   M   24      1       2 14333  27.00   F   16      1       2 14334  51.00   M    9      1       2 14335  32.00   M    8      1       2 14336  15.00   M   14      1       1 14337  46.00   M   23      1       2 14338  26.00   F   10      1       2 14339  37.00   F   10      1       2 14340  23.00   F   10      1       2 14341  32.00   M   16      1       2 14342  42.00   M   10      1       2 14343  26.00   F   14      1       2 14344   6.00   M   12      1       1 14345  36.00   M   14      1       2 14346   3.00   M   12      1       1 14347  18.00   M   12      1       2 14348  40.00   M   12      1       2 14349  29.00   M   12      1       2 14350  16.00   M   27      1       1 14351  11.00   M   12      1       1 14352  19.00   F   12      1       2 14353  12.00   M   19      1       1 14354  50.00   M   12      1       2 14355  50.00   F   33      1       2 14356  37.00   F   22      1       2 14357  18.00   F   27      1       2 14358  15.00   F   12      1       1 14359  13.00   M   12      1       1 14360  50.00   F   12      1       2 14361  11.00   F   12      1       1 14362  19.00   F   27      1       2 14363  25.00   F   12      1       2 14364  11.00   F   12      1       1 14365  16.00   M   12      1       1 14366  13.00   M   12      1       1 14367  25.00   M   12      1       2 14368  37.00   M   12      1       2 14369  18.00   F   12      1       2 14370  36.00   F   12      1       2 14371  14.00   M   12      1       1 14372  22.00   F   12      1       2 14373  30.00   F   12      1       2 14374  21.00   F   27      1       2 14375  18.00   F   12      1       2 14376  21.00   F   12      1       2 14377   4.00   F   12      1       1 14378  12.00   F   12      1       1 14379  42.00   M   22      1       2 14380  13.00   F   12      1       1 14381  22.00   F   12      1       2 14382  23.00   M   20      1       2 14383   6.00   M   20      1       1 14384  45.00   F   11      1       2 14385  31.00   F   11      1       2 14386  36.00   M   11      1       2 14387  28.00   F   16      1       2 14388  27.00   M   11      1       2 14389  50.00   M   11      1       2 14390  40.00   F   23      1       2 14391  27.00   F    5      1       2 14392  32.00   M    5      1       2 14393  22.00   M    5      1       2 14394  14.00   F    5      1       1 14395  38.00   M   16      1       2 14396  23.00   M   25      1       2 14397  35.00   M   25      1       2 14398  16.00   M   19      1       1 14399  24.00   M   19      1       2 14400  20.00   M   27      1       2 14401  22.00   M    8      1       2 14402  46.00   F   16      1       2 14403  42.00   M   21      1       2 14404   9.00   M   21      1       1 14405  13.00   F   27      1       1 14406  35.00   F   16      1       2 14407  13.00   F    8      1       1 14408  36.00   F   16      1       2 14409  35.00   M   19      1       2 14410   1.00   M   20      1       1 14411  28.00   M    8      1       2 14412  24.00   M   19      1       2 14413  18.00   F    8      1       2 14414  21.00   F   25      1       2 14415  20.00   F   16      1       2 14416  11.00   M   23      1       1 14417  35.00   F   25      1       2 14418  10.00   M   25      1       1 14419  18.00   M    8      1       2 14420  13.00   M   19      1       1 14421  17.00   F   25      1       1 14422  18.00   M   16      1       2 14423  35.00   M   20      1       2 14424   5.00   M   25      1       1 14425   5.00   M   16      1       1 14426  30.00   F    8      1       2 14427  36.00   M   25      1       2 14428  30.00   M    8      1       2 14429  30.00   M   25      1       2 14430  30.00   F   16      1       2 14431  18.00   F   31      1       2 14432  20.00   F   23      1       2 14433  40.00   M    8      1       2 14434  36.00   F   23      1       2 14435  25.00   F   31      1       2 14436  22.00   F    8      1       2 14437  22.00   F   20      1       2 14438  25.00   F   16      1       2 14439  18.00   F   25      1       2 14440   6.00   F   25      1       1 14441  20.00   M   25      1       2 14442  28.00   M   25      1       2 14443  48.00   F   16      1       2 14444  28.00   F   16      1       2 14445  34.00   F   19      1       2 14446   3.00   F   25      1       1 14447  20.00   F   31      1       2 14448  25.00   F   16      1       2 14449  22.00   M   16      1       2 14450  30.00   F   25      1       2 14451   3.00   M   19      1       1 14452  35.00   M   20      1       2 14453   6.00   F   20      1       1 14454  47.00   M   16      1       2 14455  18.00   M   20      1       2 14456  21.00   M   16      1       2 14457  50.00   M   19      1       2 14458  27.00   M   24      1       2 14459  32.00   M    7      1       2 14460  21.00   F   19      1       2 14461   1.00   M   26      1       1 14462  21.00   M    7      1       2 14463  23.00   M   12      1       2 14464  28.00   F    7      1       2 14465  35.00   M    7      1       2 14466  27.00   M   12      1       2 14467  25.00   M   15      1       2 14468   3.00   M   21      1       1 14469  47.00   M   15      1       2 14470  35.00   M   12      1       2 14471  55.00   M   24      1       2 14472   4.00   F   11      1       1 14473  45.00   F    8      1       2 14474  35.00   F    9      1       2 14475  23.00   F    9      1       2 14476  24.00   M   11      1       2 14477  62.00   M   13      1       3 14478  22.00   M   11      1       2 14479  26.00   F   18      1       2 14480  26.00   F   18      1       2 14481  25.00   M   10      1       2 14482  10.00   F   10      1       1 14483   4.00   M   18      1       1 14484  18.00   F   10      1       2 14485  30.00   M   10      1       2 14486  16.00   F   10      1       1 14487  16.00   F   10      1       1 14488  21.00   M   10      1       2 14489  35.00   F   10      1       2 14490  30.00   F   17      1       2 14491  30.00   F   24      1       2 14492  33.00   M   21      1       2 14493  59.00   M    8      1       2 14494  37.00   M   14      1       2 14495   5.00   M    8      1       1 14496   2.00   M   11      1       1 14497  20.00   F   16      1       2 14498  54.00   F   16      1       2 14499  30.00   M    8      1       2 14500  22.00   F   14      1       2 14501  25.00   F   14      1       2 14502   3.00   F    8      1       1 14503  28.00   F   19      1       2 14504  50.00   M   16      1       2 14505  49.00   M   18      1       2 14506  42.00   M    4      1       2 14507  35.00   M   10      1       2 14508  39.00   F   18      1       2 14509  29.00   M    5      1       2 14510  25.00   F    5      1       2 14511  31.00   M    6      1       2 14512  51.00   M    5      1       2 14513  32.00   M   10      1       2 14514  25.00   F    5      1       2 14515   6.00   F   12      1       1 14516  33.00   M   16      1       2 14517  43.00   M    7      1       2 14518  31.00   F   14      1       2 14519  27.00   M   18      1       2 14520  52.00   M   18      1       2 14521  50.00   M   18      1       2 14522  44.00   M   16      1       2 14523  30.00   M    7      1       2 14524  17.00   M    6      1       1 14525  31.00   M    6      1       2 14526  52.00   M    6      1       2 14527  35.00   F    6      1       2 14528  40.00   M    9      1       2 14529  38.00   F    6      1       2 14530  43.00   M   14      1       2 14531  20.00   M   10      1       2 14532  31.00   F    6      1       2 14533  42.00   F   17      1       2 14534   7.00   F    6      1       1 14535  36.00   M    6      1       2 14536  45.00   M    6      1       2 14537  12.00   M    5      1       1 14538  31.00   F    5      1       2 14539  28.00   M   10      1       2 14540  58.00   M   10      1       2 14541  36.00   M   10      1       2 14542  48.00   F   10      1       2 14543  10.00   M   12      1       1 14544  26.00   M    8      1       2 14545  42.00   M    8      1       2 14546  43.00   M    8      1       2 14547  16.00   F    8      1       1 14548  36.00   F    8      1       2 14549  14.00   M   12      1       1 14550  30.00   F    8      1       2 14551  14.00   F   12      1       1 14552  28.00   F    8      1       2 14553  43.00   F    8      1       2 14554  49.00   M   12      1       2 14555  32.00   F    8      1       2 14556  31.00   F   12      1       2 14557   5.00   F    8      1       1 14558  16.00   F   32      1       1 14559  20.00   M   12      1       2 14560  34.00   M   15      1       2 14561  64.00   F    8      1       3 14562  24.00   F   15      1       2 14563  10.00   M   15      1       1 14564  40.00   M   12      1       2 14565  44.00   M   12      1       2 14566  38.00   M   12      1       2 14567  40.00   M   13      1       2 14568  13.00   M   12      1       1 14569  42.00   M    9      1       2 14570  48.00   M   13      1       2 14571  11.00   M   15      1       1 14572  38.00   M    7      1       2 14573  35.00   M    7      1       2 14574  26.00   F   16      1       2 14575  54.00   F   16      1       2 14576  28.00   F    7      1       2 14577  25.00   M    7      1       2 14578  36.00   M   13      1       2 14579  29.00   M    3      1       2 14580  50.00   M    9      1       2 14581  32.00   F    7      1       2 14582  35.00   F    7      1       2 14583  29.00   M    5      1       2 14584  39.00   M   11      1       2 14585  25.00   M    7      1       2 14586  55.00   F   10      1       2 14587  67.00   M    5      1       3 14588  45.00   M    7      1       2 14589  10.00   F    3      1       1 14590  28.00   M   23      1       2 14591  56.00   M    6      1       2 14592  57.00   M    7      1       2 14593  22.00   M    7      1       2 14594  35.00   M    7      1       2 14595  37.00   F    5      1       2 14596  41.00   M    7      1       2 14597  65.00   M    5      1       3 14598  53.00   M   16      1       2 14599  33.00   M    7      1       2 14600  49.00   M    7      1       2 14601  59.00   M    7      1       2 14602   4.00   M   13      1       1 14603  43.00   M    3      1       2 14604  47.00   M    9      1       2 14605  33.00   M    7      1       2 14606  39.00   M    5      1       2 14607  43.00   M    3      1       2 14608  42.00   M    3      1       2 14609  34.00   F   15      1       2 14610  40.00   M    7      1       2 14611  33.00   F    7      1       2 14612  10.00   M    9      1       1 14613  43.00   M    3      1       2 14614  31.00   F    7      1       2 14615  29.00   M    7      1       2 14616  70.00   M    5      1       3 14617   9.00   F   13      1       1 14618  41.00   M   11      1       2 14619  39.00   M   13      1       2 14620  30.00   F   10      1       2 14621  16.00   M   13      1       1 14622  33.00   M    7      1       2 14623  22.00   M   14      1       2 14624  23.00   M    8      1       2 14625  29.00   M   11      1       2 14626  29.00   M   11      1       2 14627  24.00   M   11      1       2 14628  30.00   F    8      1       2 14629  52.00   M   11      1       2 14630  52.00   M   14      1       2 14631  49.00   M    8      1       2 14632   2.00   F    8      1       1 14633  28.00   M    8      1       2 14634  57.00   M   18      1       2 14635  35.00   F   23      1       2 14636   0.30   M    9      1       1 14637   8.00   F   15      1       1 14638  19.00   M   15      1       2 14639  20.00   M   12      1       2 14640  14.00   F   15      1       1 14641  51.00   M   15      1       2 14642  58.00   M   15      1       2 14643  43.00   M   12      1       2 14644  44.00   M   17      1       2 14645  30.00   F    7      1       2 14646  35.00   F   17      1       2 14647  55.00   M    7      1       2 14648  50.00   M   15      1       2 14649  13.00   F   15      1       1 14650  15.00   F   14      1       1 14651   9.00   M   14      1       1 14652  32.00   M   10      1       2 14653  30.00   F   10      1       2 14654  42.00   M   17      1       2 14655  50.00   M    9      1       2 14656  39.00   F   17      1       2 14657  15.00   M   13      1       1 14658  44.00   M   18      1       2 14659  36.00   M   11      1       2 14660  40.00   M    8      1       2 14661  55.00   F   13      1       2 14662  47.00   M   13      1       2 14663  30.00   M    9      1       2 14664  44.00   M    7      1       2 14665  45.00   M    7      1       2 14666  58.00   M   24      1       2 14667  50.00   M    9      1       2 14668   3.00   F   16      1       1 14669  13.00   F    7      1       1 14670  47.00   M    7      1       2 14671  37.00   M    7      1       2 14672  22.00   M   13      1       2 14673  47.00   M   13      1       2 14674  29.00   M    9      1       2 14675  36.00   F    5      1       2 14676  45.00   F    9      1       2 14677  24.00   F    9      1       2 14678  50.00   M    9      1       2 14679  27.00   M    9      1       2 14680  43.00   M    4      1       2 14681  48.00   F    6      1       2 14682  28.00   M    6      1       2 14683  29.00   M   13      1       2 14684  51.00   F   13      1       2 14685  27.00   F    6      1       2 14686  59.00   M    6      1       2 14687  27.00   M   13      1       2 14688  23.00   M   13      1       2 14689  15.00   F   13      1       1 14690  30.00   F   23      1       2 14691  37.00   F   23      1       2 14692  43.00   M    9      1       2 14693  22.00   F    9      1       2 14694   3.00   F    9      1       1 14695   7.00   M   10      1       1 14696   8.00   F   10      1       1 14697  12.00   F   10      1       1 14698  15.00   F   16      1       1 14699  35.00   F   16      1       2 14700  45.00   M   16      1       2 14701  65.00   F    9      1       3 14702  34.00   M    9      1       2 14703  16.00   M    9      1       1 14704  35.00   M    9      1       2 14705  20.00   M    9      1       2 14706  32.00   M    9      1       2 14707  30.00   M    9      1       2 14708  26.00   M    9      1       2 14709  20.00   M    9      1       2 14710  20.00   M    9      1       2 14711  19.00   M    9      1       2 14712  54.00   M    9      1       2 14713  44.00   M    9      1       2 14714  25.00   M   10      1       2 14715  25.00   M    9      1       2 14716  47.00   M    9      1       2 14717  25.00   M    9      1       2 14718  45.00   F    6      1       2 14719  50.00   M    6      1       2 14720  27.00   M   23      1       2 14721  28.00   F    4      1       2 14722  28.00   M   13      1       2 14723  15.00   F    8      1       1 14724  12.00   F   13      1       1 14725  38.00   F   16      1       2 14726  31.00   M   27      1       2 14727  28.00   F   10      1       2 14728  34.00   M    7      1       2 14729  31.00   M    5      1       2 14730  14.00   M    7      1       1 14731  30.00   F   10      1       2 14732  31.00   M   10      1       2 14733  13.00   F   10      1       1 14734  30.00   F   10      1       2 14735   4.00   M   11      1       1 14736   6.00   M   10      1       1 14737  65.00   F    9      1       3 14738   2.00   F   12      1       1 14739  11.00   M    6      1       1 14740  53.00   M    8      1       2 14741  31.00   M    8      1       2 14742  29.00   F    8      1       2 14743  35.00   M    8      1       2 14744  44.00   M    8      1       2 14745  38.00   M    8      1       2 14746  32.00   F    8      1       2 14747  26.00   M    8      1       2 14748  20.00   M    8      1       2 14749  48.00   M    8      1       2 14750  66.00   F    8      1       3 14751  42.00   M   10      1       2 14752  25.00   M   12      1       2 14753  47.00   M   12      1       2 14754  22.00   M    6      1       2 14755  21.00   M    6      1       2 14756  37.00   F    6      1       2 14757  30.00   M    6      1       2 14758  45.00   M    8      1       2 14759  35.00   M   16      1       2 14760  30.00   F   14      1       2 14761  40.00   M   10      1       2 14762  24.00   M    6      1       2 14763  30.00   M    6      1       2 14764  39.00   M   10      1       2 14765  36.00   M   12      1       2 14766  36.00   M    6      1       2 14767  28.00   M   10      1       2 14768  36.00   M    9      1       2 14769   8.00   M    6      1       1 14770  28.00   M   14      1       2 14771  32.00   M   14      1       2 14772  28.00   F   14      1       2 14773  50.00   M   14      1       2 14774  36.00   F   30      1       2 14775  59.00   F   30      1       2 14776  40.00   M   14      1       2 14777  18.00   F   10      1       2 14778  19.00   F   14      1       2 14779  21.00   M   14      1       2 14780  28.00   M   10      1       2 14781  38.00   F    5      1       2 14782  17.00   M    5      1       1 14783  24.00   F    5      1       2 14784  29.00   M   12      1       2 14785  37.00   M   12      1       2 14786  42.00   F   12      1       2 14787  30.00   F   11      1       2 14788  19.00   M   16      1       2 14789  12.00   F   10      1       1 14790  18.00   M   12      1       2 14791  40.00   M   10      1       2 14792  36.00   M   10      1       2 14793  33.00   M   10      1       2 14794  30.00   M   10      1       2 14795  29.00   F   10      1       2 14796  40.00   M   10      1       2 14797  28.00   F   15      1       2 14798  44.00   F   10      1       2 14799  30.00   F   15      1       2 14800  28.00   M   10      1       2 14801  24.00   F   10      1       2 14802  30.00   F   10      1       2 14803  28.00   M   12      1       2 14804  26.00   F   15      1       2 14805   4.00   M   12      1       1 14806  40.00   M   15      1       2 14807  35.00   F   15      1       2 14808  31.00   M   16      1       2 14809  21.00   M   12      1       2 14810  28.00   F   16      1       2 14811  22.00   F   12      1       2 14812   4.00   F   12      1       1 14813   1.00   F   12      1       1 14814  28.00   M   12      1       2 14815   2.00   F   15      1       1 14816  43.00   M   15      1       2 14817  35.00   F   15      1       2 14818  21.00   M   15      1       2 14819  42.00   M   12      1       2 14820  58.00   M    6      1       2 14821  30.00   M   10      1       2 14822  29.00   M   23      1       2 14823  36.00   M    6      1       2 14824  33.00   F    6      1       2 14825  36.00   M   11      1       2 14826  14.00   F   10      1       1 14827  20.00   M   12      1       2 14828  42.00   F   15      1       2 14829  42.00   M   12      1       2 14830  26.00   M   10      1       2 14831  27.00   M    6      1       2 14832  42.00   M   12      1       2 14833  20.00   M   12      1       2 14834  44.00   M   12      1       2 14835  22.00   M   14      1       2 14836  49.00   M   10      1       2 14837  21.00   M    9      1       2 14838  39.00   M   10      1       2 14839  16.00   F   10      1       1 14840  30.00   F   17      1       2 14841  42.00   F   10      1       2 14842  47.00   M   11      1       2 14843  26.00   F   10      1       2 14844  32.00   M    9      1       2 14845  56.00   M   10      1       2 14846  42.00   M    6      1       2 14847  38.00   M   10      1       2 14848  42.00   M   12      1       2 14849  37.00   M    6      1       2 14850  45.00   M   14      1       2 14851  24.00   M   10      1       2 14852  67.00   M   12      1       3 14853  48.00   M   14      1       2 14854  42.00   M   11      1       2 14855  34.00   F   12      1       2 14856  39.00   M   14      1       2 14857  33.00   M   14      1       2 14858  44.00   M   14      1       2 14859  29.00   F    6      1       2 14860  44.00   M    6      1       2 14861  42.00   M   12      1       2 14862  36.00   F    6      1       2 14863  29.00   M   10      1       2 14864  34.00   M    6      1       2 14865  23.00   M   17      1       2 14866  32.00   M   10      1       2 14867  21.00   M   23      1       2 14868  38.00   M   10      1       2 14869  29.00   F    6      1       2 14870  34.00   M    6      1       2 14871  39.00   M    6      1       2 14872  47.00   M   10      1       2 14873  32.00   M   24      1       2 14874  16.00   M    6      1       1 14875  38.00   M   12      1       2 14876  38.00   M   10      1       2 14877  29.00   M   14      1       2 14878  31.00   M   14      1       2 14879  30.00   M   15      1       2 14880  42.00   M   11      1       2 14881  36.00   M    6      1       2 14882  41.00   M   12      1       2 14883  26.00   M   23      1       2 14884  29.00   M    6      1       2 14885  33.00   F   10      1       2 14886   4.00   M   23      1       1 14887  47.00   M   11      1       2 14888  40.00   M    6      1       2 14889  32.00   F   10      1       2 14890  31.00   M    6      1       2 14891  54.00   M   15      1       2 14892  31.00   M   10      1       2 14893  32.00   M   11      1       2 14894  30.00   F   12      1       2 14895  25.00   M   12      1       2 14896  29.00   M   10      1       2 14897  43.00   M   14      1       2 14898  39.00   M    6      1       2 14899  33.00   F   14      1       2 14900  44.00   M   12      1       2 14901  31.00   M   11      1       2 14902  27.00   M   10      1       2 14903  32.00   F   11      1       2 14904  45.00   M   18      1       2 14905  26.00   M   14      1       2 14906  40.00   F    6      1       2 14907  28.00   M   14      1       2 14908  37.00   M    6      1       2 14909  35.00   M    6      1       2 14910  55.00   M    6      1       2 14911  47.00   M    6      1       2 14912  44.00   M   19      1       2 14913  38.00   M   19      1       2 14914  35.00   M   12      1       2 14915  33.00   M   11      1       2 14916  18.00   M   12      1       2 14917  40.00   M   12      1       2 14918  50.00   F    6      1       2 14919  35.00   M    6      1       2 14920  52.00   M    6      1       2 14921  24.00   F   12      1       2 14922  57.00   M   15      1       2 14923  18.00   M   10      1       2 14924  35.00   F    6      1       2 14925  36.00   M   12      1       2 14926  46.00   M   12      1       2 14927  50.00   M   14      1       2 14928  22.00   F    6      1       2 14929  43.00   M   12      1       2 14930  49.00   M   14      1       2 14931  34.00   F    6      1       2 14932  39.00   M    6      1       2 14933  37.00   F   14      1       2 14934  33.00   M   15      1       2 14935  36.00   M   10      1       2 14936  14.00   M    6      1       1 14937  46.00   M   13      1       2 14938  43.00   M   14      1       2 14939  33.00   F   11      1       2 14940  43.00   M   14      1       2 14941  42.00   M    6      1       2 14942  26.00   M   11      1       2 14943  24.00   M    6      1       2 14944  35.00   M    7      1       2 14945  32.00   M    7      1       2 14946  40.00   M    7      1       2 14947   0.20   F   10      1       1 14948  14.00   F    7      1       1 14949  30.00   F   10      1       2 14950  30.00   F   23      1       2 14951   6.00   F   23      1       1 14952   1.00   M   23      1       1 14953  22.00   M   17      1       2 14954   3.00   M   23      1       1 14955  27.00   M   17      1       2 14956  32.00   M   23      1       2 14957  49.00   F   19      1       2 14958  22.00   F   14      1       2 14959  42.00   M   23      1       2 14960  23.00   M   23      1       2 14961  21.00   M   23      1       2 14962  26.00   F   14      1       2 14963  35.00   M   18      1       2 14964  25.00   F   23      1       2 14965   5.00   F   11      1       1 14966  65.00   F    7      1       3 14967  37.00   F    5      1       2 14968  24.00   M    9      1       2 14969  46.00   F    9      1       2 14970  35.00   F    8      1       2 14971  24.00   M   14      1       2 14972  19.00   M    7      1       2 14973  48.00   M   10      1       2 14974  32.00   M   10      1       2 14975  42.00   F   10      1       2 14976  28.00   M   10      1       2 14977  24.00   F   10      1       2 14978   7.00   F   10      1       1 14979  23.00   M   13      1       2 14980  40.00   F   17      1       2 14981  21.00   M   22      1       2 14982  18.00   M   12      1       2 14983  25.00   M   17      1       2 14984  25.00   F   10      1       2 14985  45.00   M   12      1       2 14986  30.00   M   10      1       2 14987  34.00   M   10      1       2 14988  26.00   F   10      1       2 14989  43.00   M   12      1       2 14990   2.00   M   10      1       1 14991  55.00   M   10      1       2 14992  12.00   M   10      1       1 14993  29.00   M   13      1       2 14994  19.00   M   13      1       2 14995  16.00   F    8      1       1 14996  42.00   M   12      1       2 14997  14.00   M   10      1       1 14998  27.00   F   13      1       2 14999  37.00   F   10      1       2 15000  55.00   M   10      1       2 15001  32.00   M   17      1       2 15002  56.00   F   10      1       2 15003  20.00   M   10      1       2 15004  14.00   M   17      1       1 15005  31.00   F    5      1       2 15006  28.00   F   10      1       2 15007  45.00   M   10      1       2 15008  26.00   F    5      1       2 15009  62.00   F    5      1       3 15010  30.00   F    5      1       2 15011  32.00   F    5      1       2 15012  26.00   F    5      1       2 15013  13.00   M    5      1       1 15014  46.00   M    5      1       2 15015  48.00   F   32      1       2 15016  17.00   M   10      1       1 15017  47.00   M    5      1       2 15018  15.00   M    5      1       1 15019  45.00   M    5      1       2 15020  41.00   M    5      1       2 15021   6.00   M    5      1       1 15022  51.00   M   17      1       2 15023  22.00   F    5      1       2 15024  24.00   M    5      1       2 15025  42.00   F    5      1       2 15026  26.00   F    5      1       2 15027  41.00   F   17      1       2 15028   4.00   F   10      1       1 15029  65.00   F    5      1       3 15030  25.00   M   12      1       2 15031  30.00   M   12      1       2 15032  25.00   M   12      1       2 15033  30.00   F   10      1       2 15034  22.00   M   10      1       2 15035  26.00   M   10      1       2 15036  22.00   M   10      1       2 15037  30.00   M   10      1       2 15038   9.00   M    9      1       1 15039  10.00   M   12      1       1 15040  21.00   M   10      1       2 15041  39.00   M   10      1       2 15042   4.00   M    9      1       1 15043  32.00   F    9      1       2 15044  35.00   M    9      1       2 15045  65.00   F    9      1       3 15046  24.00   F    9      1       2 15047  35.00   F    9      1       2 15048  39.00   M   27      1       2 15049  45.00   M   12      1       2 15050  50.00   F   10      1       2 15051  48.00   F   10      1       2 15052  18.00   M   10      1       2 15053   6.00   F   10      1       1 15054  30.00   F    8      1       2 15055  35.00   F   12      1       2 15056   5.00   F   10      1       1 15057  30.00   M   10      1       2 15058  15.00   M   15      1       1 15059  13.00   F   10      1       1 15060   5.00   M   12      1       1 15061  30.00   F   15      1       2 15062  12.00   M   15      1       1 15063  37.00   F   10      1       2 15064  27.00   M   10      1       2 15065   4.00   F   10      1       1 15066  40.00   M   17      1       2 15067  24.00   M   17      1       2 15068  32.00   M   10      1       2 15069  30.00   M   10      1       2 15070  10.00   M   17      1       1 15071  35.00   M   12      1       2 15072  26.00   F   17      1       2 15073  33.00   F   12      1       2 15074  35.00   M   12      1       2 15075  11.00   M   12      1       1 15076  39.00   F   17      1       2 15077  48.00   F   12      1       2 15078   2.00   F   12      1       1 15079   5.00   M   15      1       1 15080  28.00   M   10      1       2 15081   6.00   M   12      1       1 15082  20.00   M   17      1       2 15083  25.00   F   12      1       2 15084  38.00   M   15      1       2 15085  13.00   M   15      1       1 15086  33.00   F   12      1       2 15087  35.00   M   12      1       2 15088  37.00   F   12      1       2 15089  32.00   F   12      1       2 15090  15.00   M   12      1       1 15091  42.00   M   12      1       2 15092  24.00   M   14      1       2 15093  25.00   F   10      1       2 15094  55.00   M   10      1       2 15095   8.00   M   12      1       1 15096  35.00   F   12      1       2 15097  38.00   M   10      1       2 15098  40.00   M   17      1       2 15099  50.00   M   17      1       2 15100  55.00   M    7      1       2 15101  32.00   F   14      1       2 15102  23.00   F   12      1       2 15103  40.00   F   12      1       2 15104  27.00   F   11      1       2 15105  38.00   M    5      1       2 15106  45.00   M   22      1       2 15107  43.00   M   11      1       2 15108  30.00   M   11      1       2 15109  51.00   M   22      1       2 15110  40.00   M    9      1       2 15111  36.00   F   11      1       2 15112  32.00   M   11      1       2 15113  35.00   M   15      1       2 15114  38.00   M    3      1       2 15115  39.00   M   15      1       2 15116  32.00   M   10      1       2 15117  35.00   M   10      1       2 15118  43.00   F   11      1       2 15119  59.00   F   15      1       2 15120  28.00   M   15      1       2 15121  35.00   M   14      1       2 15122  45.00   M   13      1       2 15123  37.00   M   14      1       2 15124  11.00   F   20      1       1 15125  43.00   M   18      1       2 15126  42.00   F   10      1       2 15127  61.00   F   10      1       3 15128  25.00   M   12      1       2 15129  29.00   F    9      1       2 15130  43.00   M   14      1       2 15131  35.00   M   22      1       2 15132  33.00   M   11      1       2 15133  35.00   M    5      1       2 15134  22.00   M    7      1       2 15135  11.00   F   11      1       1 15136  28.00   M    9      1       2 15137  10.00   M   13      1       1 15138  18.00   F    7      1       2 15139  34.00   F   13      1       2 15140  31.00   M    5      1       2 15141  30.00   M   13      1       2 15142  33.00   M   11      1       2 15143  27.00   M   13      1       2 15144  36.00   M   13      1       2 15145  23.00   M    9      1       2 15146  48.00   M   13      1       2 15147  37.00   M   11      1       2 15148  52.00   M    9      1       2 15149  33.00   F    7      1       2 15150  47.00   M    7      1       2 15151  66.00   M    3      1       3 15152  55.00   F    7      1       2 15153   3.00   F    9      1       1 15154   4.00   M    7      1       1 15155  63.00   M   15      1       3 15156  32.00   F    7      1       2 15157  49.00   M   22      1       2 15158  12.00   M    7      1       1 15159   5.00   F    9      1       1 15160  60.00   M   14      1       3 15161  37.00   M    9      1       2 15162  32.00   M    7      1       2 15163  23.00   M   11      1       2 15164  23.00   M    6      1       2 15165  25.00   M   22      1       2 15166  25.00   M   16      1       2 15167  40.00   M   18      1       2 15168  25.00   M   12      1       2 15169  36.00   M   11      1       2 15170  29.00   M   18      1       2 15171  45.00   M    5      1       2 15172  15.00   F   22      1       1 15173  23.00   F   22      1       2 15174  26.00   M   22      1       2 15175  35.00   F    5      1       2 15176  15.00   M   13      1       1 15177  10.00   F    5      1       1 15178  35.00   F   22      1       2 15179  30.00   M    5      1       2 15180  39.00   M   13      1       2 15181  16.00   M    5      1       1 15182  12.00   M   13      1       1 15183  27.00   M    5      1       2 15184  48.00   M   22      1       2 15185  28.00   F    5      1       2 15186  14.00   F    5      1       1 15187  42.00   M    5      1       2 15188  13.00   F   24      1       1 15189  11.00   F    5      1       1 15190  27.00   M    5      1       2 15191  28.00   M   24      1       2 15192  23.00   M    5      1       2 15193  47.00   F    5      1       2 15194  14.00   M   16      1       1 15195  49.00   M    5      1       2 15196  73.00   M   16      1       3 15197  36.00   M    5      1       2 15198  20.00   F    5      1       2 15199  30.00   F    5      1       2 15200  32.00   M   20      1       2 15201  45.00   M   20      1       2 15202  40.00   F   20      1       2 15203  28.00   F   20      1       2 15204  39.00   M   14      1       2 15205  23.00   M   13      1       2 15206  46.00   M   24      1       2 15207  36.00   F   14      1       2 15208   2.00   M    9      1       1 15209   1.00   M   12      1       1 15210  22.00   M   12      1       2 15211  54.00   M    8      1       2 15212  42.00   M   14      1       2 15213  33.00   M   14      1       2 15214  33.00   M    4      1       2 15215  53.00   M   20      1       2 15216  35.00   M   20      1       2 15217  47.00   M   17      1       2 15218  45.00   M   14      1       2 15219  55.00   M   17      1       2 15220  32.00   M   14      1       2 15221  49.00   M   20      1       2 15222  50.00   M   14      1       2 15223  52.00   M   17      1       2 15224  55.00   M   15      1       2 15225  35.00   F    7      1       2 15226  30.00   M   24      1       2 15227  64.00   F    7      1       3 15228  20.00   F    6      1       2 15229  37.00   F    9      1       2 15230  36.00   F   21      1       2 15231  35.00   M   17      1       2 15232  17.00   M   10      1       1 15233  13.00   M   10      1       1 15234  25.00   F   10      1       2 15235  40.00   F   10      1       2 15236   1.00   M   10      1       1 15237  35.00   M   10      1       2 15238  35.00   F   11      1       2 15239  35.00   M    9      1       2 15240   5.00   F   20      1       1 15241  21.00   F   10      1       2 15242  32.00   M   16      1       2 15243   7.00   M   10      1       1 15244  20.00   M   10      1       2 15245  60.00   M   20      1       3 15246  40.00   M   16      1       2 15247  38.00   M   10      1       2 15248  36.00   M   10      1       2 15249  26.00   F   10      1       2 15250  10.00   F   11      1       1 15251  24.00   M   10      1       2 15252  42.00   M   16      1       2 15253  38.00   F   16      1       2 15254  17.00   M   16      1       1 15255  12.00   F   18      1       1 15256  27.00   M   18      1       2 15257  28.00   M   15      1       2 15258  22.00   F   14      1       2 15259   2.00   M   14      1       1 15260  36.00   M   10      1       2 15261  25.00   F   10      1       2 15262  27.00   M   10      1       2 15263  20.00   F   14      1       2 15264  35.00   M   10      1       2 15265   8.00   M   10      1       1 15266  30.00   F   10      1       2 15267   7.00   F   10      1       1 15268  20.00   M   14      1       2 15269  36.00   M   16      1       2 15270  70.00   F   21      1       3 15271  70.00   F   14      1       3 15272  68.00   M    8      1       3 15273  55.00   F   11      1       2 15274  68.00   M    8      1       3 15275  26.00   M    7      1       2 15276  23.00   F   10      1       2 15277  24.00   M   10      1       2 15278  18.00   M   10      1       2 15279  68.00   F   10      1       3 15280   6.00   F   18      1       1 15281  28.00   M   18      1       2 15282  28.00   F   18      1       2 15283  30.00   M   14      1       2 15284  28.00   M   14      1       2 15285  35.00   F   10      1       2 15286  45.00   F   27      1       2 15287  27.00   F   13      1       2 15288  50.00   F   13      1       2 15289  29.00   M   29      1       2 15290  31.00   M    4      1       2 15291  30.00   M   11      1       2 15292  24.00   M    4      1       2 15293  39.00   M   11      1       2 15294  18.00   M   11      1       2 15295  20.00   M   11      1       2 15296  27.00   F   16      1       2 15297  54.00   M   16      1       2 15298  20.00   M   20      1       2 15299  22.00   M   20      1       2 15300  14.00   M   16      1       1 15301  24.00   F   16      1       2 15302  16.00   M   10      1       1 15303  18.00   M   10      1       2 15304  35.00   F   21      1       2 15305   7.00   F   14      1       1 15306  40.00   F   10      1       2 15307   3.00   F   16      1       1 15308  35.00   F    9      1       2 15309  45.00   M   14      1       2 15310  26.00   F    7      1       2 15311  12.00   M   16      1       1 15312   4.00   M   14      1       1 15313  14.00   F   25      1       1 15314  32.00   M   11      1       2 15315  12.00   M   34      1       1 15316  22.00   M   10      1       2 15317  25.00   M    9      1       2 15318  23.00   F   17      1       2 15319  39.00   M   16      1       2 15320  15.00   F   14      1       1 15321  12.00   M   17      1       1 15322   8.00   M    9      1       1 15323  40.00   M    9      1       2 15324  37.00   F    9      1       2 15325  17.00   M    9      1       1 15326  14.00   M    9      1       1 15327  33.00   M   13      1       2 15328  26.00   M   34      1       2 15329  65.00   M   13      1       3 15330   6.00   F   13      1       1 15331  31.00   M   11      1       2 15332  29.00   M   11      1       2 15333  26.00   M   21      1       2 15334  24.00   F   21      1       2 15335  11.00   M   11      1       1 15336  37.00   M   11      1       2 15337  13.00   M   11      1       1 15338   8.00   M   11      1       1 15339  20.00   M   21      1       2 15340  26.00   M   14      1       2 15341  18.00   M   13      1       2 15342  17.00   M   14      1       1 15343  50.00   F   14      1       2 15344  28.00   M    9      1       2 15345  24.00   M   11      1       2 15346  57.00   F   14      1       2 15347  25.00   M   11      1       2 15348  17.00   M    9      1       1 15349  22.00   M    9      1       2 15350  28.00   M   11      1       2 15351  21.00   M   10      1       2 15352   7.00   M   11      1       1 15353  56.00   F   14      1       2 15354   8.00   M   11      1       1 15355  27.00   F   15      1       2 15356  24.00   F    8      1       2 15357  40.00   F   21      1       2 15358  37.00   F   11      1       2 15359  18.00   F    8      1       2 15360  49.00   M   11      1       2 15361  25.00   M   11      1       2 15362  16.00   M   11      1       1 15363  58.00   F    8      1       2 15364  21.00   M    8      1       2 15365  24.00   M    8      1       2 15366  24.00   M   12      1       2 15367  10.00   F   11      1       1 15368  36.00   M   11      1       2 15369  54.00   M    7      1       2 15370  62.00   F   20      1       3 15371  55.00   F    6      1       2 15372  22.00   F    6      1       2 15373  36.00   M   11      1       2 15374  26.00   F    9      1       2 15375   5.00   F    6      1       1 15376   1.00   M    6      1       1 15377  35.00   F    9      1       2 15378  21.00   F    6      1       2 15379  55.00   M    6      1       2 15380  37.00   M    7      1       2 15381  35.00   F    7      1       2 15382  25.00   M   15      1       2 15383  65.00   M   23      1       3 15384  24.00   M   23      1       2 15385  30.00   M   23      1       2 15386  35.00   F   15      1       2 15387  28.00   M   23      1       2 15388  37.00   M   23      1       2 15389  33.00   M   19      1       2 15390  12.00   M   19      1       1 15391  18.00   M   19      1       2 15392  35.00   F   19      1       2 15393  40.00   M   19      1       2 15394  13.00   M   17      1       1 15395  16.00   F   17      1       1 15396   6.00   M   15      1       1 15397  11.00   M   15      1       1 15398  28.00   M   19      1       2 15399  12.00   F   12      1       1 15400  18.00   F   15      1       2 15401  11.00   M   12      1       1 15402  14.00   F   15      1       1 15403  35.00   F   12      1       2 15404  38.00   M   15      1       2 15405   9.00   M   17      1       1 15406  40.00   M   15      1       2 15407  14.00   F   19      1       1 15408   9.00   F   23      1       1 15409  35.00   M   16      1       2 15410  37.00   F   12      1       2 15411  19.00   M   23      1       2 15412  60.00   M   19      1       3 15413   6.00   F   19      1       1 15414  30.00   F   19      1       2 15415   9.00   M   15      1       1 15416  14.00   F   19      1       1 15417  21.00   F   15      1       2 15418   9.00   M   23      1       1 15419  12.00   M   19      1       1 15420  36.00   M    2      1       2 15421   3.00   M   12      1       1 15422  52.00   F   23      1       2 15423   8.00   F   15      1       1 15424  15.00   M   15      1       1 15425  18.00   F   19      1       2 15426  25.00   M   19      1       2 15427   1.00   F   17      1       1 15428  10.00   M   15      1       1 15429  37.00   M   17      1       2 15430  22.00   F   19      1       2 15431  32.00   M   19      1       2 15432  12.00   M   19      1       1 15433   7.00   F   19      1       1 15434  35.00   F   23      1       2 15435  10.00   M   16      1       1 15436   2.00   M   19      1       1 15437  42.00   M   16      1       2 15438  16.00   M   23      1       1 15439  30.00   F   23      1       2 15440  20.00   F   15      1       2 15441  35.00   M   23      1       2 15442  11.00   M   12      1       1 15443   3.00   M   19      1       1 15444   6.00   M   19      1       1 15445  41.00   M   23      1       2 15446   7.00   M   19      1       1 15447  30.00   M   23      1       2 15448  10.00   F   23      1       1 15449  25.00   F   23      1       2 15450  34.00   F   23      1       2 15451  35.00   M   17      1       2 15452  25.00   F   19      1       2 15453  35.00   F   19      1       2 15454  12.00   F   15      1       1 15455  12.00   F   12      1       1 15456  13.00   F   19      1       1 15457  32.00   F   23      1       2 15458   6.00   M   12      1       1 15459  17.00   M   12      1       1 15460  28.00   M   16      1       2 15461  25.00   F    9      1       2 15462  22.00   F   19      1       2 15463  39.00   M   12      1       2 15464   7.00   F   23      1       1 15465   1.00   M   30      1       1 15466  28.00   F   19      1       2 15467   3.00   M   12      1       1 15468  26.00   F   15      1       2 15469  35.00   M   14      1       2 15470  28.00   F    8      1       2 15471  48.00   M   21      1       2 15472  50.00   M    9      1       2 15473   3.00   M   10      1       1 15474   8.00   F   10      1       1 15475  32.00   M   10      1       2 15476  25.00   F   21      1       2 15477  23.00   F    9      1       2 15478  15.00   F    9      1       1 15479  27.00   M   21      1       2 15480  31.00   F    9      1       2 15481   9.00   M   21      1       1 15482   7.00   M    9      1       1 15483  34.00   M    9      1       2 15484   4.00   F   21      1       1 15485  42.00   F    9      1       2 15486  35.00   M   21      1       2 15487  42.00   F    9      1       2 15488  54.00   M   14      1       2 15489  33.00   M   14      1       2 15490  48.00   M   14      1       2 15491   7.00   M   14      1       1 15492  43.00   M   14      1       2 15493  39.00   M   14      1       2 15494  32.00   F   14      1       2 15495   9.00   M   14      1       1 15496  53.00   M   14      1       2 15497  28.00   M   14      1       2 15498  18.00   M   14      1       2 15499  21.00   F   19      1       2 15500  42.00   M   14      1       2 15501  41.00   F   14      1       2 15502  47.00   M   14      1       2 15503  29.00   F    5      1       2 15504  16.00   M    4      1       1 15505  47.00   M    4      1       2 15506  12.00   F    6      1       1 15507  42.00   M    4      1       2 15508  37.00   F   16      1       2 15509  36.00   M    9      1       2 15510  57.00   M    9      1       2 15511  46.00   M    9      1       2 15512  45.00   M   13      1       2 15513  45.00   M   13      1       2 15514  40.00   M   12      1       2 15515  25.00   M   10      1       2 15516  30.00   M    8      1       2 15517  28.00   M    4      1       2 15518  32.00   M   14      1       2 15519  52.00   M    4      1       2 15520  39.00   M    4      1       2 15521  28.00   M    4      1       2 15522  37.00   M   10      1       2 15523  43.00   M    4      1       2 15524  45.00   M    8      1       2 15525  29.00   M   12      1       2 15526  51.00   M    4      1       2 15527  38.00   M    9      1       2 15528  30.00   M   10      1       2 15529  34.00   F   22      1       2 15530  32.00   M    4      1       2 15531  42.00   M   10      1       2 15532  26.00   M    9      1       2 15533  47.00   F   20      1       2 15534  25.00   M    4      1       2 15535  25.00   M    4      1       2 15536  53.00   M    4      1       2 15537  39.00   M   12      1       2 15538  28.00   M   13      1       2 15539  27.00   M    8      1       2 15540  27.00   M    4      1       2 15541  33.00   F    8      1       2 15542  61.00   M    4      1       3 15543  50.00   M    8      1       2 15544  59.00   M    4      1       2 15545  54.00   M   10      1       2 15546  51.00   M   10      1       2 15547  21.00   M    4      1       2 15548  37.00   M   20      1       2 15549  54.00   M    6      1       2 15550  48.00   M   10      1       2 15551  25.00   F    6      1       2 15552  35.00   M    6      1       2 15553  30.00   M    9      1       2 15554  42.00   M   22      1       2 15555  29.00   F    6      1       2 15556  56.00   F   13      1       2 15557  26.00   F   13      1       2 15558  22.00   M   13      1       2 15559  51.00   M   13      1       2 15560  49.00   M   10      1       2 15561  39.00   M    6      1       2 15562  36.00   M    6      1       2 15563  23.00   M   11      1       2 15564  34.00   M    8      1       2 15565  33.00   M    9      1       2 15566  33.00   F    6      1       2 15567  19.00   M   10      1       2 15568  30.00   M   13      1       2 15569  34.00   M   11      1       2 15570  43.00   M   10      1       2 15571  29.00   M    8      1       2 15572  42.00   M   20      1       2 15573  26.00   M   13      1       2 15574  32.00   M    8      1       2 15575  22.00   M   13      1       2 15576  27.00   M    8      1       2 15577  53.00   M    6      1       2 15578  43.00   F   10      1       2 15579  45.00   M   13      1       2 15580  40.00   M    9      1       2 15581  19.00   M    7      1       2 15582  17.00   F   10      1       1 15583  44.00   M   13      1       2 15584  35.00   M   11      1       2 15585  42.00   M   10      1       2 15586  27.00   M   13      1       2 15587  41.00   M   22      1       2 15588  30.00   M   13      1       2 15589  35.00   M   13      1       2 15590  31.00   M   13      1       2 15591  20.00   M   22      1       2 15592  30.00   F   14      1       2 15593   9.00   F    4      1       1 15594  38.00   M   16      1       2 15595  32.00   M    8      1       2 15596  36.00   M   10      1       2 15597  54.00   M   10      1       2 15598  19.00   M   12      1       2 15599  19.00   M   29      1       2 15600  60.00   M   21      1       3 15601  55.00   F   21      1       2 15602  54.00   M   27      1       2 15603  51.00   F   27      1       2 15604  21.00   M   27      1       2 15605  18.00   M   27      1       2 15606  20.00   F   27      1       2 15607  55.00   F   27      1       2 15608  24.00   M   27      1       2 15609  32.00   M   27      1       2 15610  38.00   M   20      1       2 15611  38.00   M   20      1       2 15612  24.00   M   27      1       2 15613  46.00   M   27      1       2 15614  23.00   M   13      1       2 15615  25.00   M   21      1       2 15616  25.00   M   10      1       2 15617  27.00   M    7      1       2 15618  24.00   F    2      1       2 15619  18.00   M    9      1       2 15620  30.00   F   19      1       2 15621  28.00   M   19      1       2 15622  68.00   M   12      1       3 15623  23.00   M   28      1       2 15624  52.00   M    9      1       2 15625   2.00   M    6      1       1 15626  41.00   M    6      1       2 15627   2.00   M    6      1       1 15628  37.00   F    6      1       2 15629  48.00   M   10      1       2 15630  11.00   F    7      1       1 15631  18.00   M    7      1       2 15632  55.00   M    7      1       2 15633  25.00   M    7      1       2 15634  47.00   M    6      1       2 15635  47.00   M   15      1       2 15636  37.00   M    6      1       2 15637  34.00   F    7      1       2 15638  36.00   M    6      1       2 15639  22.00   M    6      1       2 15640  20.00   F    6      1       2 15641  48.00   M    7      1       2 15642  52.00   M    6      1       2 15643  13.00   M    6      1       1 15644   6.00   M    6      1       1 15645  16.00   M    6      1       1 15646  16.00   M    6      1       1 15647  21.00   F   13      1       2 15648  19.00   M    6      1       2 15649  35.00   F    7      1       2 15650  34.00   F    7      1       2 15651  40.00   M    6      1       2 15652  34.00   M    7      1       2 15653  30.00   F    6      1       2 15654  45.00   M    7      1       2 15655  40.00   F    6      1       2 15656  32.00   M    6      1       2 15657  27.00   M    7      1       2 15658  11.00   F    6      1       1 15659  13.00   M    6      1       1 15660  14.00   M    6      1       1 15661  30.00   M    6      1       2 15662  20.00   M    6      1       2 15663  18.00   F   10      1       2 15664  45.00   F   11      1       2 15665  21.00   F   20      1       2 15666  12.00   F   15      1       1 15667  37.00   M   11      1       2 15668  25.00   F   11      1       2 15669  28.00   M   11      1       2 15670   5.00   F    9      1       1 15671  35.00   M   11      1       2 15672  13.00   M    9      1       1 15673  16.00   M   11      1       1 15674  48.00   M   11      1       2 15675  19.00   M   12      1       2 15676  22.00   M   12      1       2 15677  20.00   F   12      1       2 15678  12.00   M   15      1       1 15679  60.00   F   22      1       3 15680  30.00   M    9      1       2 15681   8.00   F   22      1       1 15682  38.00   F   25      1       2 15683  10.00   F   15      1       1 15684  52.00   F    9      1       2 15685  36.00   F   11      1       2 15686  13.00   F   15      1       1 15687  14.00   F   11      1       1 15688  24.00   M   11      1       2 15689  11.00   F   15      1       1 15690  40.00   F   11      1       2 15691  50.00   M   11      1       2 15692  36.00   F   11      1       2 15693  20.00   M   11      1       2 15694  35.00   F   12      1       2 15695  28.00   M   11      1       2 15696  30.00   F   17      1       2 15697  13.00   F    9      1       1 15698  25.00   M   12      1       2 15699  37.00   M   12      1       2 15700  52.00   M   20      1       2 15701  44.00   M    7      1       2 15702  37.00   F   11      1       2 15703  25.00   M   11      1       2 15704  29.00   M   11      1       2 15705  40.00   F   11      1       2 15706  17.00   M   11      1       1 15707  18.00   M   11      1       2 15708  18.00   M   20      1       2 15709  36.00   M   11      1       2 15710  28.00   M   11      1       2 15711  40.00   M   11      1       2 15712  23.00   M   12      1       2 15713  27.00   F   11      1       2 15714  44.00   M   11      1       2 15715  38.00   M   11      1       2 15716  38.00   F   11      1       2 15717  48.00   M   11      1       2 15718  20.00   F    7      1       2 15719  14.00   F    7      1       1 15720  29.00   M   15      1       2 15721   8.00   F    9      1       1 15722  48.00   M   11      1       2 15723  51.00   M   12      1       2 15724   6.00   M    9      1       1 15725  11.00   M    9      1       1 15726  10.00   M    9      1       1 15727  47.00   F   11      1       2 15728  30.00   F    7      1       2 15729  18.00   M    7      1       2 15730  36.00   F   22      1       2 15731  30.00   F    7      1       2 15732  11.00   M    7      1       1 15733  42.00   M    7      1       2 15734  22.00   M   22      1       2 15735  27.00   M    7      1       2 15736  15.00   F   10      1       1 15737  18.00   F   10      1       2 15738  60.00   M   10      1       3 15739  32.00   M   12      1       2 15740  33.00   M   12      1       2 15741  23.00   M   12      1       2 15742  25.00   M   11      1       2 15743   7.00   M   12      1       1 15744  13.00   F   11      1       1 15745  31.00   M   15      1       2 15746  36.00   M   12      1       2 15747  30.00   F    9      1       2 15748   8.00   M   10      1       1 15749  13.00   F   11      1       1 15750   5.00   M   11      1       1 15751  28.00   F    7      1       2 15752  45.00   F   32      1       2 15753   3.00   M    9      1       1 15754   3.00   M   15      1       1 15755  36.00   M   12      1       2 15756  37.00   M   15      1       2 15757  56.00   F   12      1       2 15758  26.00   M    5      1       2 15759   8.00   M   12      1       1 15760  30.00   M    8      1       2 15761  33.00   M    7      1       2 15762  36.00   M   14      1       2 15763  32.00   F   12      1       2 15764  13.00   F   10      1       1 15765  46.00   M   10      1       2 15766  35.00   M   11      1       2 15767  39.00   M   11      1       2 15768  27.00   M    5      1       2 15769  45.00   F    5      1       2 15770  18.00   M    5      1       2 15771   2.00   F    5      1       1 15772   1.00   F    5      1       1 15773  22.00   M    5      1       2 15774  16.00   F    5      1       1 15775   6.00   M    5      1       1 15776  30.00   M    5      1       2 15777  14.00   M    5      1       1 15778  55.00   M   14      1       2 15779  21.00   M   18      1       2 15780  29.00   F   14      1       2 15781  35.00   M   18      1       2 15782  36.00   M   18      1       2 15783  50.00   M   14      1       2 15784  48.00   M   14      1       2 15785  47.00   F   18      1       2 15786  57.00   M   14      1       2 15787  14.00   M   14      1       1 15788  33.00   F   18      1       2 15789  43.00   M   18      1       2 15790  76.00   F   18      1       3 15791  58.00   M    7      1       2 15792  36.00   M    7      1       2 15793  52.00   M    4      1       2 15794  24.00   M   20      1       2 15795  60.00   F    7      1       3 15796  48.00   M    7      1       2 15797  43.00   M    1      1       2 15798  43.00   M   20      1       2 15799  57.00   M   16      1       2 15800  35.00   M   10      1       2 15801  22.00   F   18      1       2 15802  53.00   F    8      1       2 15803  32.00   F   16      1       2 15804  60.00   F   14      1       3 15805  62.00   M    7      1       3 15806  46.00   F    7      1       2 15807  17.00   F    9      1       1 15808  62.00   M    9      1       3 15809  22.00   M    9      1       2 15810  25.00   F    9      1       2 15811  10.00   F    9      1       1 15812   8.00   M    9      1       1 15813  29.00   F    9      1       2 15814  49.00   M    9      1       2 15815  17.00   M    7      1       1 15816  41.00   F    7      1       2 15817  55.00   M   10      1       2 15818  25.00   M   10      1       2 15819  26.00   M    7      1       2 15820  22.00   M   23      1       2 15821  23.00   M   20      1       2 15822  17.00   M    7      1       1 15823  25.00   M   10      1       2 15824  27.00   F    7      1       2 15825  30.00   M    7      1       2 15826  20.00   F   14      1       2 15827  24.00   M   10      1       2 15828  22.00   M   10      1       2 15829  45.00   M   19      1       2 15830  25.00   M    7      1       2 15831  32.00   M   14      1       2 15832   9.00   F    7      1       1 15833  36.00   M    5      1       2 15834   2.00   F    5      1       1 15835  12.00   F   25      1       1 15836  31.00   M   14      1       2 15837  56.00   M   14      1       2 15838  46.00   M    9      1       2 15839  12.00   F   14      1       1 15840   9.00   M   23      1       1 15841   5.00   M   19      1       1 15842  44.00   M   19      1       2 15843  17.00   M   14      1       1 15844  13.00   M   14      1       1 15845  31.00   M   14      1       2 15846  27.00   M    7      1       2 15847  58.00   M    5      1       2 15848  12.00   M    7      1       1 15849  21.00   M    3      1       2 15850  40.00   M    3      1       2 15851  28.00   F    6      1       2 15852  50.00   M    3      1       2 15853   8.00   F    3      1       1 15854  35.00   F    3      1       2 15855  25.00   M    3      1       2 15856  24.00   M   19      1       2 15857  24.00   M    3      1       2 15858  28.00   M    3      1       2 15859  16.00   F    3      1       1 15860  23.00   F    3      1       2 15861   9.00   F    3      1       1 15862  27.00   F    3      1       2 15863  16.00   M    3      1       1 15864  23.00   M    3      1       2 15865  30.00   M    3      1       2 15866  35.00   M    3      1       2 15867  35.00   F    3      1       2 15868  26.00   F   12      1       2 15869  23.00   M    4      1       2 15870  20.00   F   15      1       2 15871  39.00   M    3      1       2 15872  31.00   F    4      1       2 15873  20.00   F    6      1       2 15874   2.00   F    9      1       1 15875  39.00   M   11      1       2 15876  22.00   M    9      1       2 15877  61.00   M    7      1       3 15878  25.00   M    3      1       2 15879  42.00   M    3      1       2 15880  30.00   F    3      1       2 15881  52.00   M    7      1       2 15882  24.00   M   12      1       2 15883  32.00   M   12      1       2 15884  38.00   M   12      1       2 15885  11.00   M    7      1       1 15886  40.00   M    7      1       2 15887  33.00   M   12      1       2 15888  33.00   M    7      1       2 15889  39.00   M    9      1       2 15890  13.00   M   11      1       1 15891  47.00   M   20      1       2 15892  32.00   M    9      1       2 15893  24.00   M   21      1       2 15894  32.00   M    7      1       2 15895  26.00   F    7      1       2 15896  32.00   M    8      1       2 15897  39.00   F    9      1       2 15898  43.00   M    9      1       2 15899  42.00   M    6      1       2 15900  29.00   M   11      1       2 15901  46.00   M    7      1       2 15902  41.00   F   20      1       2 15903  36.00   F    9      1       2 15904  46.00   M    7      1       2 15905  50.00   M    7      1       2 15906  43.00   M    7      1       2 15907  49.00   M    8      1       2 15908  36.00   M    7      1       2 15909  16.00   F   11      1       1 15910  40.00   M    9      1       2 15911  30.00   F   11      1       2 15912  49.00   M   10      1       2 15913  32.00   M    8      1       2 15914  55.00   M   15      1       2 15915  48.00   M    9      1       2 15916  28.00   F    7      1       2 15917  28.00   F    9      1       2 15918  47.00   M   15      1       2 15919  42.00   F    5      1       2 15920  33.00   F    5      1       2 15921  40.00   M   11      1       2 15922  41.00   M    5      1       2 15923  42.00   M    8      1       2 15924   3.00   M    7      1       1 15925  41.00   M   20      1       2 15926  33.00   M    9      1       2 15927  37.00   F    9      1       2 15928  44.00   M   12      1       2 15929  54.00   M    3      1       2 15930  33.00   M    9      1       2 15931  41.00   M    8      1       2 15932  44.00   M   20      1       2 15933  56.00   F    7      1       2 15934  28.00   M    7      1       2 15935  49.00   M   11      1       2 15936  57.00   M   11      1       2 15937  11.00   M   12      1       1 15938  46.00   M   11      1       2 15939  39.00   M    5      1       2 15940  34.00   F    7      1       2 15941  56.00   M    7      1       2 15942  47.00   F    5      1       2 15943  33.00   M    7      1       2 15944  33.00   M    7      1       2 15945  30.00   F    7      1       2 15946  20.00   M    7      1       2 15947  19.00   M    9      1       2 15948  47.00   M   11      1       2 15949  30.00   M    9      1       2 15950  48.00   F   12      1       2 15951  18.00   M   11      1       2 15952  44.00   M   18      1       2 15953  55.00   M    5      1       2 15954  42.00   M   11      1       2 15955  44.00   M    9      1       2 15956  36.00   M    7      1       2 15957  24.00   M    8      1       2 15958  35.00   M   11      1       2 15959  20.00   M    7      1       2 15960  37.00   M    7      1       2 15961  54.00   M    7      1       2 15962  16.00   M   11      1       1 15963  44.00   M   10      1       2 15964  26.00   M    5      1       2 15965  51.00   M   11      1       2 15966  55.00   M   11      1       2 15967  39.00   M    7      1       2 15968  48.00   M   12      1       2 15969  42.00   M   11      1       2 15970  35.00   M   11      1       2 15971  40.00   F    5      1       2 15972   6.00   M   13      1       1 15973  42.00   M    5      1       2 15974  54.00   M   13      1       2 15975  18.00   M    9      1       2 15976  46.00   M   18      1       2 15977  25.00   M    7      1       2 15978  37.00   M   11      1       2 15979  42.00   M    5      1       2 15980  43.00   M    9      1       2 15981  42.00   M    7      1       2 15982  44.00   M    5      1       2 15983  61.00   M    7      1       3 15984  13.00   M   11      1       1 15985  40.00   F   11      1       2 15986  38.00   M   11      1       2 15987  41.00   M   11      1       2 15988  20.00   M   11      1       2 15989  58.00   M    5      1       2 15990  33.00   M    8      1       2 15991  19.00   M    7      1       2 15992  22.00   M    7      1       2 15993  18.00   F   11      1       2 15994  48.00   M    7      1       2 15995  22.00   M    7      1       2 15996  18.00   M   13      1       2 15997  37.00   F    8      1       2 15998  40.00   M   11      1       2 15999  22.00   M   11      1       2 16000  16.00   F    7      1       1 16001  41.00   M   13      1       2 16002  58.00   F    8      1       2 16003  26.00   F   11      1       2 16004  35.00   M   21      1       2 16005  27.00   M   12      1       2 16006  32.00   F   21      1       2 16007  42.00   M    8      1       2 16008  45.00   M   10      1       2 16009  45.00   M    3      1       2 16010  48.00   M    7      1       2 16011  41.00   M   11      1       2 16012  52.00   M   20      1       2 16013  42.00   M   12      1       2 16014  29.00   M   11      1       2 16015  26.00   M    8      1       2 16016  40.00   M   20      1       2 16017  48.00   M    9      1       2 16018  52.00   M    7      1       2 16019  18.00   F   20      1       2 16020  38.00   M    9      1       2 16021  41.00   M    7      1       2 16022  28.00   M   12      1       2 16023  33.00   F   13      1       2 16024  29.00   F    7      1       2 16025  39.00   F    3      1       2 16026  32.00   M    7      1       2 16027  41.00   M    7      1       2 16028  48.00   M    8      1       2 16029  55.00   M   12      1       2 16030  30.00   M    5      1       2 16031  26.00   M    5      1       2 16032   9.00   F    7      1       1 16033  37.00   M    8      1       2 16034  32.00   F    7      1       2 16035  42.00   M   10      1       2 16036  55.00   M    9      1       2 16037  33.00   M    5      1       2 16038   2.00   M    6      1       1 16039  32.00   F    6      1       2 16040  42.00   M    6      1       2 16041  28.00   F    5      1       2 16042  34.00   F    5      1       2 16043  50.00   M    8      1       2 16044  47.00   M    7      1       2 16045  40.00   M    5      1       2 16046  32.00   M   12      1       2 16047  26.00   M    9      1       2 16048  60.00   M   11      1       3 16049  38.00   M    9      1       2 16050  31.00   M   11      1       2 16051  28.00   M    9      1       2 16052  46.00   F    5      1       2 16053  32.00   F    5      1       2 16054  22.00   M    7      1       2 16055  41.00   M    7      1       2 16056  42.00   M   13      1       2 16057  32.00   M    9      1       2 16058  31.00   M    7      1       2 16059  52.00   M   13      1       2 16060  28.00   F    5      1       2 16061  33.00   M   11      1       2 16062  53.00   M    7      1       2 16063  37.00   F    9      1       2 16064  33.00   M    5      1       2 16065  25.00   F    9      1       2 16066  33.00   M    8      1       2 16067  21.00   M    7      1       2 16068  32.00   M    9      1       2 16069  33.00   M    9      1       2 16070  46.00   M    3      1       2 16071  47.00   M   11      1       2 16072  38.00   M   13      1       2 16073  18.00   M    6      1       2 16074  24.00   M    7      1       2 16075  18.00   M    7      1       2 16076  43.00   F   11      1       2 16077  39.00   M    7      1       2 16078  26.00   M    3      1       2 16079  60.00   M    6      1       3 16080  30.00   M    4      1       2 16081  36.00   M   12      1       2 16082   8.00   F    4      1       1 16083  19.00   M    4      1       2 16084  28.00   M    4      1       2 16085  45.00   M    4      1       2 16086  34.00   M    4      1       2 16087  45.00   F    4      1       2 16088  51.00   M    4      1       2 16089  35.00   M    4      1       2 16090  35.00   F    4      1       2 16091  69.00   M   11      1       3 16092  56.00   M    9      1       2 16093  39.00   M    4      1       2 16094  70.00   F    4      1       3 16095  65.00   F   15      1       3 16096  47.00   F    4      1       2 16097  58.00   F    8      1       2 16098  60.00   F    4      1       3 16099  58.00   M   19      1       2 16100  25.00   F    4      1       2 16101  70.00   M    6      1       3 16102  58.00   F   12      1       2 16103  70.00   M    4      1       3 16104  25.00   F   11      1       2 16105  33.00   M    7      1       2 16106  64.00   M    7      1       3 16107  36.00   F   10      1       2 16108  60.00   F    7      1       3 16109  35.00   F   10      1       2 16110  42.00   F    7      1       2 16111  29.00   M   13      1       2 16112  61.00   M   24      1       3 16113  27.00   F    8      1       2 16114  47.00   F    8      1       2 16115   4.00   M   12      1       1 16116  46.00   F   10      1       2 16117  45.00   F   18      1       2 16118  24.00   F   27      1       2 16119  24.00   M   10      1       2 16120  23.00   M   20      1       2 16121  42.00   M   32      1       2 16122  32.00   M   17      1       2 16123  45.00   F   27      1       2 16124  28.00   M   10      1       2 16125  37.00   F    5      1       2 16126  18.00   F    9      1       2 16127  40.00   M   14      1       2 16128  28.00   M    9      1       2 16129  60.00   F    9      1       3 16130   4.00   M   14      1       1 16131  50.00   M    9      1       2 16132  45.00   F    9      1       2 16133  46.00   M    9      1       2 16134  42.00   M   11      1       2 16135  25.00   M   16      1       2 16136  18.00   M    9      1       2 16137  16.00   M    9      1       1 16138   8.00   M   10      1       1 16139  29.00   M    9      1       2 16140  27.00   F    6      1       2 16141   7.00   F    9      1       1 16142  25.00   M   14      1       2 16143  20.00   M   11      1       2 16144  38.00   M   31      1       2 16145  16.00   F   11      1       1 16146  24.00   M   31      1       2 16147  56.00   F    9      1       2 16148   3.00   M   16      1       1 16149  60.00   F   10      1       3 16150   6.00   F    9      1       1 16151  19.00   M   11      1       2 16152   6.00   M   11      1       1 16153   4.00   M   11      1       1 16154  28.00   F    6      1       2 16155  42.00   M   11      1       2 16156  50.00   F   28      1       2 16157  35.00   F   10      1       2 16158  38.00   F   11      1       2 16159   5.00   M   11      1       1 16160  17.00   F   11      1       1 16161  14.00   M    9      1       1 16162  30.00   M    9      1       2 16163  11.00   M    9      1       1 16164  99.00   F   11      1       3 16165  35.00   M    9      1       2 16166   5.00   M    9      1       1 16167  30.00   F   20      1       2 16168  29.00   M   11      1       2 16169  12.00   M   11      1       1 16170  10.00   M    9      1       1 16171  35.00   M    9      1       2 16172  22.00   M   14      1       2 16173   5.00   F   11      1       1 16174  24.00   F   11      1       2 16175   4.00   F   11      1       1 16176  25.00   F   11      1       2 16177   1.00   F    9      1       1 16178  38.00   M    9      1       2 16179  29.00   M    9      1       2 16180  40.00   M    9      1       2 16181  30.00   M   11      1       2 16182  45.00   M    9      1       2 16183   2.00   M    9      1       1 16184  50.00   F    9      1       2 16185  36.00   F   11      1       2 16186   7.00   M   31      1       1 16187  13.00   F   11      1       1 16188  26.00   M   11      1       2 16189  35.00   M   31      1       2 16190  30.00   M    9      1       2 16191  25.00   F   11      1       2 16192  20.00   M   11      1       2 16193  30.00   M   11      1       2 16194  32.00   M   11      1       2 16195  18.00   F   11      1       2 16196   7.00   F   11      1       1 16197  28.00   M   11      1       2 16198  35.00   M   13      1       2 16199  24.00   F   13      1       2 16200  26.00   F   11      1       2 16201  32.00   M   11      1       2 16202  49.00   F   11      1       2 16203  30.00   M   11      1       2 16204  52.00   M    9      1       2 16205  20.00   M   17      1       2 16206  38.00   M    9      1       2 16207  22.00   F   11      1       2 16208  25.00   M   11      1       2 16209  40.00   F   11      1       2 16210  22.00   F   11      1       2 16211  45.00   M   11      1       2 16212  40.00   F   11      1       2 16213  18.00   M   11      1       2 16214  35.00   F   11      1       2 16215  28.00   M   11      1       2 16216  31.00   M   36      1       2 16217  27.00   M   11      1       2 16218  15.00   F   11      1       1 16219  12.00   F   11      1       1 16220  39.00   M    9      1       2 16221  20.00   F    9      1       2 16222  30.00   F    9      1       2 16223   7.00   F    9      1       1 16224  26.00   M   22      1       2 16225  23.00   F    9      1       2 16226  20.00   M    9      1       2 16227  25.00   F   21      1       2 16228  24.00   F   19      1       2 16229  48.00   F    6      1       2 16230  45.00   F    6      1       2 16231  27.00   M   21      1       2 16232  21.00   F    9      1       2 16233  23.00   F    9      1       2 16234  14.00   M   21      1       1 16235  12.00   M    8      1       1 16236   6.00   F    8      1       1 16237   9.00   M   21      1       1 16238   8.00   F   21      1       1 16239  19.00   F   11      1       2 16240  18.00   M   11      1       2 16241  17.00   M   11      1       1 16242  40.00   M   11      1       2 16243  16.00   F   11      1       1 16244  31.00   F   11      1       2 16245  35.00   M   10      1       2 16246  30.00   F   10      1       2 16247  10.00   M   10      1       1 16248   8.00   M   10      1       1 16249  30.00   M   11      1       2 16250  62.00   M   21      1       3 16251  45.00   M   11      1       2 16252  35.00   F   11      1       2 16253  21.00   F   11      1       2 16254  40.00   M    9      1       2 16255  35.00   F    9      1       2 16256   9.00   M    9      1       1 16257  12.00   M   21      1       1 16258  11.00   M   11      1       1 16259  52.00   M   11      1       2 16260  48.00   F   21      1       2 16261  15.00   F   11      1       1 16262  24.00   M   21      1       2 16263  34.00   M    8      1       2 16264  61.00   F   17      1       3 16265  50.00   F    6      1       2 16266  45.00   M    6      1       2 16267  27.00   F    6      1       2 16268  54.00   F    8      1       2 16269  36.00   M    8      1       2 16270   2.00   F   14      1       1 16271  40.00   M   10      1       2 16272  25.00   F   10      1       2 16273  30.00   M   11      1       2 16274  38.00   M    8      1       2 16275  42.00   F   11      1       2 16276  29.00   M    6      1       2 16277  28.00   M    7      1       2 16278  32.00   M    7      1       2 16279  29.00   M    7      1       2 16280  40.00   M   15      1       2 16281  35.00   M   13      1       2 16282  10.00   M    9      1       1 16283   8.00   F    6      1       1 16284  23.00   M    9      1       2 16285  35.00   F   11      1       2 16286  10.00   M   11      1       1 16287   8.00   M   21      1       1 16288  24.00   M   11      1       2 16289  30.00   M   11      1       2 16290  55.00   F   11      1       2 16291  55.00   F    6      1       2 16292  34.00   F    9      1       2 16293  17.00   M   21      1       1 16294  34.00   M   21      1       2 16295  12.00   M    8      1       1 16296   6.00   M    8      1       1 16297   5.00   F    8      1       1 16298  14.00   M    8      1       1 16299  55.00   F   11      1       2 16300  36.00   F   11      1       2 16301   2.00   M    8      1       1 16302   8.00   M   11      1       1 16303  21.00   M   11      1       2 16304  18.00   M   11      1       2 16305  20.00   M   21      1       2 16306  20.00   M   11      1       2 16307  24.00   M   21      1       2 16308  34.00   M   11      1       2 16309  58.00   M   10      1       2 16310  20.00   M   21      1       2 16311   7.00   M   16      1       1 16312  35.00   M   16      1       2 16313  50.00   M   11      1       2 16314  30.00   M   11      1       2 16315  35.00   M   11      1       2 16316  58.00   M   11      1       2 16317  26.00   M    8      1       2 16318  37.00   F   11      1       2 16319  29.00   M   11      1       2 16320   6.00   M    8      1       1 16321   2.00   M    8      1       1 16322  23.00   F   19      1       2 16323  29.00   M   11      1       2 16324  26.00   M   14      1       2 16325  30.00   M   13      1       2 16326  38.00   M    8      1       2 16327  44.00   F    8      1       2 16328  43.00   M   27      1       2 16329  36.00   M    6      1       2 16330  27.00   M    6      1       2 16331  37.00   M    6      1       2 16332  31.00   M    6      1       2 16333  35.00   F    6      1       2 16334  24.00   M    8      1       2 16335  33.00   M    6      1       2 16336  42.00   F    6      1       2 16337  13.00   F    3      1       1 16338  53.00   M   13      1       2 16339  42.00   F   13      1       2 16340  16.00   M   13      1       1 16341  29.00   M    6      1       2 16342  30.00   M   13      1       2 16343  15.00   M    6      1       1 16344  38.00   F   14      1       2 16345  49.00   M    1      1       2 16346  30.00   M   19      1       2 16347  24.00   M    6      1       2 16348  32.00   F    3      1       2 16349   6.00   F   13      1       1 16350  27.00   M   13      1       2 16351   9.00   M   13      1       1 16352  37.00   M   10      1       2 16353  50.00   M   10      1       2 16354  40.00   M    9      1       2 16355  38.00   F    6      1       2 16356  73.00   M   11      1       3 16357  34.00   M    7      1       2 16358  39.00   M    7      1       2 16359  43.00   M    4      1       2 16360  45.00   M   11      1       2 16361  49.00   M    7      1       2 16362   8.00   F    7      1       1 16363  48.00   F    7      1       2 16364  59.00   M    7      1       2 16365  55.00   F    7      1       2 16366  56.00   F    8      1       2 16367  41.00   M    8      1       2 16368  39.00   M    6      1       2 16369  26.00   F    6      1       2 16370  39.00   M    6      1       2 16371   3.00   F   11      1       1 16372  34.00   M   17      1       2 16373  61.00   M   12      1       3 16374  23.00   M   10      1       2 16375  35.00   M   11      1       2 16376   1.00   F   11      1       1 16377  48.00   F    7      1       2 16378  33.00   M    4      1       2 16379  33.00   M    7      1       2 16380  46.00   M   11      1       2 16381  12.00   F    7      1       1 16382  10.00   F    7      1       1 16383  55.00   M   11      1       2 16384  44.00   M    7      1       2 16385  10.00   M    7      1       1 16386  40.00   M    7      1       2 16387  36.00   M    7      1       2 16388  46.00   M    7      1       2 16389  13.00   F    7      1       1 16390  39.00   M   11      1       2 16391  24.00   M    7      1       2 16392  32.00   M   11      1       2 16393  20.00   F    9      1       2 16394  48.00   M   11      1       2 16395  35.00   M    7      1       2 16396  44.00   F    8      1       2 16397  31.00   M    7      1       2 16398  33.00   F    7      1       2 16399  35.00   F   11      1       2 16400  46.00   M    2      1       2 16401   6.00   M    6      1       1 16402  38.00   M    6      1       2 16403  36.00   M    4      1       2 16404   5.00   M    7      1       1 16405  10.00   M    4      1       1 16406  13.00   F    7      1       1 16407  42.00   M    7      1       2 16408   7.00   F    7      1       1 16409  13.00   F   11      1       1 16410  48.00   M   16      1       2 16411  39.00   F   11      1       2 16412  12.00   F   16      1       1 16413  17.00   F   11      1       1 16414  37.00   F   11      1       2 16415  12.00   F   11      1       1 16416  26.00   F    4      1       2 16417  45.00   M    4      1       2 16418  44.00   M    7      1       2 16419  43.00   M   11      1       2 16420  27.00   F    7      1       2 16421  37.00   M   11      1       2 16422  17.00   M   11      1       1 16423  50.00   M    7      1       2 16424  42.00   F   11      1       2 16425  37.00   M    7      1       2 16426  48.00   M   18      1       2 16427  60.00   M    7      1       3 16428  25.00   M    7      1       2 16429  33.00   M    9      1       2 16430  30.00   M   11      1       2 16431  42.00   M    7      1       2 16432  46.00   M   10      1       2 16433  46.00   M    7      1       2 16434   6.00   F    7      1       1 16435  71.00   M   11      1       3 16436   6.00   M   10      1       1 16437  36.00   F   10      1       2 16438  26.00   M   11      1       2 16439  33.00   M    6      1       2 16440  34.00   M    8      1       2 16441  31.00   M    6      1       2 16442   4.00   F    6      1       1 16443  46.00   M    8      1       2 16444  20.00   M    8      1       2 16445  34.00   F    6      1       2 16446  10.00   F    7      1       1 16447  38.00   F   12      1       2 16448  28.00   M   19      1       2 16449  52.00   F   10      1       2 16450  19.00   M    8      1       2 16451  33.00   M   11      1       2 16452  25.00   F    6      1       2 16453  48.00   F    6      1       2 16454  45.00   M    6      1       2 16455  45.00   F   10      1       2 16456  34.00   F    8      1       2 16457  13.00   M    6      1       1 16458  36.00   M    6      1       2 16459  31.00   M    6      1       2 16460  48.00   M    4      1       2 16461  43.00   F    5      1       2 16462  41.00   F   12      1       2 16463  46.00   M    8      1       2 16464  10.00   F    4      1       1 16465  38.00   M    4      1       2 16466  61.00   M    8      1       3 16467   1.00   M    5      1       1 16468  39.00   M   11      1       2 16469   4.00   M    7      1       1 16470  27.00   F    7      1       2 16471  19.00   M   11      1       2 16472  30.00   M   11      1       2 16473  28.00   M    2      1       2 16474  31.00   F    8      1       2 16475  64.00   M    4      1       3 16476   1.00   F   12      1       1 16477  52.00   M    8      1       2 16478  46.00   F    8      1       2 16479  26.00   F    8      1       2 16480  17.00   M   12      1       1 16481  65.00   M    8      1       3 16482  54.00   M    8      1       2 16483  47.00   F   15      1       2 16484  72.00   F   19      1       3 16485  65.00   F    9      1       3 16486  17.00   M   26      1       1 16487  20.00   F   31      1       2 16488  35.00   M   26      1       2 16489  12.00   M   15      1       1 16490  18.00   M   15      1       2 16491  26.00   M   15      1       2 16492  26.00   F   14      1       2 16493  19.00   F   13      1       2 16494  22.00   M   13      1       2 16495  44.00   F   13      1       2 16496  29.00   M   13      1       2 16497  18.00   M   13      1       2 16498   4.00   F   13      1       1 16499  10.00   F   28      1       1 16500  37.00   M   13      1       2 16501  33.00   F   14      1       2 16502   4.00   M   14      1       1 16503  45.00   M   13      1       2 16504  43.00   M   19      1       2 16505  24.00   F   31      1       2 16506  25.00   M   13      1       2 16507  18.00   M   26      1       2 16508  24.00   M   13      1       2 16509  45.00   M   13      1       2 16510  16.00   M   15      1       1 16511  22.00   M   15      1       2 16512  26.00   M   15      1       2 16513  24.00   F   19      1       2 16514   2.00   F   31      1       1 16515  28.00   F   15      1       2 16516  25.00   F   15      1       2 16517   7.00   M   15      1       1 16518   7.00   M   15      1       1 16519  51.00   M   13      1       2 16520  32.00   M   10      1       2 16521  30.00   F   10      1       2 16522  20.00   M    5      1       2 16523  20.00   M    5      1       2 16524  45.00   F    5      1       2 16525  55.00   F    5      1       2 16526  55.00   M    5      1       2 16527  19.00   F   10      1       2 16528  55.00   M   20      1       2 16529   3.00   F    9      1       1 16530  20.00   F    9      1       2 16531  10.00   F    9      1       1 16532   6.00   M   10      1       1 16533  60.00   M    9      1       3 16534  20.00   M   20      1       2 16535  25.00   M    5      1       2 16536  21.00   F    8      1       2 16537  35.00   F    5      1       2 16538  25.00   M   16      1       2 16539  20.00   F   16      1       2 16540  28.00   M    8      1       2 16541  18.00   F   21      1       2 16542  12.00   M    9      1       1 16543   9.00   M    9      1       1 16544  25.00   F   20      1       2 16545  10.00   F   20      1       1 16546  20.00   M    9      1       2 16547  35.00   F    8      1       2 16548  18.00   F    8      1       2 16549  40.00   M    9      1       2 16550  13.00   F    9      1       1 16551  25.00   M   10      1       2 16552  25.00   F    8      1       2 16553  52.00   M   10      1       2 16554  20.00   M    8      1       2 16555  18.00   M    8      1       2 16556  30.00   F   20      1       2 16557  32.00   M    8      1       2 16558  29.00   M   10      1       2 16559  71.00   F    7      1       3 16560  50.00   F    7      1       2 16561  23.00   F    6      1       2 16562  20.00   F    6      1       2 16563  46.00   F    6      1       2 16564  45.00   M    6      1       2 16565  42.00   F    6      1       2 16566  46.00   F    7      1       2 16567  24.00   F    6      1       2 16568   9.00   M    7      1       1 16569  42.00   M    7      1       2 16570  31.00   F    7      1       2 16571  36.00   F    7      1       2 16572  42.00   F    7      1       2 16573  48.00   F    7      1       2 16574  53.00   F    6      1       2 16575  44.00   M    7      1       2 16576  33.00   F   25      1       2 16577  49.00   F   25      1       2 16578  40.00   M   25      1       2 16579  26.00   M   25      1       2 16580  37.00   F   25      1       2 16581  32.00   F    9      1       2 16582  25.00   M    9      1       2 16583  57.00   F   11      1       2 16584  52.00   M   26      1       2 16585  57.00   M    9      1       2 16586  33.00   M   19      1       2 16587  50.00   F   16      1       2 16588  55.00   M   18      1       2 16589  18.00   M    9      1       2 16590  42.00   F    9      1       2 16591  48.00   F    9      1       2 16592  50.00   M   26      1       2 16593  22.00   F   26      1       2 16594   7.00   M   13      1       1 16595  17.00   F   10      1       1 16596  45.00   F   13      1       2 16597  16.00   F   13      1       1 16598  28.00   F   13      1       2 16599  38.00   F   10      1       2 16600  15.00   M   10      1       1 16601  54.00   M   10      1       2 16602  48.00   M   10      1       2 16603  13.00   M   13      1       1 16604  42.00   F   10      1       2 16605  16.00   F   10      1       1 16606  52.00   M   12      1       2 16607  37.00   M   14      1       2 16608  50.00   M    3      1       2 16609  17.00   M    3      1       1 16610  32.00   F    3      1       2 16611  52.00   M    8      1       2 16612  57.00   M   18      1       2 16613  32.00   M    7      1       2 16614  34.00   M   18      1       2 16615  33.00   M   14      1       2 16616  40.00   M    3      1       2 16617  41.00   F    3      1       2 16618  15.00   M    3      1       1 16619  40.00   F    6      1       2 16620  38.00   M   18      1       2 16621  41.00   M   18      1       2 16622  45.00   M    9      1       2 16623  14.00   M    6      1       1 16624  35.00   F   20      1       2 16625  12.00   F   19      1       1 16626  10.00   M   19      1       1 16627  50.00   M    7      1       2 16628  31.00   M   11      1       2 16629  39.00   M    7      1       2 16630  21.00   F   16      1       2 16631  24.00   F   16      1       2 16632  11.00   F   16      1       1 16633  59.00   M    9      1       2 16634  49.00   F    9      1       2 16635  14.00   M   14      1       1 16636  24.00   F   14      1       2 16637  35.00   M    8      1       2 16638  64.00   M   12      1       3 16639  61.00   F    5      1       3 16640  31.00   F    9      1       2 16641  25.00   M    5      1       2 16642  43.00   M    5      1       2 16643  24.00   M    9      1       2 16644  28.00   M    8      1       2 16645  36.00   M   12      1       2 16646  30.00   F   12      1       2 16647  20.00   M    5      1       2 16648   7.00   F    9      1       1 16649   4.00   F    5      1       1 16650  28.00   F    5      1       2 16651  36.00   M    5      1       2 16652  30.00   F    8      1       2 16653   5.00   M   27      1       1 16654  20.00   M    5      1       2 16655  10.00   F   27      1       1 16656  52.00   M    8      1       2 16657  20.00   M    9      1       2 16658  47.00   M    9      1       2 16659  20.00   M    9      1       2 16660  22.00   M    5      1       2 16661  26.00   M    5      1       2 16662  12.00   M    5      1       1 16663  25.00   M    7      1       2 16664  35.00   M    5      1       2 16665  17.00   M    5      1       1 16666  45.00   M    9      1       2 16667  24.00   M   12      1       2 16668   7.00   M    5      1       1 16669  28.00   M    5      1       2 16670  27.00   F    5      1       2 16671   6.00   F    5      1       1 16672  26.00   M    9      1       2 16673  13.00   M   13      1       1 16674   8.00   F   10      1       1 16675  28.00   F   10      1       2 16676  22.00   F   10      1       2 16677  18.00   M    9      1       2 16678  30.00   M    9      1       2 16679   6.00   F    9      1       1 16680  30.00   F    9      1       2 16681   2.00   F    8      1       1 16682  42.00   M   14      1       2 16683  50.00   M   18      1       2 16684  41.00   F   14      1       2 16685  15.00   M   14      1       1 16686  42.00   F    7      1       2 16687  32.00   F    6      1       2 16688  35.00   M    7      1       2 16689  47.00   F   18      1       2 16690   7.00   M    3      1       1 16691  42.00   M    5      1       2 16692  42.00   M    6      1       2 16693  57.00   M   10      1       2 16694  53.00   F    7      1       2 16695  52.00   M    3      1       2 16696  30.00   F   23      1       2 16697  45.00   M   20      1       2 16698   8.00   M    4      1       1 16699  23.00   M   17      1       2 16700  26.00   F   11      1       2 16701  23.00   M   10      1       2 16702  47.00   M   15      1       2 16703  38.00   F   15      1       2 16704  18.00   F    8      1       2 16705  48.00   F   15      1       2 16706  39.00   F   30      1       2 16707  10.00   F   15      1       1 16708  22.00   F   15      1       2 16709  16.00   M   16      1       1 16710  20.00   F   16      1       2 16711  36.00   M   16      1       2 16712  45.00   F   10      1       2 16713  64.00   F   19      1       3 16714  38.00   M    9      1       2 16715  34.00   F    9      1       2 16716   9.00   F    9      1       1 16717  20.00   M    9      1       2 16718  36.00   M   30      1       2 16719  23.00   F   15      1       2 16720  58.00   F    7      1       2 16721  46.00   M   17      1       2 16722  42.00   F   17      1       2 16723  14.00   M   17      1       1 16724   4.00   F   14      1       1 16725  30.00   F   17      1       2 16726  22.00   M   13      1       2 16727  42.00   F   13      1       2 16728  46.00   M   13      1       2 16729  12.00   M   17      1       1 16730  23.00   F   17      1       2 16731  14.00   M   16      1       1 16732   5.00   F   30      1       1 16733  24.00   F    9      1       2 16734  18.00   F    9      1       2 16735  38.00   F   14      1       2 16736  18.00   F   30      1       2 16737  30.00   M   30      1       2 16738  42.00   M    9      1       2 16739  27.00   F    9      1       2 16740  44.00   M    9      1       2 16741  42.00   F   14      1       2 16742  16.00   M   16      1       1 16743  20.00   M   14      1       2 16744  78.00   M   12      1       3 16745  29.00   F   16      1       2 16746  20.00   F    9      1       2 16747  31.00   M    9      1       2 16748  20.00   F   15      1       2 16749  45.00   F    9      1       2 16750  22.00   F    9      1       2 16751  20.00   F    9      1       2 16752  15.00   M   16      1       1 16753  23.00   M    9      1       2 16754  24.00   F   14      1       2 16755  42.00   F    9      1       2 16756  26.00   M    9      1       2 16757  32.00   F    9      1       2 16758  25.00   F   14      1       2 16759  71.00   F   29      1       3 16760  40.00   F   30      1       2 16761  18.00   M    9      1       2 16762  26.00   M   25      1       2 16763  10.00   M    9      1       1 16764  20.00   F   10      1       2 16765   7.00   M   12      1       1 16766  17.00   F    7      1       1 16767  30.00   F    7      1       2 16768  35.00   M   15      1       2 16769  33.00   M   12      1       2 16770  30.00   F    7      1       2 16771   8.00   M    7      1       1 16772  23.00   F    7      1       2 16773  28.00   F    7      1       2 16774  18.00   M   10      1       2 16775  27.00   M    7      1       2 16776  23.00   M   12      1       2 16777  34.00   M   10      1       2 16778  36.00   M    7      1       2 16779  30.00   M   15      1       2 16780  41.00   M    7      1       2 16781  42.00   M    7      1       2 16782  43.00   M    7      1       2 16783  29.00   M    7      1       2 16784  33.00   M    7      1       2 16785  28.00   M    7      1       2 16786  26.00   M    7      1       2 16787  22.00   M   10      1       2 16788  25.00   M    7      1       2 16789  27.00   M    7      1       2 16790  35.00   M    7      1       2 16791  25.00   M    7      1       2 16792  41.00   M   15      1       2 16793  28.00   F    7      1       2 16794   7.00   F   10      1       1 16795  12.00   M    7      1       1 16796  60.00   F   11      1       3 16797  30.00   F    7      1       2 16798  12.00   F   11      1       1 16799  30.00   M    7      1       2 16800  24.00   M   12      1       2 16801  25.00   F    7      1       2 16802  38.00   M    7      1       2 16803  32.00   F   10      1       2 16804  24.00   F    7      1       2 16805  16.00   F   10      1       1 16806  36.00   M    7      1       2 16807   6.00   M    7      1       1 16808  10.00   M   12      1       1 16809  13.00   M   10      1       1 16810  11.00   F   10      1       1 16811  35.00   F   10      1       2 16812  45.00   F    6      1       2 16813  54.00   M    6      1       2 16814  48.00   M   15      1       2 16815  34.00   M   13      1       2 16816  29.00   M   15      1       2 16817  42.00   M   15      1       2 16818  36.00   F   10      1       2 16819  44.00   M   19      1       2 16820  20.00   M    7      1       2 16821  19.00   F    7      1       2 16822  24.00   M    7      1       2 16823   3.00   M    9      1       1 16824   8.00   F    9      1       1 16825  40.00   M   20      1       2 16826  30.00   M    9      1       2 16827  25.00   F    9      1       2 16828   3.00   F   14      1       1 16829  25.00   F    9      1       2 16830   7.00   F   21      1       1 16831  36.00   M    9      1       2 16832  28.00   F    9      1       2 16833  20.00   F    9      1       2 16834  20.00   M    9      1       2 16835  48.00   M   17      1       2 16836  25.00   F   17      1       2 16837  30.00   M   21      1       2 16838  11.00   F   21      1       1 16839  21.00   M    9      1       2 16840  15.00   M    9      1       1 16841  20.00   M    7      1       2 16842  43.00   F   17      1       2 16843  28.00   M   17      1       2 16844   6.00   M   17      1       1 16845  47.00   F   21      1       2 16846  38.00   M   21      1       2 16847   5.00   F   17      1       1 16848  33.00   M   21      1       2 16849   6.00   F   14      1       1 16850  40.00   F   17      1       2 16851  17.00   F   14      1       1 16852  17.00   M   17      1       1 16853  17.00   F   19      1       1 16854  28.00   F   19      1       2 16855   8.00   F    7      1       1 16856  20.00   M    9      1       2 16857  28.00   F   19      1       2 16858  30.00   M    9      1       2 16859   5.00   F    9      1       1 16860  35.00   M    9      1       2 16861  40.00   M   12      1       2 16862  16.00   F   17      1       1 16863  16.00   F    9      1       1 16864  12.00   F   11      1       1 16865  18.00   M   15      1       2 16866  22.00   M    9      1       2 16867  58.00   F   15      1       2 16868  13.00   M   17      1       1 16869  33.00   M    9      1       2 16870  40.00   F   14      1       2 16871  12.00   M   14      1       1 16872  15.00   M   20      1       1 16873  30.00   F   15      1       2 16874  40.00   F    9      1       2 16875  23.00   M    9      1       2 16876  25.00   F    9      1       2 16877  48.00   F   15      1       2 16878  19.00   M    7      1       2 16879  42.00   M    9      1       2 16880  38.00   M   15      1       2 16881  18.00   F    7      1       2 16882   1.30   M   17      1       1 16883  30.00   M   17      1       2 16884   9.00   F   17      1       1 16885  65.00   M   19      1       3 16886  26.00   F    6      1       2 16887  43.00   M    6      1       2 16888  44.00   M    7      1       2 16889  53.00   M    5      1       2 16890  48.00   F    5      1       2 16891  26.00   M    6      1       2 16892  50.00   M   10      1       2 16893  17.00   M    5      1       1 16894  15.00   M   10      1       1 16895  28.00   M    5      1       2 16896  24.00   M    2      1       2 16897  36.00   M    2      1       2 16898  44.00   M   15      1       2 16899  35.00   M   13      1       2 16900  34.00   F    2      1       2 16901  33.00   F    2      1       2 16902  20.00   F    2      1       2 16903  52.00   M    2      1       2 16904  48.00   M    5      1       2 16905  47.00   M    2      1       2 16906  51.00   M    2      1       2 16907   6.00   M    6      1       1 16908   1.00   F    6      1       1 16909  36.00   F    6      1       2 16910  39.00   F    6      1       2 16911  16.00   M    6      1       1 16912  54.00   M    3      1       2 16913  22.00   M   18      1       2 16914  20.00   F    5      1       2 16915  36.00   M    5      1       2 16916  43.00   M    5      1       2 16917  27.00   F   18      1       2 16918  30.00   F    5      1       2 16919  13.00   M    7      1       1 16920   5.00   M   12      1       1 16921  52.00   M   17      1       2 16922  32.00   M    8      1       2 16923  55.00   M    6      1       2 16924  47.00   M    6      1       2 16925  56.00   M    8      1       2 16926  34.00   M    5      1       2 16927  33.00   F    4      1       2 16928  10.00   M    4      1       1 16929  30.00   F   10      1       2 16930  46.00   M    5      1       2 16931  61.00   M   12      1       3 16932  13.00   M   10      1       1 16933  30.00   F   32      1       2 16934  20.00   M   12      1       2 16935  30.00   F   12      1       2 16936  18.00   F   16      1       2 16937   3.00   M   12      1       1 16938  20.00   F   16      1       2 16939  50.00   M   12      1       2 16940  38.00   M   12      1       2 16941  19.00   M   15      1       2 16942  25.00   F   12      1       2 16943  22.00   M   11      1       2 16944  30.00   M   10      1       2 16945  22.00   M   10      1       2 16946  40.00   M   14      1       2 16947  27.00   F   16      1       2 16948  43.00   M   15      1       2 16949  50.00   M   10      1       2 16950   9.00   F   10      1       1 16951  25.00   F   21      1       2 16952  36.00   M   12      1       2 16953  20.00   F   11      1       2 16954   9.00   M   14      1       1 16955  25.00   M   14      1       2 16956  26.00   M   12      1       2 16957  27.00   M   14      1       2 16958  47.00   M   10      1       2 16959  24.00   F   15      1       2 16960  18.00   M   15      1       2 16961  45.00   M   15      1       2 16962  23.00   F   12      1       2 16963  27.00   M   10      1       2 16964  35.00   M   12      1       2 16965  21.00   F   16      1       2 16966  60.00   M   10      1       3 16967  25.00   M   29      1       2 16968  15.00   M   12      1       1 16969  26.00   F   10      1       2 16970  30.00   F   14      1       2 16971  34.00   M   10      1       2 16972  57.00   F   14      1       2 16973  45.00   M   15      1       2 16974  20.00   M   32      1       2 16975  22.00   M   32      1       2 16976   4.00   M   29      1       1 16977  81.00   M   14      1       3 16978  30.00   M   12      1       2 16979  55.00   F   10      1       2 16980   9.00   F   10      1       1 16981  25.00   F   10      1       2 16982  19.00   M   15      1       2 16983  24.00   F   14      1       2 16984  23.00   M   10      1       2 16985  62.00   M   10      1       3 16986  25.00   F   14      1       2 16987  12.00   F   32      1       1 16988   6.00   F   16      1       1 16989  26.00   M   14      1       2 16990  21.00   M   14      1       2 16991  44.00   F   12      1       2 16992  18.00   F   12      1       2 16993  25.00   F   13      1       2 16994  46.00   M   10      1       2 16995  30.00   M   10      1       2 16996  38.00   M    7      1       2 16997  32.00   M   10      1       2 16998  46.00   M   16      1       2 16999  24.00   M    7      1       2 17000  28.00   M   10      1       2 17001  27.00   M   12      1       2 17002  22.00   M   16      1       2 17003  25.00   M    7      1       2 17004  24.00   M    9      1       2 17005  31.00   M   16      1       2 17006  29.00   M   16      1       2 17007  26.00   M   16      1       2 17008  26.00   M   10      1       2 17009  28.00   M   16      1       2 17010  28.00   M   11      1       2 17011  24.00   M    7      1       2 17012  31.00   M   16      1       2 17013  28.00   M   17      1       2 17014  30.00   M   10      1       2 17015  27.00   M   10      1       2 17016  27.00   M   11      1       2 17017  24.00   M   16      1       2 17018  76.00   F    7      1       3 17019  30.00   M   24      1       2 17020  12.00   M   24      1       1 17021  50.00   F    9      1       2 17022  52.00   F    9      1       2 17023  38.00   F   15      1       2 17024  42.00   M   15      1       2 17025  34.00   F   24      1       2 17026  24.00   F   15      1       2 17027  32.00   M   15      1       2 17028  50.00   F   24      1       2 17029  32.00   M   24      1       2 17030  60.00   M   12      1       3 17031  24.00   M   15      1       2 17032  54.00   F    9      1       2 17033  34.00   F   24      1       2 17034  38.00   F   24      1       2 17035  68.00   F   24      1       3 17036  40.00   M   13      1       2 17037  11.00   F    9      1       1 17038  42.00   F    7      1       2 17039  34.00   F    7      1       2 17040  40.00   M    7      1       2 17041  62.00   M    7      1       3 17042  58.00   M    7      1       2 17043  48.00   F    7      1       2 17044  66.00   F    7      1       3 17045  36.00   F    7      1       2 17046  38.00   F    7      1       2 17047  58.00   M    8      1       2 17048  48.00   M    8      1       2 17049  27.00   M    8      1       2 17050  25.00   M    8      1       2 17051  68.00   M   18      1       3 17052  56.00   F   16      1       2 17053  38.00   F   20      1       2 17054  60.00   M   15      1       3 17055  65.00   M   14      1       3 17056  50.00   M    8      1       2 17057  45.00   F    5      1       2 17058  23.00   F    5      1       2 17059  52.00   M    5      1       2 17060  60.00   M    5      1       3 17061  18.00   M   12      1       2 17062  33.00   M    8      1       2 17063  28.00   M   14      1       2 17064  20.00   F   14      1       2 17065  52.00   M   14      1       2 17066  60.00   M   13      1       3 17067  21.00   M    8      1       2 17068  18.00   M    8      1       2 17069  52.00   M   16      1       2 17070  16.00   F   20      1       1 17071  25.00   M    8      1       2 17072  35.00   F   20      1       2 17073  30.00   F   20      1       2 17074   7.00   M   10      1       1 17075  25.00   F   18      1       2 17076   4.00   F   13      1       1 17077   2.00   F   18      1       1 17078   8.00   F   10      1       1 17079  50.00   M   20      1       2 17080  43.00   F   13      1       2 17081  13.00   M   10      1       1 17082  28.00   F   20      1       2 17083  21.00   M   16      1       2 17084  33.00   M   13      1       2 17085  33.00   M   13      1       2 17086  10.00   M   20      1       1 17087   4.00   F   16      1       1 17088  35.00   F    8      1       2 17089  16.00   M    8      1       1 17090  20.00   M    8      1       2 17091  50.00   F   14      1       2 17092  30.00   M    8      1       2 17093  20.00   M    8      1       2 17094  37.00   M    8      1       2 17095  28.00   F   18      1       2 17096  32.00   M    8      1       2 17097  30.00   F   20      1       2 17098  60.00   M   13      1       3 17099  15.00   F   10      1       1 17100  10.00   F   13      1       1 17101  35.00   M    8      1       2 17102  26.00   F   18      1       2 17103  20.00   M    8      1       2 17104  15.00   F   11      1       1 17105  30.00   F   13      1       2 17106  42.00   M    6      1       2 17107  25.00   M   18      1       2 17108  25.00   M    6      1       2 17109  29.00   M   11      1       2 17110  10.00   F   18      1       1 17111  46.00   M   16      1       2 17112  10.00   F   16      1       1 17113  22.00   M    8      1       2 17114   4.00   M   16      1       1 17115  30.00   F   10      1       2 17116  20.00   M    8      1       2 17117  15.00   F   16      1       1 17118  60.00   M   16      1       3 17119  13.00   F   16      1       1 17120  30.00   F   11      1       2 17121  15.00   M   18      1       1 17122  27.00   F    8      1       2 17123  45.00   M    8      1       2 17124  39.00   F    8      1       2 17125  20.00   M    8      1       2 17126  40.00   M   13      1       2 17127  32.00   F   10      1       2 17128  18.00   M   10      1       2 17129  30.00   M    9      1       2 17130  30.00   M   16      1       2 17131  20.00   F   14      1       2 17132  30.00   M   11      1       2 17133  27.00   F   14      1       2 17134  52.00   M   11      1       2 17135  45.00   M   11      1       2 17136  50.00   M   14      1       2 17137  53.00   M   14      1       2 17138  36.00   M   11      1       2 17139  56.00   F   11      1       2 17140  22.00   M   11      1       2 17141  22.00   M   16      1       2 17142  60.00   M   16      1       3 17143  29.00   M    8      1       2 17144  60.00   M    6      1       3 17145  30.00   M    1      1       2 17146  40.00   M    5      1       2 17147  10.00   M   28      1       1 17148   8.00   M   13      1       1 17149  30.00   F   28      1       2 17150  22.00   F    7      1       2 17151  35.00   M   13      1       2 17152  32.00   M   17      1       2 17153  10.00   M   17      1       1 17154  48.00   M   13      1       2 17155  17.00   F   15      1       1 17156  35.00   M   17      1       2 17157  25.00   M   15      1       2 17158  22.00   F    7      1       2 17159  45.00   M   13      1       2 17160  38.00   M   13      1       2 17161  17.00   M    7      1       1 17162  19.00   M    7      1       2 17163  19.00   M    7      1       2 17164  15.00   M   15      1       1 17165  10.00   M   15      1       1 17166   7.00   F   15      1       1 17167  25.00   F   17      1       2 17168  50.00   F   13      1       2 17169  36.00   F   13      1       2 17170  27.00   M    7      1       2 17171  17.00   M    7      1       1 17172  20.00   M   15      1       2 17173  60.00   F   15      1       3 17174  60.00   F   22      1       3 17175  28.00   M   17      1       2 17176  27.00   M    8      1       2 17177   5.00   F   15      1       1 17178  30.00   M    9      1       2 17179   7.00   M    9      1       1 17180  13.00   F    9      1       1 17181  35.00   M   15      1       2 17182  24.00   F   15      1       2 17183  13.00   M   10      1       1 17184  20.00   F    5      1       2 17185   2.00   F    9      1       1 17186  26.00   F    9      1       2 17187  61.00   M   31      1       3 17188  29.00   F   10      1       2 17189  31.00   F   15      1       2 17190  26.00   F   10      1       2 17191   5.00   F    5      1       1 17192   3.00   F    5      1       1 17193  50.00   F    6      1       2 17194  25.00   M    7      1       2 17195  24.00   M    7      1       2 17196  23.00   F    9      1       2 17197   7.00   M    3      1       1 17198   2.00   F    6      1       1 17199   1.00   M    3      1       1 17200  10.00   M    3      1       1 17201  25.00   F    7      1       2 17202   7.00   M    9      1       1 17203  25.00   F   13      1       2 17204  15.00   F    7      1       1 17205  23.00   F   15      1       2 17206  30.00   F   10      1       2 17207  32.00   F   15      1       2 17208  56.00   F   15      1       2 17209  35.00   F   15      1       2 17210  37.00   M   15      1       2 17211  28.00   M    9      1       2 17212  42.00   F    4      1       2 17213  46.00   F    7      1       2 17214  34.00   M    7      1       2 17215  25.00   F    9      1       2 17216  32.00   M    7      1       2 17217  19.00   M    7      1       2 17218  54.00   F    7      1       2 17219  62.00   F    7      1       3 17220  43.00   M   11      1       2 17221  34.00   F   24      1       2 17222  19.00   M   14      1       2 17223  16.00   M    6      1       1 17224  23.00   M   23      1       2 17225  60.00   M   24      1       3 17226  37.00   M   23      1       2 17227  28.00   M   23      1       2 17228  43.00   M   23      1       2 17229  22.00   F   13      1       2 17230  20.00   F   24      1       2 17231  29.00   M   24      1       2 17232  54.00   M   24      1       2 17233  27.00   F   11      1       2 17234  62.00   F   15      1       3 17235  57.00   M   32      1       2 17236  24.00   F    8      1       2 17237  17.00   M   17      1       1 17238  25.00   F    7      1       2 17239  39.00   M   10      1       2 17240  40.00   F    7      1       2 17241  32.00   F   10      1       2 17242  23.00   M    5      1       2 17243  23.00   F    5      1       2 17244  21.00   F   10      1       2 17245  34.00   M   10      1       2 17246  24.00   M   10      1       2 17247  17.00   M   10      1       1 17248  32.00   M   14      1       2 17249  38.00   F   18      1       2 17250  40.00   F   14      1       2 17251  65.00   F   14      1       3 17252  30.00   F   18      1       2 17253   7.00   F   16      1       1 17254   7.00   M   16      1       1 17255  10.00   M   14      1       1 17256   9.00   F   11      1       1 17257  35.00   M   16      1       2 17258   5.00   F   14      1       1 17259  25.00   M   10      1       2 17260   6.00   F   10      1       1 17261  24.00   F   10      1       2 17262  53.00   F   10      1       2 17263  24.00   F   10      1       2 17264   7.00   M   11      1       1 17265  20.00   M   14      1       2 17266  20.00   M   14      1       2 17267  29.00   F   10      1       2 17268  25.00   F   10      1       2 17269  38.00   F   10      1       2 17270  42.00   F   14      1       2 17271  20.00   F   10      1       2 17272  15.00   F   16      1       1 17273  50.00   F   16      1       2 17274  28.00   M   16      1       2 17275  10.00   F   14      1       1 17276  25.00   F   16      1       2 17277  30.00   M   14      1       2 17278  42.00   M   16      1       2 17279  24.00   F   18      1       2 17280  30.00   F   12      1       2 17281   8.00   F   16      1       1 17282  25.00   M   10      1       2 17283  45.00   M   14      1       2 17284  10.00   M   16      1       1 17285  21.00   M   16      1       2 17286  25.00   M   14      1       2 17287  30.00   F   16      1       2 17288  31.00   M   14      1       2 17289  35.00   F   10      1       2 17290  28.00   F   14      1       2 17291  47.00   F   10      1       2 17292  23.00   F   14      1       2 17293  17.00   F   14      1       1 17294  17.00   F   14      1       1 17295  17.00   F   14      1       1 17296  20.00   M   14      1       2 17297  30.00   F   14      1       2 17298  46.00   M   14      1       2 17299  23.00   M   14      1       2 17300  17.00   M   24      1       1 17301  20.00   F   16      1       2 17302  14.00   F   16      1       1 17303  46.00   F   10      1       2 17304  22.00   M   14      1       2 17305  29.00   M   14      1       2 17306  28.00   F   16      1       2 17307  35.00   M    6      1       2 17308  13.00   F   10      1       1 17309   7.00   M   10      1       1 17310   2.00   F   13      1       1 17311  45.00   F   14      1       2 17312  33.00   M    5      1       2 17313  15.00   M    6      1       1 17314  31.00   M    7      1       2 17315  29.00   M    5      1       2 17316  59.00   M    7      1       2 17317  24.00   M   14      1       2 17318  35.00   M    5      1       2 17319  32.00   M   11      1       2 17320  58.00   M   22      1       2 17321  23.00   F   13      1       2 17322  22.00   F    7      1       2 17323  25.00   F    7      1       2 17324  55.00   F   22      1       2 17325  45.00   M   23      1       2 17326  37.00   M   12      1       2 17327  43.00   F    8      1       2 17328  43.00   M    8      1       2 17329  25.00   M   15      1       2 17330  25.00   M   15      1       2 17331  26.00   M   15      1       2 17332   9.00   F   14      1       1 17333  27.00   M    9      1       2 17334  46.00   M    9      1       2 17335  52.00   M    9      1       2 17336  10.00   M    9      1       1 17337  47.00   M    9      1       2 17338  35.00   F   15      1       2 17339  66.00   M   15      1       3 17340   9.00   M    9      1       1 17341  52.00   M    9      1       2 17342  25.00   F    9      1       2 17343  27.00   F    7      1       2 17344  38.00   M    7      1       2 17345  38.00   F    7      1       2 17346  42.00   M    7      1       2 17347  18.00   F    7      1       2 17348  62.00   F    7      1       3 17349  66.00   M    7      1       3 17350   4.00   M    7      1       1 17351  64.00   M    9      1       3 17352  45.00   F    6      1       2 17353  58.00   M   14      1       2 17354  16.00   F    6      1       1 17355  22.00   F    9      1       2 17356  46.00   M   15      1       2 17357  22.00   M    4      1       2 17358  24.00   F   16      1       2 17359   2.00   F   16      1       1 17360  25.00   M    9      1       2 17361  23.00   M    9      1       2 17362  21.00   M    9      1       2 17363   0.60   F    8      1       1 17364  21.00   F    8      1       2 17365   3.00   F    8      1       1 17366  20.00   F   31      1       2 17367   5.00   M   17      1       1 17368  60.00   M   24      1       3 17369  14.00   F    8      1       1 17370  26.00   F   11      1       2 17371  29.00   F    8      1       2 17372   6.00   F    8      1       1 17373  40.00   M   14      1       2 17374  35.00   F    8      1       2 17375  14.00   M    4      1       1 17376   5.00   M    9      1       1 17377  32.00   M   20      1       2 17378  63.00   F    8      1       3 17379  33.00   M    5      1       2 17380  24.00   M    5      1       2 17381  40.00   F    5      1       2 17382  65.00   M    8      1       3 17383  56.00   F    5      1       2 17384  25.00   M    5      1       2 17385  28.00   M    5      1       2 17386  10.00   M   16      1       1 17387  35.00   F    5      1       2 17388   7.00   F   10      1       1 17389  33.00   F   10      1       2 17390  28.00   M   10      1       2 17391   4.00   F   10      1       1 17392  23.00   M   10      1       2 17393  22.00   F   10      1       2 17394  12.00   F   10      1       1 17395  11.00   F   10      1       1 17396  25.00   M   10      1       2 17397  13.00   F   10      1       1 17398  27.00   M   10      1       2 17399  30.00   M   10      1       2 17400  20.00   M   25      1       2 17401  65.00   M   12      1       3 17402  59.00   M   14      1       2 17403  11.00   F    5      1       1 17404  35.00   M    3      1       2 17405  36.00   F   14      1       2 17406  34.00   F    5      1       2 17407  25.00   F    3      1       2 17408  23.00   F    9      1       2 17409  30.00   F    7      1       2 17410  30.00   M    3      1       2 17411  47.00   M   12      1       2 17412   5.00   F    1      1       1 17413  35.00   F    1      1       2 17414  33.00   M    4      1       2 17415  39.00   M    1      1       2 17416  60.00   M    2      1       3 17417  60.00   M    2      1       3 17418  16.00   M    1      1       1 17419  72.00   F    2      1       3 17420  41.00   F    1      1       2 17421  30.00   M    2      1       2 17422  47.00   M   14      1       2 17423  30.00   M    1      1       2 17424  34.00   M   10      1       2 17425  20.00   M   12      1       2 17426  21.00   F   10      1       2 17427  34.00   M   10      1       2 17428  24.00   M   10      1       2 17429  56.00   M    9      1       2 17430  34.00   F   11      1       2 17431  72.00   F   21      1       3 17432  37.00   M    8      1       2 17433  40.00   M   13      1       2 17434  49.00   M   11      1       2 17435  63.00   M   12      1       3 17436  62.00   F   11      1       3 17437  34.00   M   13      1       2 17438   0.50   M    8      1       1 17439  50.00   M   13      1       2 17440  46.00   M    8      1       2 17441  33.00   F    8      1       2 17442  12.00   F    8      1       1 17443  33.00   M    8      1       2 17444  75.00   F    9      1       3 17445  35.00   M    9      1       2 17446  28.00   M    8      1       2 17447  32.00   F   13      1       2 17448  38.00   M    4      1       2 17449  22.00   M    4      1       2 17450  18.00   M    4      1       2 17451  18.00   M    6      1       2 17452  17.00   M    4      1       1 17453  15.00   M    6      1       1 17454  18.00   M    4      1       2 17455  24.00   F    4      1       2 17456  54.00   F   13      1       2 17457  38.00   F   10      1       2 17458  52.00   M   21      1       2 17459  52.00   M    6      1       2 17460  43.00   M   13      1       2 17461  36.00   M   10      1       2 17462  43.00   M    7      1       2 17463  26.00   M   13      1       2 17464  25.00   M   13      1       2 17465  30.00   M   10      1       2 17466  37.00   M   13      1       2 17467  45.00   M   13      1       2 17468  28.00   M    7      1       2 17469  29.00   M    7      1       2 17470  27.00   M   13      1       2 17471  45.00   M   10      1       2 17472   2.00   M   23      1       1 17473  29.00   M   19      1       2 17474  20.00   M   13      1       2 17475  23.00   M   13      1       2 17476  23.00   M   21      1       2 17477  35.00   M   13      1       2 17478  28.00   M   13      1       2 17479  52.00   M   13      1       2 17480  10.00   M    9      1       1 17481  55.00   M    9      1       2 17482  31.00   M    7      1       2 17483  26.00   F    8      1       2 17484  16.00   M   11      1       1 17485  54.00   M   11      1       2 17486  36.00   F   11      1       2 17487  60.00   F   11      1       3 17488  36.00   M   10      1       2 17489  23.00   F   10      1       2 17490  42.00   F   11      1       2 17491  34.00   M   11      1       2 17492   7.00   M   11      1       1 17493  12.00   F   11      1       1 17494  30.00   F   11      1       2 17495  11.00   F   11      1       1 17496  23.00   F   11      1       2 17497  25.00   F   30      1       2 17498  28.00   F   11      1       2 17499  38.00   F   12      1       2 17500  27.00   M    9      1       2 17501  24.00   M    9      1       2 17502   7.00   M    9      1       1 17503  34.00   F    9      1       2 17504   4.00   M    9      1       1 17505  35.00   F    9      1       2 17506   4.00   F    9      1       1 17507  25.00   M    9      1       2 17508  55.00   M    9      1       2 17509  56.00   F    9      1       2 17510  60.00   M   13      1       3 17511  33.00   M    1      1       2 17512  34.00   F    1      1       2 17513  33.00   F    1      1       2 17514  44.00   M    1      1       2 17515  52.00   M    1      1       2 17516  17.00   F    1      1       1 17517   8.00   F    1      1       1 17518  22.00   M    1      1       2 17519  40.00   F    1      1       2 17520  48.00   M    1      1       2 17521  43.00   M    1      1       2 17522   9.00   F    1      1       1 17523  47.00   M    1      1       2 17524   9.00   M    1      1       1 17525  42.00   M    2      1       2 17526  32.00   M    6      1       2 17527  28.00   F    2      1       2 17528  34.00   F    2      1       2 17529  46.00   M   13      1       2 17530  55.00   M    1      1       2 17531  71.00   M   12      1       3 17532  28.00   M   12      1       2 17533  27.00   F   21      1       2 17534  25.00   F   12      1       2 17535  46.00   M   13      1       2 17536  20.00   F   12      1       2 17537  73.00   F   22      1       3 17538  33.00   M    6      1       2 17539  54.00   F   21      1       2 17540  32.00   M    9      1       2 17541  76.00   M   21      1       3 17542  48.00   F   21      1       2 17543  30.00   M   21      1       2 17544  24.00   F   21      1       2 17545  19.00   F   21      1       2 17546  25.00   M   21      1       2 17547  64.00   F   12      1       3 17548  40.00   M   12      1       2 17549  24.00   F   21      1       2 17550  45.00   M   12      1       2 17551  22.00   F   12      1       2 17552  42.00   F   21      1       2 17553  53.00   F    9      1       2 17554  46.00   F    7      1       2 17555  41.00   M   12      1       2 17556  59.00   F    8      1       2 17557  41.00   M    9      1       2 17558  32.00   M    9      1       2 17559  35.00   M   10      1       2 17560  31.00   M   10      1       2 17561  60.00   F   10      1       3 17562  27.00   F   10      1       2 17563  12.00   F   10      1       1 17564   8.00   F   10      1       1 17565   3.00   M   10      1       1 17566  23.00   F   14      1       2 17567  25.00   M   14      1       2 17568  23.00   M   14      1       2 17569  55.00   F   26      1       2 17570  25.00   M   11      1       2 17571  24.00   F    8      1       2 17572  21.00   M    8      1       2 17573  21.00   M   11      1       2 17574  34.00   M    8      1       2 17575  38.00   M    8      1       2 17576  29.00   F   22      1       2 17577  50.00   F   14      1       2 17578   3.00   F   11      1       1 17579  34.00   M    8      1       2 17580  59.00   M    8      1       2 17581  30.00   F    6      1       2 17582  15.00   M    6      1       1 17583  32.00   M    6      1       2 17584  30.00   M   11      1       2 17585  22.00   M    6      1       2 17586  34.00   M   11      1       2 17587  34.00   M   15      1       2 17588  40.00   M    6      1       2 17589  41.00   M    6      1       2 17590  33.00   M    9      1       2 17591  48.00   M   16      1       2 17592  31.00   M    9      1       2 17593  20.00   F    6      1       2 17594  27.00   M    6      1       2 17595  31.00   M   15      1       2 17596  32.00   M    6      1       2 17597  27.00   M   11      1       2 17598  35.00   M   15      1       2 17599  51.00   M    6      1       2 17600  20.00   F   20      1       2 17601   9.00   F   14      1       1 17602  36.00   M    6      1       2 17603  67.00   F    9      1       3 17604  44.00   M   18      1       2 17605  30.00   F    6      1       2 17606  25.00   M   15      1       2 17607  24.00   M    9      1       2 17608  44.00   M    6      1       2 17609  32.00   M    6      1       2 17610  38.00   M    6      1       2 17611  50.00   M    6      1       2 17612  33.00   M    9      1       2 17613  31.00   M   18      1       2 17614  21.00   M    8      1       2 17615  26.00   M    6      1       2 17616  32.00   F   11      1       2 17617  22.00   M    9      1       2 17618  32.00   M    9      1       2 17619  29.00   M    6      1       2 17620  29.00   M    8      1       2 17621  29.00   M   11      1       2 17622  31.00   M    6      1       2 17623  25.00   M    6      1       2 17624  47.00   M    6      1       2 17625  42.00   M    8      1       2 17626  29.00   M    6      1       2 17627  54.00   M   11      1       2 17628  44.00   M   14      1       2 17629  43.00   M    6      1       2 17630  31.00   M    6      1       2 17631  46.00   M   14      1       2 17632  61.00   M   11      1       3 17633  23.00   M    6      1       2 17634  24.00   M    6      1       2 17635  22.00   M   11      1       2 17636  35.00   M    9      1       2 17637  60.00   F   11      1       3 17638   3.00   F    9      1       1 17639  24.00   M   14      1       2 17640  36.00   M    6      1       2 17641  31.00   M   11      1       2 17642  43.00   M   15      1       2 17643  50.00   M    9      1       2 17644  35.00   M   15      1       2 17645  51.00   M   15      1       2 17646  21.00   M   14      1       2 17647  20.00   F   15      1       2 17648  20.00   F    6      1       2 17649  21.00   M   11      1       2 17650  29.00   F   15      1       2 17651  42.00   M   15      1       2 17652  30.00   M    6      1       2 17653  42.00   M    8      1       2 17654  34.00   M    8      1       2 17655  35.00   M   11      1       2 17656  50.00   M   11      1       2 17657  63.00   F    6      1       3 17658  39.00   F   15      1       2 17659  58.00   F   15      1       2 17660  24.00   M   15      1       2 17661  24.00   F    9      1       2 17662  18.00   M    6      1       2 17663  40.00   M    6      1       2 17664  10.00   M    6      1       1 17665  46.00   M    8      1       2 17666  36.00   F   15      1       2 17667  55.00   M   14      1       2 17668  43.00   F   14      1       2 17669  42.00   M   11      1       2 17670  19.00   M   11      1       2 17671  45.00   M   15      1       2 17672  45.00   M   11      1       2 17673  31.00   F   15      1       2 17674  31.00   F   15      1       2 17675  24.00   M    7      1       2 17676  23.00   M    9      1       2 17677  34.00   F    9      1       2 17678  55.00   M    8      1       2 17679   4.00   M   10      1       1 17680  48.00   F   12      1       2 17681  29.00   F   10      1       2 17682  27.00   M   11      1       2 17683  10.00   F   12      1       1 17684  28.00   F   13      1       2 17685  23.00   M   12      1       2 17686  34.00   M   12      1       2 17687  33.00   M   12      1       2 17688  31.00   F   12      1       2 17689  58.00   F    8      1       2 17690  27.00   M    8      1       2 17691   2.00   M   12      1       1 17692   5.00   F   12      1       1 17693  31.00   F   16      1       2 17694  20.00   M    8      1       2 17695  19.00   M   11      1       2 17696  44.00   F   11      1       2 17697  46.00   M   11      1       2 17698  49.00   M   12      1       2 17699  54.00   M    8      1       2 17700   5.00   F    8      1       1 17701  24.00   F   12      1       2 17702  21.00   F   12      1       2 17703  40.00   M    8      1       2 17704  35.00   M    8      1       2 17705  38.00   F    8      1       2 17706  62.00   F   22      1       3 17707  19.00   M   20      1       2 17708  18.00   M   20      1       2 17709  63.00   M   20      1       3 17710  57.00   M   20      1       2 17711  54.00   M   20      1       2 17712  36.00   M   20      1       2 17713  20.00   M   12      1       2 17714  46.00   F    8      1       2 17715  40.00   F   11      1       2 17716   1.00   M   10      1       1 17717  68.00   M   20      1       3 17718   4.00   F   20      1       1 17719  28.00   F   20      1       2 17720  30.00   F   20      1       2 17721  38.00   F   10      1       2 17722  36.00   M   14      1       2 17723  85.00   F   13      1       3 17724  79.00   M   13      1       3 17725  17.00   F   13      1       1 17726  16.00   M    7      1       1 17727  24.00   M    7      1       2 17728  12.00   F    7      1       1 17729  23.00   M   13      1       2 17730  45.00   M    9      1       2 17731  12.00   M    9      1       1 17732  33.00   M    9      1       2 17733  39.00   M   12      1       2 17734  35.00   F   12      1       2 17735  50.00   M    9      1       2 17736  21.00   M   11      1       2 17737  46.00   M    6      1       2 17738  55.00   M    6      1       2 17739  58.00   M    7      1       2 17740   9.00   M    6      1       1 17741  23.00   M    8      1       2 17742  28.00   M    6      1       2 17743  31.00   M    6      1       2 17744  25.00   M   12      1       2 17745  29.00   M    8      1       2 17746  24.00   M   12      1       2 17747  30.00   M    9      1       2 17748  40.00   M   12      1       2 17749  46.00   M    8      1       2 17750  28.00   M   12      1       2 17751  55.00   F    8      1       2 17752  30.00   F   12      1       2 17753   8.00   F   12      1       1 17754   1.00   M    6      1       1 17755  27.00   F    6      1       2 17756  22.00   F    6      1       2 17757   4.00   M   12      1       1 17758  35.00   M   20      1       2 17759  25.00   F    6      1       2 17760  61.00   F   12      1       3 17761   1.00   M    6      1       1 17762  29.00   F   12      1       2 17763  24.00   M   12      1       2 17764   2.00   F    6      1       1 17765   8.00   M   12      1       1 17766   2.00   M   12      1       1 17767  20.00   F    6      1       2 17768  12.00   M   12      1       1 17769   4.00   F   20      1       1 17770   1.00   M   20      1       1 17771  60.00   M   22      1       3 17772  42.00   M   10      1       2 17773  35.00   M    9      1       2 17774   7.00   F   14      1       1 17775   9.00   F   24      1       1 17776  29.00   F   14      1       2 17777  39.00   M   10      1       2 17778  50.00   F    8      1       2 17779  25.00   M   12      1       2 17780  37.00   F   14      1       2 17781  24.00   M   25      1       2 17782  15.00   M    9      1       1 17783   5.00   M   10      1       1 17784  35.00   M    5      1       2 17785  19.00   M   12      1       2 17786  75.00   M   12      1       3 17787  50.00   M   16      1       2 17788  21.00   F   14      1       2 17789  14.00   M   12      1       1 17790  35.00   F   20      1       2 17791  37.00   M   12      1       2 17792  50.00   F   14      1       2 17793   0.25   F   12      1       1 17794   3.00   M   12      1       1 17795   5.00   M   12      1       1 17796  24.00   M   12      1       2 17797  28.00   M   12      1       2 17798  12.00   F   12      1       1 17799   6.00   M   12      1       1 17800  10.00   M   12      1       1 17801  30.00   F   14      1       2 17802   7.00   M   12      1       1 17803  19.00   M   12      1       2 17804   8.00   F   12      1       1 17805  10.00   M   14      1       1 17806  45.00   M   12      1       2 17807  12.00   F   12      1       1 17808  13.00   F   12      1       1 17809  33.00   F   12      1       2 17810  35.00   M   12      1       2 17811  25.00   F   12      1       2 17812  22.00   M   12      1       2 17813  28.00   M   12      1       2 17814  42.00   M   12      1       2 17815  30.00   M   12      1       2 17816  38.00   F   14      1       2 17817  13.00   M   14      1       1 17818  30.00   F   12      1       2 17819  38.00   F   12      1       2 17820   5.00   F   14      1       1 17821  25.00   F   12      1       2 17822   6.00   M   12      1       1 17823  38.00   M   12      1       2 17824   3.00   F   12      1       1 17825  18.00   F   12      1       2 17826  46.00   F   14      1       2 17827  30.00   F   12      1       2 17828  12.00   M   12      1       1 17829  35.00   M   12      1       2 17830  14.00   M   12      1       1 17831  16.00   F   12      1       1 17832  32.00   F   12      1       2 17833  35.00   F   12      1       2 17834  11.00   M   12      1       1 17835  12.00   F   12      1       1 17836  25.00   M   14      1       2 17837  35.00   M   19      1       2 17838  46.00   M   14      1       2 17839  80.00   F   19      1       3 17840  11.00   M   13      1       1 17841  50.00   M   15      1       2 17842  55.00   F   12      1       2 17843  60.00   F   12      1       3 17844  75.00   M   14      1       3 17845  38.00   F   13      1       2 17846   4.00   M   13      1       1 17847  35.00   F    8      1       2 17848  26.00   F    8      1       2 17849  17.00   M    8      1       1 17850  28.00   F    8      1       2 17851  38.00   M    8      1       2 17852  35.00   F    8      1       2 17853  28.00   F    8      1       2 17854  19.00   M    8      1       2 17855   5.00   M    8      1       1 17856  50.00   M    8      1       2 17857  33.00   M    8      1       2 17858  30.00   M    8      1       2 17859  32.00   M    8      1       2 17860   8.00   F    8      1       1 17861  27.00   M    8      1       2 17862  26.00   F    8      1       2 17863  28.00   M   10      1       2 17864  32.00   M    8      1       2 17865  30.00   M    8      1       2 17866   6.00   F    8      1       1 17867   5.00   M    8      1       1 17868  16.00   F   10      1       1 17869  14.00   M   10      1       1 17870  46.00   F    8      1       2 17871  48.00   M    8      1       2 17872  17.00   M   10      1       1 17873  26.00   M    8      1       2 17874  16.00   F    8      1       1 17875  11.00   F    8      1       1 17876  45.00   M    8      1       2 17877  14.00   M    8      1       1 17878  36.00   M    8      1       2 17879  18.00   M    8      1       2 17880  38.00   M    8      1       2 17881  36.00   M   10      1       2 17882  21.00   M    8      1       2 17883   5.00   M    8      1       1 17884  36.00   M   10      1       2 17885  27.00   M    8      1       2 17886  14.00   M   10      1       1 17887  14.00   F   10      1       1 17888  22.00   F   10      1       2 17889  40.00   M   10      1       2 17890   9.00   M   24      1       1 17891  24.00   F   10      1       2 17892  28.00   F   10      1       2 17893  28.00   F   10      1       2 17894  16.00   M   10      1       1 17895  11.00   F   17      1       1 17896  32.00   M   16      1       2 17897   6.00   M   10      1       1 17898  27.00   F   10      1       2 17899  47.00   F   17      1       2 17900  19.00   F   10      1       2 17901  16.00   M   10      1       1 17902  20.00   F   10      1       2 17903  42.00   M   10      1       2 17904  20.00   F   10      1       2 17905  38.00   M   10      1       2 17906  28.00   F   11      1       2 17907  45.00   M   11      1       2 17908  32.00   F   24      1       2 17909  23.00   F   11      1       2 17910  70.00   F   22      1       3 17911  33.00   M   11      1       2 17912  25.00   F   10      1       2 17913   3.00   M   11      1       1 17914  28.00   M   11      1       2 17915  30.00   F   11      1       2 17916  62.00   F   13      1       3 17917  35.00   F   11      1       2 17918  30.00   F   12      1       2 17919  71.00   M   13      1       3 17920  22.00   M   13      1       2 17921  55.00   M   13      1       2 17922  30.00   M    6      1       2 17923   8.00   F   11      1       1 17924   2.00   F   11      1       1 17925  33.00   F   11      1       2 17926  27.00   M   11      1       2 17927  25.00   F   11      1       2 17928  10.00   F   11      1       1 17929   2.00   M   11      1       1 17930   1.00   F   11      1       1 17931  35.00   M   11      1       2 17932  30.00   F   11      1       2 17933  18.00   F   11      1       2 17934  19.00   F   16      1       2 17935   2.00   M   11      1       1 17936  40.00   M   11      1       2 17937  18.00   M   11      1       2 17938  30.00   F   13      1       2 17939  43.00   M   16      1       2 17940  40.00   F   11      1       2 17941  16.00   F   11      1       1 17942  15.00   F   11      1       1 17943   8.00   F   11      1       1 17944  51.00   M   11      1       2 17945  35.00   M   11      1       2 17946  18.00   M   11      1       2 17947  28.00   M   11      1       2 17948  22.00   F   11      1       2 17949  40.00   M   11      1       2 17950  30.00   F   16      1       2 17951  29.00   M   11      1       2 17952  25.00   F   11      1       2 17953   2.00   M   24      1       1 17954   2.00   M    6      1       1 17955   4.00   M   26      1       1 17956  48.00   F   14      1       2 17957  30.00   F    6      1       2 17958   5.00   M    6      1       1 17959  60.00   F    6      1       3 17960  36.00   M   14      1       2 17961  37.00   M    2      1       2 17962  44.00   M    4      1       2 17963  40.00   M    1      1       2 17964  24.00   M    1      1       2 17965  59.00   M    1      1       2 17966  23.00   M    1      1       2 17967  24.00   F    5      1       2 17968  33.00   F    2      1       2 17969  40.00   M    1      1       2 17970  23.00   F    5      1       2 17971  62.00   M    5      1       3 17972  24.00   F    2      1       2 17973   6.00   F    1      1       1 17974  35.00   M    7      1       2 17975   3.00   M    5      1       1 17976  62.00   M    8      1       3 17977  26.00   M   12      1       2 17978  54.00   F    7      1       2 17979  28.00   F    7      1       2 17980  25.00   M    7      1       2 17981  46.00   M   25      1       2 17982  54.00   M    7      1       2 17983  30.00   F   11      1       2 17984  46.00   F   11      1       2 17985  49.00   F   21      1       2 17986  36.00   F   11      1       2 17987   8.00   M   11      1       1 17988  20.00   M    7      1       2 17989  20.00   M   15      1       2 17990  39.00   F    7      1       2 17991  35.00   F    9      1       2 17992  32.00   M    3      1       2 17993  60.00   M    5      1       3 17994  24.00   F    5      1       2 17995  12.00   M    5      1       1 17996  26.00   F    5      1       2 17997  45.00   M   13      1       2 17998  15.00   M   13      1       1 17999  30.00   F   13      1       2 18000  47.00   M   13      1       2 18001  62.00   M    8      1       3 18002  30.00   M   12      1       2 18003  25.00   M    1      1       2 18004  26.00   F    9      1       2 18005  45.00   M    7      1       2 18006  35.00   F   11      1       2 18007  12.00   M    7      1       1 18008  65.00   M    7      1       3 18009  38.00   M    7      1       2 18010  23.00   M    9      1       2 18011  11.00   F   21      1       1 18012  73.00   M   19      1       3 18013  40.00   F    9      1       2 18014  34.00   M    9      1       2 18015  38.00   M   24      1       2 18016  28.00   F   19      1       2 18017  73.00   M   19      1       3 18018  37.00   M   20      1       2 18019  56.00   F   20      1       2 18020  38.00   M   11      1       2 18021  36.00   M   19      1       2 18022  42.00   M   19      1       2 18023  25.00   M   11      1       2 18024  23.00   M   11      1       2 18025  53.00   M   20      1       2 18026  46.00   F   11      1       2 18027  33.00   M   19      1       2 18028  67.00   M    7      1       3 18029  68.00   F   21      1       3 18030  29.00   F   11      1       2 18031  51.00   F   11      1       2 18032  26.00   F   19      1       2 18033   7.00   F   11      1       1 18034  45.00   M    9      1       2 18035  36.00   M   10      1       2 18036  85.00   F   14      1       3 18037  79.00   M   10      1       3 18038  17.00   F   10      1       1 18039  21.00   M   12      1       2 18040  35.00   M   22      1       2 18041  35.00   M   12      1       2 18042  34.00   F    5      1       2 18043  38.00   M   11      1       2 18044  53.00   M   18      1       2 18045  52.00   M    7      1       2 18046  50.00   M    7      1       2 18047  13.00   F    2      1       1 18048   4.00   M    9      1       1 18049  28.00   M    4      1       2 18050  42.00   M    2      1       2 18051  23.00   M    2      1       2 18052  39.00   M    4      1       2 18053  28.00   F    1      1       2 18054  36.00   M    1      1       2 18055  34.00   M    1      1       2 18056  33.00   F    4      1       2 18057  32.00   M    1      1       2 18058  41.00   M    1      1       2 18059  37.00   F    4      1       2 18060  19.00   M    1      1       2 18061  47.00   M    2      1       2 18062  10.00   M    9      1       1 18063  35.00   F    9      1       2 18064  61.00   F    9      1       3 18065  65.00   F    9      1       3 18066  32.00   M   11      1       2 18067  24.00   F   27      1       2 18068  41.00   M   11      1       2 18069  33.00   F   21      1       2 18070  50.00   M   11      1       2 18071  21.00   M   13      1       2 18072  62.00   M   11      1       3 18073  28.00   F   11      1       2 18074  10.00   F   11      1       1 18075   6.00   F   11      1       1 18076   4.00   F   11      1       1 18077  26.00   F   13      1       2 18078   6.00   F   11      1       1 18079   8.00   F   11      1       1 18080  29.00   M   13      1       2 18081  25.00   F   11      1       2 18082  21.00   F   11      1       2 18083  17.00   F   13      1       1 18084  18.00   F   21      1       2 18085  22.00   F    9      1       2 18086  28.00   M    9      1       2 18087  26.00   F    9      1       2 18088  10.00   M    7      1       1 18089  23.00   F    9      1       2 18090  17.00   M    9      1       1 18091  30.00   M    9      1       2 18092  25.00   F    9      1       2 18093  41.00   M    9      1       2 18094  36.00   F    9      1       2 18095  12.00   M    7      1       1 18096  17.00   M    9      1       1 18097  65.00   M    9      1       3 18098  26.00   F    9      1       2 18099  14.00   F    9      1       1 18100   8.00   M    7      1       1 18101  14.00   F    9      1       1 18102  48.00   M    9      1       2 18103  36.00   M    9      1       2 18104  43.00   M    9      1       2 18105  27.00   M    9      1       2 18106  27.00   F   13      1       2 18107  60.00   F   21      1       3 18108  56.00   F   21      1       2 18109  63.00   M   21      1       3 18110  44.00   M   21      1       2 18111  18.00   F    9      1       2 18112  33.00   M   12      1       2 18113  22.00   F   12      1       2 18114   6.00   M   12      1       1 18115  24.00   F    9      1       2 18116  16.00   M   10      1       1 18117   2.00   M    5      1       1 18118  60.00   M   11      1       3 18119   2.00   F    5      1       1 18120  44.00   M    5      1       2 18121  45.00   M    5      1       2 18122  66.00   M   12      1       3 18123  34.00   M   13      1       2 18124  55.00   M   11      1       2 18125  46.00   M   11      1       2 18126  43.00   M   11      1       2 18127  43.00   M   13      1       2 18128   5.00   F   12      1       1 18129  34.00   M    9      1       2 18130  26.00   M    4      1       2 18131  25.00   F   10      1       2 18132  23.00   F   10      1       2 18133  58.00   M   18      1       2 18134  56.00   F   10      1       2 18135  26.00   F    4      1       2 18136  25.00   M    6      1       2 18137  44.00   M    6      1       2 18138  25.00   M    6      1       2 18139  22.00   M   11      1       2 18140  23.00   M    6      1       2 18141  24.00   M   18      1       2 18142  39.00   M   10      1       2 18143  42.00   M   10      1       2 18144  24.00   M    6      1       2 18145  47.00   M   10      1       2 18146  23.00   M   10      1       2 18147  23.00   M   10      1       2 18148  26.00   M   10      1       2 18149  23.00   M    6      1       2 18150  25.00   M    6      1       2 18151  39.00   M   10      1       2 18152  24.00   M    6      1       2 18153  51.00   F   10      1       2 18154  29.00   F   10      1       2 18155  26.00   M    7      1       2 18156  23.00   M   22      1       2 18157  23.00   M   22      1       2 18158  23.00   M   14      1       2 18159  41.00   M    8      1       2 18160   9.00   M    7      1       1 18161  50.00   M    8      1       2 18162   2.00   M    8      1       1 18163  36.00   M   12      1       2 18164  10.00   F    8      1       1 18165  57.00   F   10      1       2 18166  27.00   M    8      1       2 18167  25.00   M   10      1       2 18168  50.00   M   18      1       2 18169  72.00   M   10      1       3 18170  63.00   M   10      1       3 18171  78.00   M   10      1       3 18172  60.00   M   10      1       3 18173  55.00   F   18      1       2 18174  17.00   M   10      1       1 18175  40.00   M   10      1       2 18176  80.00   F   10      1       3 18177  30.00   F   10      1       2 18178  72.00   M    8      1       3 18179   8.00   M   10      1       1 18180   8.00   F   10      1       1 18181  58.00   M   10      1       2 18182  26.00   M   10      1       2 18183  15.00   F   10      1       1 18184  27.00   F   11      1       2 18185  45.00   F   10      1       2 18186  51.00   M   10      1       2 18187  15.00   M   10      1       1 18188  44.00   F   10      1       2 18189  65.00   F    6      1       3 18190  11.00   M   10      1       1 18191  35.00   F   10      1       2 18192  13.00   M   10      1       1 18193  24.00   F    5      1       2 18194   8.00   F    6      1       1 18195  36.00   F   12      1       2 18196  17.00   M   23      1       1 18197  26.00   F   23      1       2 18198  30.00   F   18      1       2 18199  44.00   M    8      1       2 18200  35.00   F    8      1       2 18201  40.00   M    5      1       2 18202  24.00   F    8      1       2 18203  27.00   M   14      1       2 18204  27.00   F    8      1       2 18205  63.00   M    8      1       3 18206  35.00   F    8      1       2 18207  19.00   M    8      1       2 18208  27.00   M    8      1       2 18209  25.00   F    8      1       2 18210  40.00   F   14      1       2 18211  15.00   M   14      1       1 18212  30.00   M    8      1       2 18213  25.00   F   10      1       2 18214  40.00   F    8      1       2 18215  32.00   M   10      1       2 18216  28.00   F   17      1       2 18217  28.00   F   12      1       2 18218  56.00   F   10      1       2 18219  16.00   F    8      1       1 18220  51.00   M   10      1       2 18221  30.00   M   10      1       2 18222  28.00   M   10      1       2 18223  35.00   F   10      1       2 18224  72.00   M   17      1       3 18225   3.00   M    7      1       1 18226  55.00   M   10      1       2 18227  36.00   M   10      1       2 18228  36.00   F   17      1       2 18229  22.00   M   11      1       2 18230  16.00   M   11      1       1 18231  37.00   F   11      1       2 18232  35.00   F   11      1       2 18233  22.00   F   11      1       2 18234   2.00   F   11      1       1 18235   2.00   F   12      1       1 18236  49.00   M   11      1       2 18237  43.00   F   11      1       2 18238  22.00   M   11      1       2 18239  48.00   M   11      1       2 18240  27.00   M   11      1       2 18241  26.00   M   11      1       2 18242  20.00   M   11      1       2 18243  35.00   M   17      1       2 18244   4.00   F   11      1       1 18245  28.00   M   15      1       2 18246  21.00   F   15      1       2 18247  21.00   F   12      1       2 18248  27.00   M   11      1       2 18249  33.00   M   11      1       2 18250  20.00   M   11      1       2 18251  55.00   F   11      1       2 18252   5.00   M   11      1       1 18253   8.00   F   11      1       1 18254   1.00   F   11      1       1 18255  25.00   F   11      1       2 18256  15.00   M   11      1       1 18257  43.00   F   11      1       2 18258  17.00   M   11      1       1 18259  36.00   F   11      1       2 18260  18.00   F   11      1       2 18261  42.00   M   10      1       2 18262  20.00   M   11      1       2 18263  44.00   M    8      1       2 18264  19.00   M    8      1       2 18265  21.00   M    8      1       2 18266  28.00   M   20      1       2 18267  40.00   M   11      1       2 18268  30.00   F   12      1       2 18269  46.00   M   13      1       2 18270   7.00   M    8      1       1 18271  12.00   F   10      1       1 18272  45.00   M   11      1       2 18273  40.00   F   11      1       2 18274  17.00   F   11      1       1 18275  15.00   M   11      1       1 18276  20.00   F    6      1       2 18277  30.00   F    6      1       2 18278   6.00   F    6      1       1 18279  29.00   M   19      1       2 18280  26.00   M   13      1       2 18281   4.00   F   16      1       1 18282  29.00   M   12      1       2 18283  21.00   M    4      1       2 18284  22.00   M   12      1       2 18285  14.00   F    6      1       1 18286  34.00   M    6      1       2 18287  38.00   M    6      1       2 18288  22.00   F    6      1       2 18289  34.00   M   12      1       2 18290  42.00   M    6      1       2 18291  35.00   F   12      1       2 18292  26.00   F    6      1       2 18293  37.00   M    4      1       2 18294  32.00   F    4      1       2 18295  46.00   F    6      1       2 18296  44.00   M    9      1       2 18297  36.00   M   10      1       2 18298  50.00   M    8      1       2 18299  53.00   M    8      1       2 18300  46.00   M    8      1       2 18301  68.00   F    9      1       3 18302  57.00   M   19      1       2 18303  69.00   M   18      1       3 18304  20.00   M   19      1       2 18305  57.00   M   20      1       2 18306  20.00   F   20      1       2 18307  36.00   F   18      1       2 18308  61.00   M   18      1       3 18309  18.00   F   18      1       2 18310  57.00   M   18      1       2 18311  60.00   M   19      1       3 18312  30.00   F   18      1       2 18313  64.00   F   18      1       3 18314  51.00   M    8      1       2 18315  35.00   F    9      1       2 18316  34.00   M   18      1       2 18317  40.00   M    8      1       2 18318  48.00   F   11      1       2 18319  26.00   M   11      1       2 18320  45.00   M   12      1       2 18321  50.00   M   12      1       2 18322  26.00   M   12      1       2 18323  33.00   M    7      1       2 18324  35.00   M    5      1       2 18325  33.00   M    9      1       2 18326   0.91   M    8      1       1 18327  47.00   M   18      1       2 18328  29.00   F   17      1       2 18329  30.00   M   17      1       2 18330  60.00   F   17      1       3 18331  27.00   M   18      1       2 18332  42.00   M   17      1       2 18333  20.00   M   17      1       2 18334  30.00   M   26      1       2 18335  29.00   M    9      1       2 18336  33.00   M   17      1       2 18337  78.00   M   17      1       3 18338  68.00   M   18      1       3 18339  64.00   M   21      1       3 18340  45.00   M    7      1       2 18341  62.00   M   17      1       3 18342  32.00   M   18      1       2 18343  38.00   M   17      1       2 18344  24.00   M    8      1       2 18345  60.00   M   19      1       3 18346  59.00   F    8      1       2 18347  38.00   M   18      1       2 18348  38.00   M   17      1       2 18349  34.00   M   13      1       2 18350  40.00   M   13      1       2 18351  25.00   F   13      1       2 18352  19.00   M    9      1       2 18353  25.00   F    9      1       2 18354  63.00   M   11      1       3 18355   3.00   M    9      1       1 18356  28.00   M    9      1       2 18357  78.00   F   11      1       3 18358  80.00   M   11      1       3 18359  35.00   M   15      1       2 18360  33.00   M    8      1       2 18361  38.00   F    8      1       2 18362  12.00   F    8      1       1 18363   8.00   M    8      1       1 18364  46.00   M    2      1       2 18365  17.00   F    9      1       1 18366  39.00   F    9      1       2 18367  18.00   F    9      1       2 18368  65.00   F    2      1       3 18369  44.00   F    9      1       2 18370  14.00   M    9      1       1 18371  17.00   M    9      1       1 18372  42.00   M   10      1       2 18373   8.00   M    8      1       1 18374  61.00   F    8      1       3 18375  65.00   M   10      1       3 18376  62.00   M   12      1       3 18377  63.00   M   10      1       3 18378   7.00   F   11      1       1 18379  24.00   M    5      1       2 18380  22.00   M    5      1       2 18381  22.00   M    5      1       2 18382  22.00   M    5      1       2 18383  23.00   M   10      1       2 18384  28.00   M   10      1       2 18385  26.00   M    5      1       2 18386  23.00   M   10      1       2 18387  24.00   M   10      1       2 18388  23.00   M    5      1       2 18389  25.00   M    9      1       2 18390  22.00   M    9      1       2 18391  46.00   M    9      1       2 18392  29.00   M    9      1       2 18393  26.00   M   17      1       2 18394  34.00   M    9      1       2 18395  35.00   M    5      1       2 18396  22.00   M    9      1       2 18397  25.00   F    5      1       2 18398   2.00   F    9      1       1 18399  26.00   F    9      1       2 18400  30.00   M    5      1       2 18401  55.00   F    9      1       2 18402  28.00   F    9      1       2 18403  25.00   M    6      1       2 18404  26.00   M    6      1       2 18405  26.00   M    9      1       2 18406  27.00   M    9      1       2 18407  25.00   M   17      1       2 18408  26.00   M    9      1       2 18409  28.00   M    9      1       2 18410  24.00   M   17      1       2 18411  25.00   M   17      1       2 18412  38.00   M   17      1       2 18413  38.00   M    9      1       2 18414  12.00   M    9      1       1 18415  36.00   M    6      1       2 18416  46.00   M    9      1       2 18417  34.00   M    9      1       2 18418  27.00   M   10      1       2 18419  27.00   M   10      1       2 18420  34.00   M   10      1       2 18421  40.00   M   10      1       2 18422  48.00   M    9      1       2 18423  39.00   M    9      1       2 18424  33.00   M    9      1       2 18425  40.00   M    6      1       2 18426  57.00   M    6      1       2 18427  47.00   M    6      1       2 18428  31.00   M    9      1       2 18429  36.00   M    9      1       2 18430  37.00   M    9      1       2 18431  42.00   M    9      1       2 18432  45.00   M    9      1       2 18433  32.00   M    9      1       2 18434  24.00   F    9      1       2 18435  25.00   M    9      1       2 18436  23.00   M   17      1       2 18437  20.00   M    9      1       2 18438  25.00   M   10      1       2 18439  31.00   M    9      1       2 18440  24.00   M    5      1       2 18441  25.00   M    9      1       2 18442  32.00   M    9      1       2 18443  29.00   M    6      1       2 18444  25.00   M    9      1       2 18445  27.00   M    9      1       2 18446  48.00   M    6      1       2 18447  23.00   M    9      1       2 18448  38.00   M    6      1       2 18449  32.00   M   10      1       2 18450  28.00   M    6      1       2 18451  31.00   M    9      1       2 18452  32.00   M    5      1       2 18453  44.00   M    9      1       2 18454  28.00   M    6      1       2 18455  26.00   M   10      1       2 18456  26.00   M    6      1       2 18457  33.00   M    9      1       2 18458  43.00   M    9      1       2 18459  30.00   M    9      1       2 18460  46.00   M    9      1       2 18461  49.00   F    9      1       2 18462  36.00   M    9      1       2 18463  12.00   M   10      1       1 18464  75.00   F   10      1       3 18465  83.00   M   10      1       3 18466  12.00   M   10      1       1 18467  57.00   M    8      1       2 18468  57.00   M   10      1       2 18469  49.00   M   10      1       2 18470  26.00   F   16      1       2 18471  11.00   F   16      1       1 18472   3.00   M   16      1       1 18473  21.00   F   18      1       2 18474  52.00   M    9      1       2 18475   6.00   M    8      1       1 18476  70.00   F    6      1       3 18477  55.00   F    5      1       2 18478  35.00   M    5      1       2 18479  34.00   M    5      1       2 18480  27.00   M    8      1       2 18481  36.00   M    7      1       2 18482  38.00   M   14      1       2 18483  21.00   M   10      1       2 18484  39.00   M    7      1       2 18485  25.00   F   12      1       2 18486  49.00   M    9      1       2 18487  31.00   M    7      1       2 18488  65.00   M   16      1       3 18489  32.00   M    9      1       2 18490  45.00   M    9      1       2 18491  35.00   F    9      1       2 18492  28.00   M   11      1       2 18493  23.00   F   10      1       2 18494  27.00   M    7      1       2 18495   7.00   M   10      1       1 18496  40.00   M   12      1       2 18497  31.00   M    9      1       2 18498  35.00   F   10      1       2 18499   5.00   F    7      1       1 18500  33.00   M    9      1       2 18501   3.00   F    7      1       1 18502  36.00   F   11      1       2 18503  27.00   M    9      1       2 18504  20.00   F    9      1       2 18505  38.00   F    9      1       2 18506  17.00   F    9      1       1 18507  28.00   F    9      1       2 18508  14.00   M    9      1       1 18509  40.00   F    9      1       2 18510  17.00   F    9      1       1 18511  23.00   M    9      1       2 18512  23.00   M    9      1       2 18513  22.00   F    9      1       2 18514  23.00   M    9      1       2 18515  54.00   F    9      1       2 18516  45.00   F    9      1       2 18517  21.00   F    9      1       2 18518  43.00   F   16      1       2 18519  21.00   M    7      1       2 18520  37.00   M    9      1       2 18521  23.00   M    9      1       2 18522  29.00   M    9      1       2 18523   5.00   F    7      1       1 18524  50.00   F    9      1       2 18525   9.00   F   10      1       1 18526  16.00   F    9      1       1 18527  27.00   M    9      1       2 18528  24.00   M    9      1       2 18529  30.00   F    9      1       2 18530  40.00   M    9      1       2 18531  21.00   F   11      1       2 18532  26.00   M    9      1       2 18533  25.00   M   16      1       2 18534  31.00   M    6      1       2 18535  30.00   F    6      1       2 18536  29.00   F    6      1       2 18537  41.00   M    8      1       2 18538   1.00   F    5      1       1 18539  12.00   M    5      1       1 18540  20.00   M    5      1       2 18541  21.00   F    5      1       2 18542  28.00   M    5      1       2 18543  35.00   F    5      1       2 18544  22.00   M   10      1       2 18545  25.00   M   14      1       2 18546  30.00   M   10      1       2 18547  45.00   M   10      1       2 18548  41.00   M   13      1       2 18549  24.00   F    7      1       2 18550  29.00   F   10      1       2 18551  22.00   M    7      1       2 18552  10.00   M    7      1       1 18553  31.00   M   11      1       2 18554  30.00   M   11      1       2 18555  25.00   F   14      1       2 18556   4.00   F   11      1       1 18557  46.00   M   16      1       2 18558  60.00   F   19      1       3 18559  23.00   F    9      1       2 18560  24.00   F   10      1       2 18561  35.00   F   11      1       2 18562  10.00   M   10      1       1 18563   8.00   F   10      1       1 18564   6.00   M   26      1       1 18565  29.00   F   26      1       2 18566  35.00   F   10      1       2 18567  40.00   M   10      1       2 18568  18.00   F   11      1       2 18569  27.00   M   11      1       2 18570   2.00   M    4      1       1 18571  28.00   M   10      1       2 18572  26.00   F   10      1       2 18573  24.00   F    4      1       2 18574  47.00   M   10      1       2 18575  42.00   M   12      1       2 18576  48.00   M    9      1       2 18577  37.00   F   11      1       2 18578  39.00   M   11      1       2 18579  39.00   M   11      1       2 18580  13.00   F    9      1       1 18581  45.00   M   26      1       2 18582  28.00   F   10      1       2 18583   6.00   M   26      1       1 18584  35.00   M   10      1       2 18585  58.00   F   10      1       2 18586  34.00   M   10      1       2 18587  25.00   F   10      1       2 18588  24.00   M   10      1       2 18589  36.00   F   11      1       2 18590  30.00   M   11      1       2 18591  29.00   M   10      1       2 18592  26.00   M   11      1       2 18593  30.00   F   11      1       2 18594  18.00   M   10      1       2 18595  40.00   F   10      1       2 18596  39.00   F   11      1       2 18597   5.00   F   23      1       1 18598  41.00   M   12      1       2 18599  36.00   M   19      1       2 18600  32.00   M   10      1       2 18601  15.00   M   10      1       1 18602  25.00   F   19      1       2 18603  37.00   M   16      1       2 18604  18.00   M   11      1       2 18605  30.00   F   10      1       2 18606  49.00   F   10      1       2 18607  35.00   M   15      1       2 18608  32.00   M   15      1       2 18609  17.00   M   15      1       1 18610  38.00   M    9      1       2 18611  27.00   F    9      1       2 18612  30.00   M   15      1       2 18613  33.00   M   18      1       2 18614  16.00   F   15      1       1 18615  57.00   M   18      1       2 18616  70.00   M   15      1       3 18617  50.00   M   18      1       2 18618  40.00   F   15      1       2 18619  29.00   M   15      1       2 18620  55.00   M    9      1       2 18621  59.00   M    9      1       2 18622  30.00   F    7      1       2 18623  10.00   F    9      1       1 18624  34.00   M    9      1       2 18625  33.00   M    9      1       2 18626  33.00   F   20      1       2 18627  35.00   M    9      1       2 18628  16.00   F    9      1       1 18629  47.00   M    9      1       2 18630  65.00   M   22      1       3 18631  52.00   F    8      1       2 18632  64.00   F   22      1       3 18633  35.00   M    8      1       2 18634  43.00   M    8      1       2 18635  30.00   F   16      1       2 18636  38.00   F   16      1       2 18637  12.00   F   16      1       1 18638  42.00   M    9      1       2 18639  35.00   M   16      1       2 18640  32.00   M    1      1       2 18641  45.00   M   10      1       2 18642  27.00   M    1      1       2 18643   9.00   F   13      1       1 18644  22.00   F   13      1       2 18645   9.00   M    7      1       1 18646  54.00   M    7      1       2 18647  16.00   M   10      1       1 18648  51.00   F   10      1       2 18649  24.00   F   10      1       2 18650   5.00   M    9      1       1 18651  45.00   M    9      1       2 18652  50.00   M   10      1       2 18653  20.00   F    7      1       2 18654  37.00   M   15      1       2 18655  56.00   F   10      1       2 18656  20.00   M   10      1       2 18657  25.00   F   10      1       2 18658  28.00   F    8      1       2 18659  24.00   F    8      1       2 18660  29.00   F    9      1       2 18661  55.00   M    9      1       2 18662  51.00   M   10      1       2 18663  20.00   F   10      1       2 18664   6.00   F   10      1       1 18665  10.00   M   10      1       1 18666  34.00   F   10      1       2 18667  68.00   F   10      1       3 18668  31.00   M    7      1       2 18669  23.00   F    8      1       2 18670  26.00   F    9      1       2 18671  56.00   F   18      1       2 18672  35.00   M   16      1       2 18673  63.00   F   20      1       3 18674  33.00   M   16      1       2 18675  22.00   F   16      1       2 18676  36.00   M   16      1       2 18677  33.00   F   17      1       2 18678  34.00   F   16      1       2 18679  19.00   F   16      1       2 18680  67.00   M   17      1       3 18681  35.00   M   18      1       2 18682  44.00   M   17      1       2 18683  49.00   M   16      1       2 18684  24.00   M   17      1       2 18685  46.00   M   17      1       2 18686  24.00   F   17      1       2 18687  36.00   M    7      1       2 18688  59.00   M   17      1       2 18689  57.00   M   17      1       2 18690  54.00   M   17      1       2 18691  18.00   F   16      1       2 18692  40.00   F   16      1       2 18693  32.00   F   16      1       2 18694   4.00   M   16      1       1 18695  35.00   M   18      1       2 18696  28.00   M   16      1       2 18697  15.00   F   16      1       1 18698  30.00   F   10      1       2 18699  13.00   M   10      1       1 18700  25.00   F   16      1       2 18701   5.00   M   11      1       1 18702  23.00   M   16      1       2 18703  30.00   M   14      1       2 18704  26.00   F   14      1       2 18705  14.00   F   14      1       1 18706  31.00   F   18      1       2 18707  37.00   M   14      1       2 18708  29.00   F   10      1       2 18709   6.00   F   10      1       1 18710  40.00   M   10      1       2 18711  10.00   F   10      1       1 18712  25.00   F   14      1       2 18713  11.00   F   18      1       1 18714  58.00   F   14      1       2 18715  18.00   M   14      1       2 18716  35.00   M   18      1       2 18717  45.00   F   10      1       2 18718  55.00   M   10      1       2 18719  25.00   F   10      1       2 18720   3.00   M   10      1       1 18721  15.00   F   25      1       1 18722   6.00   F   10      1       1 18723  26.00   M   14      1       2 18724  23.00   F   14      1       2 18725  35.00   M   25      1       2 18726   6.00   M   10      1       1 18727  20.00   M   14      1       2 18728  48.00   F    9      1       2 18729  19.00   M   18      1       2 18730   2.00   M    9      1       1 18731  37.00   M    8      1       2 18732  23.00   M    9      1       2 18733   9.00   M    9      1       1 18734  13.00   F    8      1       1 18735   4.00   M    8      1       1 18736  50.00   M    8      1       2 18737  23.00   F    8      1       2 18738  32.00   F    8      1       2 18739   4.00   M    8      1       1 18740   3.00   M    8      1       1 18741   2.00   F   15      1       1 18742  41.00   F   15      1       2 18743  45.00   F   15      1       2 18744   7.00   M   10      1       1 18745   9.00   F   10      1       1 18746   1.00   F    7      1       1 18747   9.00   M    7      1       1 18748  35.00   F    7      1       2 18749  30.00   M    7      1       2 18750  28.00   F    7      1       2 18751  31.00   F    7      1       2 18752  44.00   M    7      1       2 18753   4.00   M    7      1       1 18754  11.00   F    7      1       1 18755  10.00   M    7      1       1 18756  30.00   M    7      1       2 18757  25.00   M   13      1       2 18758  34.00   M    8      1       2 18759  36.00   F    8      1       2 18760  43.00   M    8      1       2 18761  10.00   F    9      1       1 18762  61.00   M   24      1       3 18763   5.00   F    6      1       1 18764  50.00   M   10      1       2 18765   7.00   F    6      1       1 18766  45.00   M   10      1       2 18767  49.00   M   13      1       2 18768  48.00   M    8      1       2 18769  14.00   F    8      1       1 18770  43.00   M    8      1       2 18771  40.00   F    8      1       2 18772  28.00   F    6      1       2 18773  52.00   M   13      1       2 18774   8.00   M    9      1       1 18775  10.00   F    9      1       1 18776   1.00   M    6      1       1 18777  27.00   M    9      1       2 18778  62.00   M   15      1       3 18779  29.00   F   13      1       2 18780   6.00   M    9      1       1 18781  43.00   M   10      1       2 18782  28.00   M   10      1       2 18783  49.00   M   24      1       2 18784  10.00   M   14      1       1 18785  34.00   M   14      1       2 18786  44.00   M   14      1       2 18787  13.00   M   14      1       1 18788  22.00   M   14      1       2 18789  35.00   F   14      1       2 18790  15.00   M   14      1       1 18791  63.00   M    9      1       3 18792   4.00   F   20      1       1 18793   3.00   F    9      1       1 18794  23.00   M   15      1       2 18795  50.00   M    8      1       2 18796  22.00   M   15      1       2 18797  58.00   M    8      1       2 18798  25.00   M   15      1       2 18799  34.00   M    8      1       2 18800  28.00   M    8      1       2 18801  23.00   M    8      1       2 18802  35.00   M    8      1       2 18803  30.00   M   15      1       2 18804  48.00   F    8      1       2 18805  44.00   F    8      1       2 18806  27.00   M    8      1       2 18807  24.00   M    8      1       2 18808  45.00   M    8      1       2 18809  38.00   M    8      1       2 18810  27.00   M   15      1       2 18811  36.00   M    8      1       2 18812  39.00   M    8      1       2 18813  25.00   M   15      1       2 18814  23.00   M    8      1       2 18815   5.00   F    7      1       1 18816  39.00   F    7      1       2 18817  21.00   F    7      1       2 18818  60.00   M    7      1       3 18819  69.00   M    7      1       3 18820  19.00   F    7      1       2 18821  18.00   M    7      1       2 18822  30.00   M    6      1       2 18823  52.00   M    9      1       2 18824  50.00   M    9      1       2 18825  15.00   F   11      1       1 18826  50.00   F   11      1       2 18827  21.00   M   11      1       2 18828  32.00   M   11      1       2 18829  26.00   M   11      1       2 18830  32.00   F   11      1       2 18831  20.00   M   11      1       2 18832  25.00   M   11      1       2 18833  55.00   M   21      1       2 18834  45.00   F   11      1       2 18835  43.00   M   11      1       2 18836  26.00   F   11      1       2 18837   3.00   F   11      1       1 18838  29.00   F   11      1       2 18839  72.00   F   13      1       3 18840  12.00   M   11      1       1 18841  40.00   M   11      1       2 18842  23.00   M   11      1       2 18843  25.00   F    8      1       2 18844  30.00   M    5      1       2 18845  32.00   F    5      1       2 18846  27.00   M    7      1       2 18847  25.00   M    8      1       2 18848  37.00   F    8      1       2 18849  53.00   F    8      1       2 18850  30.00   M   11      1       2 18851  56.00   M   10      1       2 18852   5.00   M    9      1       1 18853  28.00   M    9      1       2 18854  30.00   M   10      1       2 18855  30.00   F    7      1       2 18856   8.00   M    9      1       1 18857  37.00   M   10      1       2 18858  23.00   M   10      1       2 18859  40.00   M   13      1       2 18860  45.00   F   13      1       2 18861  23.00   M   13      1       2 18862  39.00   M   10      1       2 18863  28.00   F    8      1       2 18864  21.00   M    8      1       2 18865  42.00   F   14      1       2 18866  18.00   F   12      1       2 18867  35.00   F   12      1       2 18868  23.00   F    8      1       2 18869  37.00   M    9      1       2 18870  46.00   F    9      1       2 18871  52.00   M    9      1       2 18872  48.00   M    9      1       2 18873  30.00   M    9      1       2 18874  34.00   M    9      1       2 18875  30.00   M   12      1       2 18876  35.00   F    9      1       2 18877  40.00   F    9      1       2 18878  40.00   F    9      1       2 18879  15.00   M    9      1       1 18880  30.00   M    9      1       2 18881  60.00   M    9      1       3 18882  32.00   F   12      1       2 18883  41.00   F    8      1       2 18884  25.00   M    9      1       2 18885  32.00   M    9      1       2 18886  34.00   M    9      1       2 18887  52.00   M    9      1       2 18888  22.00   M   14      1       2 18889  42.00   M   23      1       2 18890  50.00   M   14      1       2 18891  31.00   M   12      1       2 18892  29.00   M   12      1       2 18893  40.00   M   12      1       2 18894  16.00   M   12      1       1 18895  38.00   F   12      1       2 18896  32.00   M   12      1       2 18897  27.00   M    9      1       2 18898  28.00   M   12      1       2 18899  22.00   F    9      1       2 18900   3.00   F   12      1       1 18901  64.00   M   12      1       3 18902  37.00   M   14      1       2 18903  48.00   F    9      1       2 18904  26.00   M   15      1       2 18905  22.00   M   14      1       2 18906  70.00   M   14      1       3 18907  30.00   F   14      1       2 18908   8.00   M   12      1       1 18909   7.00   F    9      1       1 18910  30.00   F   15      1       2 18911  45.00   F   16      1       2 18912  65.00   M   16      1       3 18913  17.00   M    9      1       1 18914  17.00   F    9      1       1 18915  31.00   F   11      1       2 18916  24.00   M   24      1       2 18917  30.00   M   10      1       2 18918  26.00   F   10      1       2 18919   7.00   M   10      1       1 18920  38.00   F   10      1       2 18921  36.00   F   10      1       2 18922  35.00   F    9      1       2 18923  30.00   M    7      1       2 18924  11.00   M    7      1       1 18925  12.00   M   17      1       1 18926  11.00   F    9      1       1 18927  54.00   M   24      1       2 18928  52.00   F   10      1       2 18929   1.00   F    7      1       1 18930  40.00   F   17      1       2 18931  54.00   M   17      1       2 18932  36.00   F    3      1       2 18933  26.00   M   10      1       2 18934  33.00   M   10      1       2 18935   3.00   F   10      1       1 18936  11.00   F   11      1       1 18937  40.00   F   11      1       2 18938  14.00   M   11      1       1 18939  13.00   F   11      1       1 18940  65.00   M   11      1       3 18941  22.00   F   11      1       2 18942  60.00   M   11      1       3 18943  75.00   F   11      1       3 18944  26.00   M   10      1       2 18945  23.00   F   10      1       2 18946  27.00   F   10      1       2 18947   1.00   F   21      1       1 18948  12.00   M   15      1       1 18949  48.00   M   24      1       2 18950  10.00   F   10      1       1 18951  15.00   F   10      1       1 18952   8.00   F   24      1       1 18953  41.00   M   10      1       2 18954  17.00   F   10      1       1 18955   4.00   M    9      1       1 18956  66.00   M   21      1       3 18957   7.00   F   10      1       1 18958  50.00   F   10      1       2 18959  22.00   M   10      1       2 18960  30.00   M    7      1       2 18961   6.00   F    7      1       1 18962   8.00   F    7      1       1 18963   7.00   M    9      1       1 18964  25.00   F    7      1       2 18965  42.00   M    7      1       2 18966  34.00   F    7      1       2 18967  40.00   M   10      1       2 18968  47.00   F   11      1       2 18969  42.00   M   22      1       2 18970  54.00   M    9      1       2 18971  51.00   M    9      1       2 18972  55.00   F   21      1       2 18973  55.00   F    7      1       2 18974   2.00   M    7      1       1 18975  25.00   F   16      1       2 18976  50.00   F    6      1       2 18977  31.00   M    6      1       2 18978  24.00   M    6      1       2 18979  49.00   M    6      1       2 18980  45.00   M    6      1       2 18981  30.00   M   11      1       2 18982  28.00   M   11      1       2 18983  35.00   F    7      1       2 18984  25.00   M   13      1       2 18985  30.00   M    8      1       2 18986  24.00   M    7      1       2 18987  37.00   M   11      1       2 18988  28.00   F    7      1       2 18989  40.00   M    9      1       2 18990  36.00   F   22      1       2 18991   5.00   F   11      1       1 18992  24.00   M   13      1       2 18993  28.00   F   11      1       2 18994   2.00   M    8      1       1 18995  28.00   M   13      1       2 18996  65.00   M   22      1       3 18997  38.00   M   11      1       2 18998  43.00   M    8      1       2 18999  56.00   M   13      1       2 19000  48.00   F   13      1       2 19001  29.00   M   13      1       2 19002  64.00   M   11      1       3 19003  25.00   M   22      1       2 19004  40.00   M    7      1       2 19005  30.00   M    7      1       2 19006  43.00   M   13      1       2 19007  49.00   M   11      1       2 19008  36.00   M    7      1       2 19009  41.00   F    7      1       2 19010  23.00   M    8      1       2 19011  18.00   M   13      1       2 19012  22.00   M    8      1       2 19013  26.00   M    8      1       2 19014  34.00   M    8      1       2 19015  25.00   M   13      1       2 19016  44.00   M    7      1       2 19017  57.00   M    8      1       2 19018  46.00   F   13      1       2 19019  33.00   M    8      1       2 19020  43.00   M   14      1       2 19021  22.00   M    7      1       2 19022  23.00   M    7      1       2 19023  22.00   M    7      1       2 19024  78.00   M   14      1       3 19025  22.00   M    9      1       2 19026  34.00   F    8      1       2 19027  24.00   F    8      1       2 19028  34.00   F    8      1       2 19029  27.00   F    8      1       2 19030  38.00   M    8      1       2 19031  21.00   F    8      1       2 19032  55.00   F    8      1       2 19033  50.00   M    8      1       2 19034  32.00   M    8      1       2 19035  82.00   F    6      1       3 19036  60.00   F    8      1       3 19037  30.00   F    8      1       2 19038  29.00   F    6      1       2 19039  35.00   M    8      1       2 19040  20.00   M    8      1       2 19041  40.00   M    8      1       2 19042   8.00   M    8      1       1 19043  41.00   M    8      1       2 19044   7.00   F    9      1       1 19045  32.00   F    8      1       2 19046  37.00   M    8      1       2 19047  63.00   M    9      1       3 19048  54.00   M    9      1       2 19049  27.00   F    6      1       2 19050  36.00   M    7      1       2 19051  36.00   F    8      1       2 19052  23.00   F   14      1       2 19053  49.00   M   14      1       2 19054  26.00   F   19      1       2 19055  30.00   F   14      1       2 19056  37.00   M   18      1       2 19057  41.00   M   18      1       2 19058  40.00   M   19      1       2 19059  32.00   F   14      1       2 19060   7.00   F   19      1       1 19061  10.00   M   14      1       1 19062  60.00   M   14      1       3 19063  33.00   F   18      1       2 19064  13.00   F   14      1       1 19065  10.00   M   19      1       1 19066  56.00   F   19      1       2 19067  35.00   F   14      1       2 19068  38.00   M   19      1       2 19069  13.00   F   14      1       1 19070   7.00   M   19      1       1 19071  42.00   M   12      1       2 19072  41.00   F   12      1       2 19073  17.00   F   12      1       1 19074  38.00   M   12      1       2 19075  65.00   M   12      1       3 19076  14.00   F    8      1       1 19077  26.00   F   11      1       2 19078  50.00   F   23      1       2 19079  20.00   F    9      1       2 19080  65.00   F    9      1       3 19081  42.00   M   10      1       2 19082  28.00   F    7      1       2 19083  18.00   F    7      1       2 19084  20.00   F    4      1       2 19085  22.00   F    4      1       2 19086  21.00   F    4      1       2 19087  23.00   F    4      1       2 19088  54.00   F    7      1       2 19089  12.00   M    7      1       1 19090  60.00   F    7      1       3 19091  24.00   F    7      1       2 19092  48.00   F    7      1       2 19093  65.00   M    7      1       3 19094  35.00   M   12      1       2 19095  55.00   M   12      1       2 19096  37.00   F   13      1       2 19097  16.00   F   13      1       1 19098  31.00   M   13      1       2 19099  59.00   M   13      1       2 19100  25.00   M    8      1       2 19101  15.00   M    8      1       1 19102  27.00   M    8      1       2 19103  24.00   F   14      1       2 19104  50.00   M   10      1       2 19105  49.00   M    8      1       2 19106  40.00   F    8      1       2 19107  55.00   F   18      1       2 19108  42.00   F   18      1       2 19109  26.00   M   18      1       2 19110  35.00   M   18      1       2 19111  41.00   M   18      1       2 19112  61.00   F   15      1       3 19113  50.00   F   15      1       2 19114  64.00   F   16      1       3 19115  53.00   M   15      1       2 19116  35.00   M   18      1       2 19117  36.00   M   15      1       2 19118  53.00   M   14      1       2 19119  32.00   F   14      1       2 19120  34.00   M   15      1       2 19121  55.00   M   17      1       2 19122  61.00   F   17      1       3 19123  40.00   M   14      1       2 19124  29.00   F   14      1       2 19125  59.00   F   14      1       2 19126  61.00   F   16      1       3 19127  32.00   M   16      1       2 19128  33.00   M   17      1       2 19129  24.00   M   14      1       2 19130  45.00   F   16      1       2 19131  65.00   M   16      1       3 19132  26.00   M   14      1       2 19133  54.00   M   16      1       2 19134  17.00   F   16      1       1 19135  13.00   F   16      1       1 19136  12.00   F   16      1       1 19137  16.00   F   16      1       1 19138  22.00   F   16      1       2 19139  42.00   F   16      1       2 19140  21.00   F   16      1       2 19141  45.00   F   16      1       2 19142  18.00   F   14      1       2 19143  15.00   M   14      1       1 19144  27.00   F   14      1       2 19145   7.00   F   16      1       1 19146  45.00   F   16      1       2 19147  26.00   M   14      1       2 19148  19.00   M   16      1       2 19149  33.00   M    7      1       2 19150  19.00   F    7      1       2 19151  62.00   M   14      1       3 19152  55.00   F    7      1       2 19153  25.00   M    7      1       2 19154  30.00   M   14      1       2 19155  31.00   F    7      1       2 19156  24.00   M    7      1       2 19157  45.00   F    7      1       2 19158  33.00   M    7      1       2 19159   1.00   F    7      1       1 19160  17.00   M    7      1       1 19161  15.00   M   16      1       1 19162  25.00   M    7      1       2 19163   5.00   F   16      1       1 19164  60.00   M   14      1       3 19165  70.00   M   16      1       3 19166  42.00   M   16      1       2 19167  35.00   M   16      1       2 19168  70.00   M   15      1       3 19169  69.00   M   16      1       3 19170  36.00   M   15      1       2 19171  53.00   M   14      1       2 19172   1.00   F   15      1       1 19173  22.00   F   14      1       2 19174  50.00   F   15      1       2 19175  46.00   F   15      1       2 19176  45.00   F   15      1       2 19177  47.00   M   14      1       2 19178  56.00   F   15      1       2 19179  25.00   F   14      1       2 19180  15.00   F   14      1       1 19181  46.00   F   16      1       2 19182  52.00   M   14      1       2 19183  48.00   M   14      1       2 19184  75.00   M   14      1       3 19185  72.00   F   14      1       3 19186  29.00   M   15      1       2 19187  53.00   F   14      1       2 19188  21.00   F   15      1       2 19189  31.00   F   14      1       2 19190  47.00   M   15      1       2 19191  28.00   M   15      1       2 19192  44.00   M   15      1       2 19193  49.00   F   15      1       2 19194  23.00   M   14      1       2 19195  33.00   F   16      1       2 19196  57.00   M   15      1       2 19197  22.00   M   15      1       2 19198  66.00   F   14      1       3 19199  99.00   F   15      1       3 19200  28.00   M   14      1       2 19201  70.00   F   14      1       3 19202  31.00   F   14      1       2 19203  74.00   M   15      1       3 19204  68.00   F   16      1       3 19205  51.00   M    8      1       2 19206  50.00   M    7      1       2 19207  63.00   M   11      1       3 19208  57.00   M   11      1       2 19209  33.00   M   11      1       2 19210  22.00   F   11      1       2 19211  27.00   F    9      1       2 19212  31.00   M    9      1       2 19213  32.00   F    6      1       2 19214  67.00   M   23      1       3 19215  37.00   F    9      1       2 19216  50.00   F   23      1       2 19217  26.00   M   10      1       2 19218  50.00   M    9      1       2 19219  20.00   F   10      1       2 19220  20.00   M    9      1       2 19221  19.00   F   10      1       2 19222  23.00   M    9      1       2 19223  62.00   F    9      1       3 19224  30.00   F   10      1       2 19225  11.00   M   10      1       1 19226  32.00   M   12      1       2 19227  29.00   M    9      1       2 19228  19.00   M    9      1       2 19229  35.00   F   17      1       2 19230  48.00   M    9      1       2 19231  40.00   F   23      1       2 19232  40.00   F    9      1       2 19233  20.00   F    9      1       2 19234  18.00   F    9      1       2 19235   8.00   M    9      1       1 19236  27.00   F    9      1       2 19237  22.00   F    9      1       2 19238  21.00   M   13      1       2 19239  26.00   F    9      1       2 19240  35.00   M    9      1       2 19241  48.00   M   14      1       2 19242  34.00   M    9      1       2 19243  39.00   M   11      1       2 19244  37.00   F   10      1       2 19245   9.00   M    9      1       1 19246  18.00   F    9      1       2 19247  25.00   F    9      1       2 19248   6.00   M    9      1       1 19249   0.50   M    9      1       1 19250  35.00   M    7      1       2 19251  35.00   M    9      1       2 19252  28.00   M   10      1       2 19253  24.00   F   17      1       2 19254  40.00   F   10      1       2 19255  26.00   F    9      1       2 19256  34.00   M    9      1       2 19257  20.00   F    9      1       2 19258  55.00   M   14      1       2 19259  68.00   F   10      1       3 19260  45.00   F   14      1       2 19261  28.00   M   11      1       2 19262  25.00   F   11      1       2 19263  69.00   F    4      1       3 19264  36.00   F    7      1       2 19265  24.00   M   16      1       2 19266  45.00   M    7      1       2 19267  26.00   F    7      1       2 19268  65.00   F   13      1       3 19269  43.00   M    8      1       2 19270  48.00   M    8      1       2 19271  40.00   F    8      1       2 19272  44.00   F   17      1       2 19273  48.00   M   19      1       2 19274  23.00   M   12      1       2 19275  16.00   F   12      1       1 19276  85.00   F   17      1       3 19277  81.00   M   11      1       3 19278  25.00   M   11      1       2 19279  18.00   M    8      1       2 19280  29.00   M   15      1       2 19281  50.00   F    6      1       2 19282  23.00   F    8      1       2 19283   4.00   F    8      1       1 19284  42.00   F    8      1       2 19285  59.00   M   10      1       2 19286  17.00   F    8      1       1 19287  36.00   M    8      1       2 19288  27.00   M    8      1       2 19289  35.00   F    8      1       2 19290  44.00   M    8      1       2 19291  38.00   M   17      1       2 19292  49.00   M   15      1       2 19293  17.00   M   15      1       1 19294  39.00   F   15      1       2 19295  18.00   M   15      1       2 19296  30.00   M    8      1       2 19297  57.00   M    8      1       2 19298  21.00   M    8      1       2 19299  21.00   F    7      1       2 19300  23.00   F    9      1       2 19301  38.00   M    9      1       2 19302  54.00   M    9      1       2 19303  31.00   M    9      1       2 19304  47.00   M    9      1       2 19305  18.00   M    9      1       2 19306  50.00   M    9      1       2 19307  25.00   M    9      1       2 19308  55.00   M   15      1       2 19309  60.00   M   17      1       3 19310  62.00   M    9      1       3 19311  32.00   M    9      1       2 19312  46.00   M    6      1       2 19313  54.00   M    9      1       2 19314   6.00   F   12      1       1 19315  65.00   M   12      1       3 19316   3.00   F    7      1       1 19317   2.00   M   12      1       1 19318  24.00   F   12      1       2 19319  45.00   F   12      1       2 19320  29.00   F   18      1       2 19321   4.00   F   18      1       1 19322  13.00   F   18      1       1 19323   8.00   M   18      1       1 19324  59.00   M   18      1       2 19325  37.00   M   10      1       2 19326  40.00   F   10      1       2 19327  16.00   F   18      1       1 19328  33.00   F   18      1       2 19329  31.00   M   18      1       2 19330  56.00   M   18      1       2 19331  50.00   F   18      1       2 19332  42.00   F   10      1       2 19333  16.00   M   18      1       1 19334  12.00   F   18      1       1 19335  28.00   M   10      1       2 19336  23.00   F   10      1       2 19337  23.00   M   12      1       2 19338  31.00   M    8      1       2 19339  27.00   M   10      1       2 19340  40.00   M    8      1       2 19341  42.00   M   10      1       2 19342  36.00   M    8      1       2 19343  26.00   M   21      1       2 19344  34.00   M   10      1       2 19345  34.00   M   10      1       2 19346  39.00   M   10      1       2 19347  43.00   M   10      1       2 19348  43.00   M    8      1       2 19349  31.00   M   12      1       2 19350  40.00   M   10      1       2 19351  36.00   M   12      1       2 19352  25.00   M   10      1       2 19353  50.00   F   10      1       2 19354  29.00   F   10      1       2 19355  21.00   F   10      1       2 19356  60.00   M   10      1       3 19357  24.00   F   10      1       2 19358  21.00   M   12      1       2 19359  19.00   F    6      1       2 19360  32.00   M   10      1       2 19361  24.00   M   12      1       2 19362  56.00   M   10      1       2 19363  28.00   M   10      1       2 19364  35.00   F   10      1       2 19365  39.00   F   12      1       2 19366  38.00   F   10      1       2 19367  60.00   M   10      1       3 19368  27.00   F   10      1       2 19369  30.00   M    8      1       2 19370   0.30   M    8      1       1 19371  42.00   M    8      1       2 19372  19.00   M    5      1       2 19373  28.00   F    5      1       2 19374  45.00   F    5      1       2 19375  47.00   M   10      1       2 19376  46.00   M   10      1       2 19377  65.00   F   10      1       3 19378  45.00   F   10      1       2 19379  44.00   F   10      1       2 19380  43.00   F    7      1       2 19381  25.00   F   10      1       2 19382  22.00   F    7      1       2 19383  20.00   F    7      1       2 19384  16.00   F    7      1       1 19385  13.00   M    7      1       1 19386  15.00   F    7      1       1 19387   8.00   M   10      1       1 19388  48.00   M   10      1       2 19389  23.00   M    7      1       2 19390  24.00   M    7      1       2 19391  16.00   M    7      1       1 19392  19.00   M    7      1       2 19393  47.00   M    7      1       2 19394  62.00   F    7      1       3 19395  48.00   M    7      1       2 19396  70.00   M    7      1       3 19397  70.00   F    7      1       3 19398  38.00   M    7      1       2 19399  42.00   M    7      1       2 19400  26.00   M    7      1       2 19401  68.00   F    7      1       3 19402  28.00   M    7      1       2 19403  24.00   M    7      1       2 19404  24.00   M    7      1       2 19405  19.00   F    7      1       2 19406  30.00   M    7      1       2 19407  60.00   F    7      1       3 19408  38.00   M    7      1       2 19409  26.00   M    7      1       2 19410  38.00   F    7      1       2 19411  10.00   M    7      1       1 19412  50.00   M    7      1       2 19413  52.00   M    7      1       2 19414  40.00   M    7      1       2 19415  20.00   F    7      1       2 19416   8.00   F    7      1       1 19417  75.00   M    7      1       3 19418  65.00   F    7      1       3 19419  75.00   F    7      1       3 19420  26.00   M    7      1       2 19421  38.00   F    7      1       2 19422  64.00   M   16      1       3 19423  62.00   F    7      1       3 19424  14.00   M    7      1       1 19425   6.00   M    7      1       1 19426  38.00   M    7      1       2 19427  13.00   F    7      1       1 19428  30.00   M    7      1       2 19429  30.00   F    7      1       2 19430  55.00   F    3      1       2 19431  45.00   F    7      1       2 19432  40.00   F    7      1       2 19433   4.00   F    7      1       1 19434  33.00   M    7      1       2 19435   7.00   M    7      1       1 19436  24.00   M    7      1       2 19437  21.00   F    7      1       2 19438  28.00   M    7      1       2 19439  30.00   M    7      1       2 19440  12.00   F    7      1       1 19441  20.00   F    7      1       2 19442   9.00   F    7      1       1 19443  31.00   M    8      1       2 19444  36.00   M    8      1       2 19445  24.00   M    8      1       2 19446  33.00   M    8      1       2 19447  30.00   M    8      1       2 19448  23.00   M    8      1       2 19449  22.00   M    8      1       2 19450  42.00   M    8      1       2 19451  54.00   F    5      1       2 19452  49.00   M    8      1       2 19453  53.00   M    8      1       2 19454  52.00   M    8      1       2 19455  22.00   M    8      1       2 19456  30.00   M    8      1       2 19457  24.00   M    8      1       2 19458  28.00   M    8      1       2 19459  23.00   M    8      1       2 19460  22.00   M    8      1       2 19461  25.00   M    8      1       2 19462  21.00   M    6      1       2 19463  49.00   F    5      1       2 19464  19.00   M    8      1       2 19465  38.00   M    9      1       2 19466  30.00   F    9      1       2 19467  54.00   F   14      1       2 19468  51.00   M    9      1       2 19469  51.00   M    9      1       2 19470  38.00   F   11      1       2 19471  42.00   M   11      1       2 19472  15.00   M   11      1       1 19473  27.00   M   11      1       2 19474  17.00   M   11      1       1 19475  50.00   F    9      1       2 19476  19.00   M    9      1       2 19477  54.00   M    9      1       2 19478  33.00   M   11      1       2 19479  36.00   M    9      1       2 19480  20.00   M    9      1       2 19481  21.00   F   19      1       2 19482  55.00   F    9      1       2 19483  31.00   F    9      1       2 19484  25.00   F    9      1       2 19485  32.00   F    9      1       2 19486   4.00   F    9      1       1 19487  70.00   F    9      1       3 19488  42.00   M    9      1       2 19489  20.00   M    9      1       2 19490  32.00   F    9      1       2 19491  52.00   M    9      1       2 19492  32.00   F    9      1       2 19493  21.00   F    9      1       2 19494  60.00   F    9      1       3 19495  23.00   M    9      1       2 19496  35.00   M    9      1       2 19497  19.00   M    9      1       2 19498  20.00   F    9      1       2 19499  22.00   F    9      1       2 19500  72.00   F    9      1       3 19501  22.00   F    9      1       2 19502  20.00   M    9      1       2 19503  40.00   F    9      1       2 19504  48.00   F    9      1       2 19505  45.00   F    9      1       2 19506  80.00   F    9      1       3 19507  24.00   F   19      1       2 19508  28.00   M    9      1       2 19509  42.00   M    9      1       2 19510  30.00   F   12      1       2 19511  60.00   F   14      1       3 19512  35.00   M   13      1       2 19513  12.00   F    6      1       1 19514  21.00   M   14      1       2 19515  32.00   M   21      1       2 19516  35.00   M   14      1       2 19517  45.00   F   13      1       2 19518  36.00   M   13      1       2 19519  60.00   F   12      1       3 19520  21.00   F   14      1       2 19521  30.00   F   15      1       2 19522  22.00   F   14      1       2 19523  62.00   M   13      1       3 19524  52.00   F   14      1       2 19525  60.00   M   15      1       3 19526  69.00   M   14      1       3 19527  45.00   F   12      1       2 19528  54.00   M   14      1       2 19529  24.00   F   15      1       2 19530  54.00   F   14      1       2 19531  53.00   M   12      1       2 19532  27.00   F   13      1       2 19533  68.00   M   22      1       3 19534  50.00   F   14      1       2 19535  36.00   M   14      1       2 19536  41.00   M   14      1       2 19537  61.00   M   14      1       3 19538  37.00   F   12      1       2 19539  34.00   M   13      1       2 19540  34.00   F   13      1       2 19541  44.00   F   13      1       2 19542  65.00   F   13      1       3 19543  15.00   F   14      1       1 19544  70.00   F   14      1       3 19545  32.00   F   13      1       2 19546  53.00   F   14      1       2 19547  54.00   M   10      1       2 19548  48.00   F   10      1       2 19549  45.00   F   12      1       2 19550  20.00   F   12      1       2 19551  15.00   F   12      1       1 19552   4.00   M   12      1       1 19553  60.00   M   12      1       3 19554  40.00   M   12      1       2 19555  30.00   M   14      1       2 19556  40.00   F   14      1       2 19557  16.00   M   14      1       1 19558  16.00   M   14      1       1 19559  30.00   F   14      1       2 19560  19.00   M    7      1       2 19561  28.00   M    7      1       2 19562  35.00   F   17      1       2 19563  25.00   M   14      1       2 19564  35.00   M   14      1       2 19565  30.00   F   14      1       2 19566  18.00   M   14      1       2 19567  25.00   F    7      1       2 19568   9.00   M    8      1       1 19569  40.00   F    9      1       2 19570  28.00   F   16      1       2 19571  62.00   M    9      1       3 19572  28.00   F   17      1       2 19573  10.00   F   16      1       1 19574  60.00   F   22      1       3 19575  19.00   M   14      1       2 19576  40.00   F   10      1       2 19577  40.00   F   10      1       2 19578  50.00   M   10      1       2 19579  22.00   F   10      1       2 19580  30.00   M    9      1       2 19581  53.00   F    8      1       2 19582  55.00   M    9      1       2 19583  27.00   F   10      1       2 19584  31.00   M   10      1       2 19585  30.00   M   10      1       2 19586  24.00   F   10      1       2 19587   0.75   F   10      1       1 19588   2.00   F   16      1       1 19589  40.00   F    9      1       2 19590   8.00   M   10      1       1 19591  10.00   M    9      1       1 19592  44.00   M    9      1       2 19593  27.00   M    9      1       2 19594  24.00   F   16      1       2 19595  15.00   M    9      1       1 19596   9.00   F    9      1       1 19597  28.00   M   10      1       2 19598  25.00   F    3      1       2 19599  45.00   F   12      1       2 19600  23.00   F   21      1       2 19601  38.00   F   12      1       2 19602   6.00   F   14      1       1 19603  36.00   F   14      1       2 19604  12.00   F    5      1       1 19605  55.00   F    5      1       2 19606  56.00   M    5      1       2 19607  14.00   M    5      1       1 19608  45.00   F    5      1       2 19609  50.00   F   14      1       2 19610  36.00   M    5      1       2 19611  25.00   F   10      1       2 19612  58.00   F   10      1       2 19613   0.41   F   10      1       1 19614  33.00   M   10      1       2 19615  17.00   M    6      1       1 19616  43.00   F    7      1       2 19617  69.00   F    6      1       3 19618  50.00   M    6      1       2 19619  42.00   F    7      1       2 19620  19.00   F    6      1       2 19621  45.00   M    6      1       2 19622  29.00   F   12      1       2 19623  35.00   M   12      1       2 19624  65.00   M   10      1       3 19625  75.00   F   12      1       3 19626  27.00   M   11      1       2 19627  28.00   F   11      1       2 19628  53.00   F   11      1       2 19629   6.00   M   11      1       1 19630  31.00   F   11      1       2 19631  55.00   M   13      1       2 19632  24.00   M   11      1       2 19633  21.00   F   12      1       2 19634  33.00   M   12      1       2 19635  68.00   F   12      1       3 19636   6.00   F   12      1       1 19637  31.00   M   12      1       2 19638  12.00   M   12      1       1 19639   5.00   M   12      1       1 19640  27.00   F    9      1       2 19641   8.00   F    8      1       1 19642  12.00   M    8      1       1 19643  34.00   F    8      1       2 19644  36.00   M    8      1       2 19645  25.00   M    8      1       2 19646  15.00   M    8      1       1 19647  30.00   F    8      1       2 19648  37.00   M    8      1       2 19649  66.00   M    7      1       3 19650  28.00   M    7      1       2 19651  50.00   F    7      1       2 19652  50.00   M    7      1       2 19653  60.00   M    7      1       3 19654  65.00   F    7      1       3 19655  60.00   F    7      1       3 19656  59.00   F    7      1       2 19657  30.00   F    7      1       2 19658  61.00   M   12      1       3 19659  65.00   F    7      1       3 19660  22.00   M    7      1       2 19661  45.00   M    7      1       2 19662  16.00   M    7      1       1 19663  62.00   M    7      1       3 19664  70.00   M    7      1       3 19665  10.00   F    7      1       1 19666   8.00   M    7      1       1 19667  48.00   F    7      1       2 19668  65.00   M    3      1       3 19669  25.00   M    7      1       2 19670  70.00   F    1      1       3 19671  30.00   F    7      1       2 19672  85.00   M    7      1       3 19673  29.00   M    7      1       2 19674  58.00   F    7      1       2 19675  31.00   F    7      1       2 19676  29.00   F    7      1       2 19677   4.00   F    7      1       1 19678   1.00   M    7      1       1 19679  34.00   F    7      1       2 19680  15.00   M    7      1       1 19681  23.00   M    7      1       2 19682  20.00   F    7      1       2 19683  26.00   F    7      1       2 19684  25.00   F    7      1       2 19685  59.00   M    9      1       2 19686  42.00   M    9      1       2 19687  58.00   F    9      1       2 19688  56.00   F   10      1       2 19689  65.00   F   10      1       3 19690  26.00   M   10      1       2 19691  25.00   M   10      1       2 19692  31.00   M    7      1       2 19693  39.00   M    7      1       2 19694  58.00   F    6      1       2 19695  14.00   M    6      1       1 19696  56.00   F    7      1       2 19697  60.00   F    7      1       3 19698  59.00   M    7      1       2 19699  47.00   M    7      1       2 19700  26.00   F   10      1       2 19701  18.00   M   13      1       2 19702  31.00   M    7      1       2 19703  71.00   F   10      1       3 19704  30.00   F   10      1       2 19705  24.00   F    3      1       2 19706  29.00   F    7      1       2 19707  65.00   M   18      1       3 19708  25.00   M    4      1       2 19709  35.00   M   11      1       2 19710  60.00   F   10      1       3 19711  38.00   F   10      1       2 19712  65.00   M    9      1       3 19713  21.00   F    9      1       2 19714  38.00   M   13      1       2 19715  37.00   M   14      1       2 19716  52.00   F   13      1       2 19717  23.00   M   13      1       2 19718  45.00   F   12      1       2 19719  32.00   M   12      1       2 19720  27.00   M   12      1       2 19721  62.00   F   15      1       3 19722  48.00   M   12      1       2 19723  26.00   M   13      1       2 19724  80.00   F   13      1       3 19725  44.00   M   12      1       2 19726  34.00   M   11      1       2 19727  58.00   M   11      1       2 19728  30.00   M   14      1       2 19729   6.00   F   13      1       1 19730  68.00   M   12      1       3 19731  23.00   F   14      1       2 19732  58.00   M   14      1       2 19733  56.00   M   13      1       2 19734  46.00   M   14      1       2 19735  47.00   M   12      1       2 19736  27.00   F   13      1       2 19737  35.00   F   12      1       2 19738  64.00   F   17      1       3 19739  52.00   F   17      1       2 19740  27.00   F   13      1       2 19741  30.00   F   12      1       2 19742  30.00   M   13      1       2 19743  36.00   M   13      1       2 19744  32.00   F   12      1       2 19745  25.00   F   11      1       2 19746  51.00   F   13      1       2 19747  55.00   F   13      1       2 19748  52.00   M   13      1       2 19749  36.00   F   13      1       2 19750  63.00   F   15      1       3 19751  59.00   M   13      1       2 19752  48.00   M   13      1       2 19753  27.00   M   13      1       2 19754  59.00   M   12      1       2 19755  81.00   M   13      1       3 19756  55.00   M   13      1       2 19757  55.00   M   12      1       2 19758  55.00   M   13      1       2 19759  16.00   M   13      1       1 19760  51.00   F   12      1       2 19761  23.00   M   11      1       2 19762  15.00   F   12      1       1 19763  49.00   F   11      1       2 19764  66.00   F   11      1       3 19765  24.00   F   11      1       2 19766  19.00   F   13      1       2 19767  16.00   F   11      1       1 19768   8.00   F   11      1       1 19769  37.00   F   11      1       2 19770  52.00   F   12      1       2 19771  70.00   M   13      1       3 19772  56.00   M   16      1       2 19773  55.00   M   12      1       2 19774   1.00   M   12      1       1 19775  27.00   F   12      1       2 19776  32.00   M   12      1       2 19777   4.00   F   12      1       1 19778  27.00   M   14      1       2 19779  73.00   M   14      1       3 19780  50.00   F   13      1       2 19781  23.00   F   14      1       2 19782  54.00   M   13      1       2 19783  11.00   M   13      1       1 19784  68.00   M   20      1       3 19785  38.00   M   13      1       2 19786  43.00   F   11      1       2 19787  30.00   M   12      1       2 19788  62.00   F   12      1       3 19789  50.00   M   12      1       2 19790  49.00   M   14      1       2 19791  57.00   F   13      1       2 19792  68.00   F   15      1       3 19793  23.00   F   11      1       2 19794  25.00   M   13      1       2 19795  35.00   M   13      1       2 19796  24.00   F   13      1       2 19797  65.00   M   12      1       3 19798  33.00   M   12      1       2 19799  49.00   F   13      1       2 19800  36.00   M   13      1       2 19801  57.00   M   14      1       2 19802  63.00   M   13      1       3 19803  48.00   M   13      1       2 19804  66.00   M   13      1       3 19805  85.00   M   13      1       3 19806  16.00   M   11      1       1 19807  42.00   M   13      1       2 19808  26.00   F   13      1       2 19809  27.00   M   13      1       2 19810  28.00   M   13      1       2 19811  75.00   M   13      1       3 19812  55.00   F   13      1       2 19813  50.00   M   13      1       2 19814  55.00   M   14      1       2 19815  24.00   M   13      1       2 19816  56.00   M   13      1       2 19817  54.00   F   13      1       2 19818  42.00   M   13      1       2 19819  56.00   F   12      1       2 19820  57.00   M   12      1       2 19821  27.00   M   17      1       2 19822  27.00   F   13      1       2 19823  57.00   M   15      1       2 19824  56.00   F   13      1       2 19825  55.00   F   13      1       2 19826  29.00   M   13      1       2 19827  28.00   M   13      1       2 19828  49.00   M   13      1       2 19829  65.00   M   14      1       3 19830  67.00   M   13      1       3 19831  31.00   F   13      1       2 19832  51.00   M    4      1       2 19833   4.00   M   13      1       1 19834  10.00   F   13      1       1 19835   8.00   M   13      1       1 19836  58.00   M   13      1       2 19837  59.00   M   13      1       2 19838  51.00   M   13      1       2 19839  59.00   M   14      1       2 19840  20.00   M   13      1       2 19841  27.00   M   14      1       2 19842  25.00   M   14      1       2 19843  17.00   M   14      1       1 19844  14.00   M   14      1       1 19845  14.00   F   14      1       1 19846  70.00   M    7      1       3 19847  26.00   F    7      1       2 19848  32.00   F    8      1       2 19849  59.00   M    8      1       2 19850  30.00   F    8      1       2 19851  38.00   M    8      1       2 19852  36.00   M    9      1       2 19853  42.00   M   12      1       2 19854  54.00   M    9      1       2 19855  43.00   M   17      1       2 19856  17.00   M   17      1       1 19857  32.00   M    9      1       2 19858  50.00   M    7      1       2 19859  42.00   M    7      1       2 19860  48.00   M    7      1       2 19861  22.00   M    7      1       2 19862  56.00   M    7      1       2 19863  38.00   M    7      1       2 19864   9.00   M    7      1       1 19865  13.00   F    7      1       1 19866  30.00   M    7      1       2 19867  31.00   F    7      1       2 19868  65.00   M   10      1       3 19869  62.00   M    9      1       3 19870  33.00   F    7      1       2 19871  64.00   M    7      1       3 19872  64.00   M    7      1       3 19873  30.00   M   11      1       2 19874  35.00   M    9      1       2 19875  56.00   M   11      1       2 19876  65.00   F    8      1       3 19877  25.00   M   17      1       2 19878  32.00   M    8      1       2 19879  23.00   F    8      1       2 19880  30.00   F    8      1       2 19881  30.00   F    8      1       2 19882  37.00   F    8      1       2 19883  30.00   F    8      1       2 19884  28.00   M    8      1       2 19885  36.00   F   15      1       2 19886  65.00   M   10      1       3 19887  50.00   F   11      1       2 19888  25.00   F   20      1       2 19889  28.00   M    7      1       2 19890  31.00   M    9      1       2 19891  29.00   M    7      1       2 19892  29.00   M    7      1       2 19893  30.00   M    9      1       2 19894  29.00   F    9      1       2 19895  58.00   M    9      1       2 19896  33.00   F    9      1       2 19897  38.00   F    9      1       2 19898  19.00   M    9      1       2 19899  14.00   F    9      1       1 19900  30.00   M    9      1       2 19901  13.00   F    9      1       1 19902  25.00   M    9      1       2 19903  15.00   F    9      1       1 19904  38.00   F   12      1       2 19905  24.00   M    9      1       2 19906  28.00   M    9      1       2 19907  41.00   F    9      1       2 19908  19.00   F    9      1       2 19909  19.00   F    9      1       2 19910  50.00   M    9      1       2 19911  24.00   M    9      1       2 19912  40.00   F    9      1       2 19913  32.00   M    9      1       2 19914   6.00   M    7      1       1 19915   3.00   M    7      1       1 19916  34.00   M    9      1       2 19917  37.00   M    9      1       2 19918  30.00   M   20      1       2 19919  31.00   M   20      1       2 19920  22.00   F    9      1       2 19921  77.00   M   11      1       3 19922  72.00   M   20      1       3 19923  32.00   M    9      1       2 19924  35.00   M   20      1       2 19925  50.00   F   20      1       2 19926  69.00   F    9      1       3 19927  42.00   F   21      1       2 19928  32.00   M    2      1       2 19929  25.00   M    8      1       2 19930  24.00   F    9      1       2 19931   3.00   M    9      1       1 19932  50.00   F    9      1       2 19933  25.00   M   10      1       2 19934  50.00   M    9      1       2 19935   7.00   F    9      1       1 19936  31.00   M    9      1       2 19937   3.00   M    9      1       1 19938  26.00   M    9      1       2 19939  45.00   F    9      1       2 19940   1.00   M    9      1       1 19941  30.00   F   12      1       2 19942  12.00   F   12      1       1 19943  40.00   M   13      1       2 19944  62.00   M    8      1       3 19945  39.00   M    8      1       2 19946  57.00   F    8      1       2 19947  30.00   F    9      1       2 19948   7.00   M    8      1       1 19949  30.00   F    9      1       2 19950  45.00   M    9      1       2 19951   2.00   F   15      1       1 19952  30.00   M   13      1       2 19953  18.00   F   13      1       2 19954   9.00   F   15      1       1 19955  29.00   M   10      1       2 19956  30.00   M   15      1       2 19957   8.00   M   16      1       1 19958  52.00   M   11      1       2 19959  12.00   M    9      1       1 19960  24.00   M    9      1       2 19961  24.00   M    9      1       2 19962  22.00   M    9      1       2 19963   1.00   M   14      1       1 19964  29.00   M    4      1       2 19965  40.00   F    9      1       2 19966  42.00   F   10      1       2 19967  28.00   F   13      1       2 19968  19.00   F   14      1       2 19969  21.00   M   14      1       2 19970  57.00   F   20      1       2 19971  54.00   M   13      1       2 19972  24.00   F    9      1       2 19973  25.00   M   13      1       2 19974  30.00   F    9      1       2 19975  64.00   F   11      1       3 19976  72.00   F    8      1       3 19977  43.00   M    8      1       2 19978  30.00   M    8      1       2 19979  35.00   F    8      1       2 19980  60.00   F    6      1       3 19981  75.00   M   12      1       3 19982  19.00   F   17      1       2 19983  13.00   M   17      1       1 19984  25.00   M   12      1       2 19985  50.00   M   17      1       2 19986  20.00   F    6      1       2 19987  20.00   F    6      1       2 19988  60.00   F    6      1       3 19989  20.00   F    6      1       2 19990  32.00   F    6      1       2 19991  28.00   F    6      1       2 19992  68.00   F    8      1       3 19993  20.00   F    6      1       2 19994  70.00   M    6      1       3 19995  30.00   M    6      1       2 19996  20.00   M    6      1       2 19997  52.00   F    6      1       2 19998  33.00   M    6      1       2 19999  56.00   M    9      1       2  [ reached 'max' / getOption(\"max.print\") -- omitted 6742 rows ]"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-preparing-the-data","dir":"Articles > Interactive_tte_tutorial","previous_headings":"The study and the data","what":"Preparing the data","title":"visR","text":"analysis, slight modifications need implemented data. Since visR developed goal speed clinical development process, follows cdisc ADaM standards. Additionally, functions data-type sensitive give informative summaries proper data-type present. following changes necessary: per ADaM guidelines, event indicated 0 censoring indicated >0. data use, 2 stands event 1 stands censoring. Therefore, censoring values adjusted. column Age_Cat parsed integer Sex character. Converting factor result informative output. Furthermore, Age_Cat encoded numerical value 1-3. adding respective names categories, output readable.","code":"# 1. Adjust censoring data$Status = abs(data$Status - 2)  # 2. Convert to factors and add the age categories data = data %>%    dplyr::mutate(      Age_Cat = recode_factor(Age_Cat,                              `1` = \"<18yr\", `2` =\"18yr-60yr\", `3` = \">60yr\"),      Sex     = as.factor(Sex))  data"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-live-demo","dir":"Articles > Interactive_tte_tutorial","previous_headings":"The study and the data","what":"Live Demo","title":"visR","text":"","code":"# Live demo"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-generation-of-summary-tables","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Generation of summary tables","title":"visR","text":"typical clinical report contains demography table, detailing distribution patients across different arms clinical trial. Commonly reported variables age, sex baseline disease characteristics. table sometimes referred Table 1. visR provides set functions generate render tables. rendering, visR enforces specification data source title. functions demonstrated demonstrated . default, table generated whole population stratifier can defined. Exercise 1: Generate table using Sex stratifier. Exercise 2: Generate table using Sex Age_Cat stratifier. Exercise 3: Add additional footnote table.","code":"# Define meta-data DATASOURCE = \"https://github.com/vntkumar8/covid-survival\" TITLE = \"Indian Covid Data\"  data %>% tableone(title = TITLE, datasource = DATASOURCE) # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-note","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Generation of summary tables","what":"Note","title":"visR","text":"notice tables contain NAs stratifier split . workaround first get table 1 data.frame, remove stratifier, render . examples demonstrates modular built compatibility visR. tableone command actually wrapper around functions get_tableone builds data.frame summary statistics render command displays nicely. (fixed upcoming release.)","code":"data %>%    get_tableone(strata = c(\"Sex\", \"Age_Cat\")) %>%    filter(variable != \"Sex\" & variable != \"Age_Cat\") %>%    render(title = TITLE, datasource = DATASOURCE)"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-fitting-survival-models","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Fitting survival models","title":"visR","text":"mentioned, visR defaults following CDISC ADaM standard. data set uses well, estimating plotting survival curves particularly straightforward.","code":"# The dataset that comes with visR is cdisc ADaM compliant  mod = visR::adtte %>% estimate_KM()   # When no strata is specified, the overall patient cohort is analysed mod  mod %>% visr()"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-survival-analysis-of-the-indian-covid-data","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Fitting survival models","what":"Survival analysis of the Indian COVID data","title":"visR","text":"Since ADaM standards utilized beyond clinical trial analysis reporting,, functions visR parameters allowing specification respective column names without modifying actual data. model estimated, different summary statistics easy get_ Exercise 1: Calculate quantiles model.","code":"mod = data %>% estimate_KM(strata = \"Sex\", CNSR = \"Status\", AVAL = \"Stay\") mod mod %>% get_pvalue() # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-plotting-survival-models","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Plotting survival models","title":"visR","text":"survival models can easily plotted using visR. visR function provides several convenient functions adjust aesthetics plot. Exercise 1: Change stratifier survival model Age_Cat. Exercise 2: Plot new model.","code":"mod %>% visr() mod %>%   visr(legend_position = \"top\",        x_label = \"\") mod %>%   visr(legend_position = \"top\",        x_units = \"years\") mod %>%   visr(legend_position = \"top\",        fun = \"log\") # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-improving-survival-plots","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Plotting survival models","what":"Improving survival plots","title":"visR","text":"top , visR provides several functions improve plots. , example, easy add confidence intervals censoring markers. Exercise 1: Add markers censoring. (Hint: functions adding statistics plot visR usually start add_). Also, function provided annotate plot additional information. Exercise 1: Add information statistical significance tests survival plot. (Hint: Combine add_annotation() get_pvalue()).","code":"mod %>%   visr() %>%   add_CI() # Have fun! mod %>%   visr() %>%   visR::add_annotation(label = \"Hello world\", xmin = 0, ymin = 0.5) # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-styling","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Plotting survival models","what":"Styling","title":"visR","text":"Particular attention paid stay compatible ggplot2 ecosystem. Therefore, created objects can example styled like ggplot. Exercise 1: Try recreate Figure 5 Indian Covid data publication close possible using learned now. Alternatively, can create visually pleasing variant.","code":"gg = mod %>% visr()     gg + ggplot2::theme_dark() # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-combining-plots-with-summary-statistics","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Combining plots with summary statistics","title":"visR","text":"Another good practice provide summary statistics accompany survival plot. purpose, visR provides handy function can appended normal piping framework. NOTE: breaks compatibility ggplot2 functions. Therefore, styling happen adding risktable. Analogous functions creating showing Table 1, risktables can also generated independently (using get_ function) can rendered. Exercise 1: Create render risktable survival model","code":"mod %>%   visr() %>%   add_risktable(statlist = c(\"n.risk\", \"n.event\", \"n.censor\")) # Have fun!"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-advanced-usage","dir":"Articles > Interactive_tte_tutorial","previous_headings":"","what":"Advanced usage","title":"visR","text":"demonstrate advanced options might useful cases.","code":""},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-attrition","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Advanced usage","what":"Attrition","title":"visR","text":"Often subgroups population analysed complete data sets need filtered according specific criteria. function get_attrition allows list filter statements explanations, can visualized table (render) plotted (visr). function apply_attrition filters data.frame.","code":"data$Patient = 1:nrow(data)   condition = \"Age_Cat != '<18yr'\"  filters = data %>% get_attrition(subject_column_name = \"Patient\",                                   criteria_descriptions = \"18 and older\",                                   criteria_conditions = condition)   filters %>% visr()  filters %>% render(title = \"Attrition Table\", datasource = DATASOURCE)  data %>% apply_attrition(condition)"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-custom-summary-functions-for-the-table-1","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Advanced usage","what":"Custom summary functions for the Table 1","title":"visR","text":"tableone function accepts custom functions run columns. purpose, different possible data-type considered. simplicity, behaviour numeric columns changed example .","code":"# This is basically the default function but doesn't return the missing values. my_func = function(x) {      if (is.numeric(x)) {        dat = list(       `Mean (SD)` = paste0(format(mean(x, na.rm = TRUE), digits = 3),                             \" (\",                             format(sd(x, na.rm = TRUE), digits = 3),                             \")\"),       `Median (IQR)` = paste0(format(median(x, na.rm = TRUE), digits = 3),                                \" (\",                                format(quantile(x, probs=0.25, na.rm = TRUE), digits = 3),                               \"-\",                                format(quantile(x, probs=0.75, na.rm = TRUE), digits = 3),                                \")\"),       `Min-max` = paste0(format(min(x, na.rm = TRUE), digits = 3),                           \"-\", format(max(x, na.rm = TRUE), digits = 3))     )          list(dat)        } else {          visR::summarize_short(x)        } }  data %>%    get_tableone(strata = c(\"Sex\", \"Age_Cat\"), summary_function = my_func) %>%    filter(variable != \"Sex\" & variable != \"Age_Cat\") %>%    render(title = TITLE, datasource = DATASOURCE)"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-different-rendering-engines-for-table-output","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Advanced usage","what":"Different rendering engines for table output","title":"visR","text":"default, tables rendered gt. However, options like example kable dt also implemented.","code":"mod %>%    get_pvalue() %>%    render(title = TITLE,           datasource = DATASOURCE,           engine = \"kable\") # dt launches a child process which crashes on shinyapps.io?"},{"path":"https://openpharma.github.io/visR/articles/interactive_tte_tutorial/tte_tutorial.html","id":"section-possible-solution-to-the-exercise-for-recreating-figure-5","dir":"Articles > Interactive_tte_tutorial","previous_headings":"Advanced usage","what":"Possible solution to the exercise for recreating figure 5","title":"visR","text":"library(\"learnr\") library(\"visR\") library(\"ggplot2\") library(\"dplyr\") tutorial_options(exercise.timelimit = 120)  # prep work one chunk well can always reference one  # \"exercise-setup\" chunk data = read.csv(\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\") data$Status = abs(data$Status - 2) data = data %>%    dplyr::mutate(      Age_Cat = recode_factor(Age_Cat,                              `1` = \"<18yr\", `2` =\"18yr-60yr\", `3` = \">60yr\"),      Sex     = .factor(Sex)) DATASOURCE = \"https://github.com/vntkumar8/covid-survival\" TITLE = \"Indian Covid Data\" mod = data %>% estimate_KM(strata = \"Sex\", CNSR = \"Status\", AVAL = \"Stay\")  assign(\"data\",       data,       envir = globalenv()) assign(\"DATASOURCE\", DATASOURCE, envir = globalenv()) assign(\"TITLE\",      TITLE,      envir = globalenv()) assign(\"mod\",        mod,        envir = globalenv())  theme_set(theme_bw())   learnr:::register_http_handlers(session, metadata = NULL)  learnr:::prepare_tutorial_state(session)  learnr:::i18n_observe_tutorial_language(input, session)  session$onSessionEnded(function() {         learnr:::event_trigger(session, \"session_stop\")       })  `tutorial-exercise-load-data-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-load-data-code-editor`)), session) output$`tutorial-exercise-load-data-output` <- renderUI({   `tutorial-exercise-load-data-result`() })  learnr:::store_exercise_cache(structure(list(label = \"load-data\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = NULL, chunks = list(list(label = \"load-data\",      code = \"\\n# First, set default ggplot2 theme\\ntheme_set(theme_bw())\\n\\n# , directly load data GitHub repository\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\n\\ndata\\n\",      opts = list(label = \"\\\"load-data\\\"\", exercise = \"TRUE\", exercise.eval = \"TRUE\",          exercise.startover = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = TRUE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"load-data\", exercise = TRUE,          exercise.eval = TRUE, exercise.startover = FALSE, code = c(\"\",          \"# First, set default ggplot2 theme\", \"theme_set(theme_bw())\",          \"\", \"# , directly load data GitHub repository\",          \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",          \"\", \"data\", \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"load-data, exercise = TRUE, exercise.eval = TRUE, exercise.startover = FALSE\",          fig.alt = NULL, fig.num = 0, exercise.df_print = \"default\",          exercise.checker = \"NULL\"), engine = \"r\", version = \"4\"), class = c(\"r\",  \"tutorial_exercise\")))  `tutorial-exercise-prepare-data-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-prepare-data-code-editor`)), session) output$`tutorial-exercise-prepare-data-output` <- renderUI({   `tutorial-exercise-prepare-data-result`() })  learnr:::store_exercise_cache(structure(list(label = \"prepare-data\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"\\n# First, set default ggplot2 theme\\ntheme_set(theme_bw())\\n\\n# , directly load data GitHub repository\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\n\\ndata\\n\",      chunks = list(list(label = \"load-data\", code = \"\\n# First, set default ggplot2 theme\\ntheme_set(theme_bw())\\n\\n# , directly load data GitHub repository\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\n\\ndata\\n\",          opts = list(label = \"\\\"load-data\\\"\", exercise = \"TRUE\",              exercise.eval = \"TRUE\", exercise.startover = \"FALSE\"),          engine = \"r\"), list(label = \"prepare-data\", code = \"\\n# 1. Adjust censoring\\ndata$Status = abs(data$Status - 2)\\n\\n# 2. Convert factors add age categories\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\n\\ndata\",          opts = list(label = \"\\\"prepare-data\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"load-data\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"prepare-data\", exercise = TRUE,          exercise.setup = \"load-data\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# 1. Adjust censoring\",          \"data$Status = abs(data$Status - 2)\", \"\", \"# 2. Convert factors add age categories\",          \"data = data %>%\", \"   dplyr::mutate(\", \"     Age_Cat = recode_factor(Age_Cat,\",          \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",          \"     Sex     = .factor(Sex))\", \"\", \"data\"), .width.px = 700,          .height.px = 432.632880098888, params.src = \"prepare-data, exercise = TRUE, exercise.setup = \\\"load-data\\\", exercise.startover = FALSE, exercise.eval = FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-live-demo-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-live-demo-code-editor`)), session) output$`tutorial-exercise-live-demo-output` <- renderUI({   `tutorial-exercise-live-demo-result`() })  learnr:::store_exercise_cache(structure(list(label = \"live-demo\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"live-demo\", code = \"# Live demo\",          opts = list(label = \"\\\"live-demo\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\", exercise.lines = \"30\", fig.width = \"9\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 9, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 864,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"live-demo\", exercise = TRUE,          exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, exercise.lines = 30, code = \"# Live demo\",          .width.px = 864, .height.px = 432.632880098888,          params.src = \"live-demo, exercise = TRUE, exercise.setup = \\\"setup\\\", exercise.startover = FALSE, exercise.eval = FALSE, exercise.lines = 30, fig.width=9\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-basic-tableone-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-basic-tableone-code-editor`)), session) output$`tutorial-exercise-basic-tableone-output` <- renderUI({   `tutorial-exercise-basic-tableone-result`() })  learnr:::store_exercise_cache(structure(list(label = \"basic-tableone\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"basic-tableone\", code = \"\\n# Define meta-data\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\n\\ndata %>% tableone(title = TITLE, datasource = DATASOURCE)\",          opts = list(label = \"\\\"basic-tableone\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"basic-tableone\", exercise = TRUE,          exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# Define meta-data\",          \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",          \"TITLE = \\\"Indian Covid Data\\\"\", \"\", \"data %>% tableone(title = TITLE, datasource = DATASOURCE)\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"basic-tableone, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-1-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-1-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-1-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-1-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-1\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-1\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-1\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-1\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-1, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-fixed-tableone-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-fixed-tableone-code-editor`)), session) output$`tutorial-exercise-fixed-tableone-output` <- renderUI({   `tutorial-exercise-fixed-tableone-result`() })  learnr:::store_exercise_cache(structure(list(label = \"fixed-tableone\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"fixed-tableone\", code = \"\\ndata %>% \\n  get_tableone(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\")) %>% \\n  filter(variable != \\\"Sex\\\" & variable != \\\"Age_Cat\\\") %>% \\n  render(title = TITLE, datasource = DATASOURCE)\",          opts = list(label = \"\\\"fixed-tableone\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"fixed-tableone\", exercise = TRUE,          exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"data %>% \", \"  get_tableone(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\")) %>% \",          \"  filter(variable != \\\"Sex\\\" & variable != \\\"Age_Cat\\\") %>% \",          \"  render(title = TITLE, datasource = DATASOURCE)\"),          .width.px = 700, .height.px = 432.632880098888,          params.src = \"fixed-tableone, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-cdisc-survival-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-cdisc-survival-code-editor`)), session) output$`tutorial-exercise-cdisc-survival-output` <- renderUI({   `tutorial-exercise-cdisc-survival-result`() })  learnr:::store_exercise_cache(structure(list(label = \"cdisc-survival\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"cdisc-survival\", code = \"\\n# dataset comes visR cdisc ADaM compliant \\nmod = visR::adtte %>% estimate_KM() \\n\\n# strata specified, overall patient cohort analysed\\nmod\\n\\nmod %>% visr()\\n\",          opts = list(label = \"\\\"cdisc-survival\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"cdisc-survival\", exercise = TRUE,          exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# dataset comes visR cdisc ADaM compliant \",          \"mod = visR::adtte %>% estimate_KM() \", \"\", \"# strata specified, overall patient cohort analysed\",          \"mod\", \"\", \"mod %>% visr()\", \"\"), .width.px = 700,          .height.px = 432.632880098888, params.src = \"cdisc-survival, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-indian-survival-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-indian-survival-code-editor`)), session) output$`tutorial-exercise-indian-survival-output` <- renderUI({   `tutorial-exercise-indian-survival-result`() })  learnr:::store_exercise_cache(structure(list(label = \"indian-survival\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"indian-survival\", code = \"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\nmod\",          opts = list(label = \"\\\"indian-survival\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"indian-survival\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",          \"mod\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"indian-survival, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-get_pvalue-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-get_pvalue-code-editor`)), session) output$`tutorial-exercise-get_pvalue-output` <- renderUI({   `tutorial-exercise-get_pvalue-result`() })  learnr:::store_exercise_cache(structure(list(label = \"get_pvalue\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"get_pvalue\", code = \"\\nmod %>% get_pvalue()\\n\",          opts = list(label = \"\\\"get_pvalue\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"get_pvalue\", exercise = TRUE,          exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>% get_pvalue()\",          \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"get_pvalue, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise_1-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise_1-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise_1-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise_1-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise_1\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise_1\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise_1\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise_1\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise_1, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-basic-survival-plot-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-basic-survival-plot-code-editor`)), session) output$`tutorial-exercise-basic-survival-plot-output` <- renderUI({   `tutorial-exercise-basic-survival-plot-result`() })  learnr:::store_exercise_cache(structure(list(label = \"basic-survival-plot\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"basic-survival-plot\", code = \"\\nmod %>% visr()\\n\",          opts = list(label = \"\\\"basic-survival-plot\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"basic-survival-plot\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>% visr()\",          \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"basic-survival-plot, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-basic-survival-plot-options-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-basic-survival-plot-options-code-editor`)), session) output$`tutorial-exercise-basic-survival-plot-options-output` <- renderUI({   `tutorial-exercise-basic-survival-plot-options-result`() })  learnr:::store_exercise_cache(structure(list(label = \"basic-survival-plot-options\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"basic-survival-plot-options\",          code = \"\\nmod %>%\\n  visr(legend_position = \\\"top\\\",\\n       x_label = \\\"\\\")\\nmod %>%\\n  visr(legend_position = \\\"top\\\",\\n       x_units = \\\"years\\\")\\nmod %>%\\n  visr(legend_position = \\\"top\\\",\\n       fun = \\\"log\\\")\\n\",          opts = list(label = \"\\\"basic-survival-plot-options\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"basic-survival-plot-options\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>%\", \"  visr(legend_position = \\\"top\\\",\",          \"       x_label = \\\"\\\")\", \"mod %>%\", \"  visr(legend_position = \\\"top\\\",\",          \"       x_units = \\\"years\\\")\", \"mod %>%\", \"  visr(legend_position = \\\"top\\\",\",          \"       fun = \\\"log\\\")\", \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"basic-survival-plot-options, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-3-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-3-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-3-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-3-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-3\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-3\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-3\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-3\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-3, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-add-x-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-add-x-code-editor`)), session) output$`tutorial-exercise-survival-plot-add-x-output` <- renderUI({   `tutorial-exercise-survival-plot-add-x-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-add-x\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-add-x\", code = \"\\nmod %>%\\n  visr() %>%\\n  add_CI()\\n\",          opts = list(label = \"\\\"survival-plot-add-x\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-add-x\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>%\", \"  visr() %>%\",          \"  add_CI()\", \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-add-x, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-4-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-4-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-4-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-4-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-4\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-4\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-4\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-4\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-4, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-annotate-basic-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-annotate-basic-code-editor`)), session) output$`tutorial-exercise-survival-plot-annotate-basic-output` <- renderUI({   `tutorial-exercise-survival-plot-annotate-basic-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-annotate-basic\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-annotate-basic\",          code = \"\\nmod %>%\\n  visr() %>%\\n  visR::add_annotation(label = \\\"Hello world\\\", xmin = 0, ymin = 0.5)\\n\",          opts = list(label = \"\\\"survival-plot-annotate-basic\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-annotate-basic\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>%\", \"  visr() %>%\",          \"  visR::add_annotation(label = \\\"Hello world\\\", xmin = 0, ymin = 0.5)\",          \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-annotate-basic, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-5-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-5-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-5-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-5-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-5\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-5\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-5\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-5\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-5, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-style-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-style-code-editor`)), session) output$`tutorial-exercise-survival-plot-style-output` <- renderUI({   `tutorial-exercise-survival-plot-style-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-style\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-style\", code = \"\\ngg = mod %>% visr() \\n  \\ngg + ggplot2::theme_dark()\\n\",          opts = list(label = \"\\\"survival-plot-style\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-style\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"gg = mod %>% visr() \",          \"  \", \"gg + ggplot2::theme_dark()\", \"\"), .width.px = 700,          .height.px = 432.632880098888, params.src = \"survival-plot-style, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-6-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-6-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-6-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-6-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-6\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-6\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-6\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-6\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-6, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-risktable-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-risktable-code-editor`)), session) output$`tutorial-exercise-survival-plot-risktable-output` <- renderUI({   `tutorial-exercise-survival-plot-risktable-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-risktable\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-risktable\",          code = \"\\nmod %>%\\n  visr() %>%\\n  add_risktable(statlist = c(\\\"n.risk\\\", \\\"n.event\\\", \\\"n.censor\\\"))\\n\",          opts = list(label = \"\\\"survival-plot-risktable\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\", fig.height = \"7\"), engine = \"r\")),      code_check = NULL, error_check = NULL, check = NULL, solution = NULL,      tests = NULL, options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 7,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-risktable\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>%\", \"  visr() %>%\",          \"  add_risktable(statlist = c(\\\"n.risk\\\", \\\"n.event\\\", \\\"n.censor\\\"))\",          \"\"), .width.px = 700, .height.px = 672, params.src = \"survival-plot-risktable, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE, fig.height = 7\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-survival-plot-exercise-7-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-survival-plot-exercise-7-code-editor`)), session) output$`tutorial-exercise-survival-plot-exercise-7-output` <- renderUI({   `tutorial-exercise-survival-plot-exercise-7-result`() })  learnr:::store_exercise_cache(structure(list(label = \"survival-plot-exercise-7\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"survival-plot-exercise-7\",          code = \"\\n# fun!\\n\", opts = list(label = \"\\\"survival-plot-exercise-7\\\"\",              exercise = \"TRUE\", exercise.setup = \"\\\"setup\\\"\",              exercise.startover = \"FALSE\", exercise.eval = \"FALSE\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"survival-plot-exercise-7\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"# fun!\", \"\"         ), .width.px = 700, .height.px = 432.632880098888,          params.src = \"survival-plot-exercise-7, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-basic-attrition-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-basic-attrition-code-editor`)), session) output$`tutorial-exercise-basic-attrition-output` <- renderUI({   `tutorial-exercise-basic-attrition-result`() })  learnr:::store_exercise_cache(structure(list(label = \"basic-attrition\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"basic-attrition\", code = \"\\ndata$Patient = 1:nrow(data) \\n\\ncondition = \\\"Age_Cat != '<18yr'\\\"\\n\\nfilters = data %>% get_attrition(subject_column_name = \\\"Patient\\\", \\n                                 criteria_descriptions = \\\"18 older\\\", \\n                                 criteria_conditions = condition) \\n\\nfilters %>% visr()\\n\\nfilters %>% render(title = \\\"Attrition Table\\\", datasource = DATASOURCE)\\n\\ndata %>% apply_attrition(condition)\\n\\n\",          opts = list(label = \"\\\"basic-attrition\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"basic-attrition\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"data$Patient = 1:nrow(data) \",          \"\", \"condition = \\\"Age_Cat != '<18yr'\\\"\", \"\", \"filters = data %>% get_attrition(subject_column_name = \\\"Patient\\\", \",          \"                                 criteria_descriptions = \\\"18 older\\\", \",          \"                                 criteria_conditions = condition) \",          \"\", \"filters %>% visr()\", \"\", \"filters %>% render(title = \\\"Attrition Table\\\", datasource = DATASOURCE)\",          \"\", \"data %>% apply_attrition(condition)\", \"\", \"\"), .width.px = 700,          .height.px = 432.632880098888, params.src = \"basic-attrition, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-custom-func-tableone-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-custom-func-tableone-code-editor`)), session) output$`tutorial-exercise-custom-func-tableone-output` <- renderUI({   `tutorial-exercise-custom-func-tableone-result`() })  learnr:::store_exercise_cache(structure(list(label = \"custom-func-tableone\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"custom-func-tableone\", code = \"\\n# basically default function return missing values.\\nmy_func = function(x) {\\n  \\n  (.numeric(x)) {\\n  \\n    dat = list(\\n      `Mean (SD)` = paste0(format(mean(x, na.rm = TRUE), digits = 3), \\n                           \\\" (\\\", \\n                           format(sd(x, na.rm = TRUE), digits = 3), \\n                           \\\")\\\"),\\n      `Median (IQR)` = paste0(format(median(x, na.rm = TRUE), digits = 3), \\n                              \\\" (\\\", \\n                              format(quantile(x, probs=0.25, na.rm = TRUE), digits = 3),\\n                              \\\"-\\\", \\n                              format(quantile(x, probs=0.75, na.rm = TRUE), digits = 3), \\n                              \\\")\\\"),\\n      `Min-max` = paste0(format(min(x, na.rm = TRUE), digits = 3), \\n                         \\\"-\\\", format(max(x, na.rm = TRUE), digits = 3))\\n    )\\n    \\n    list(dat)\\n    \\n  } else {\\n    \\n    visR::summarize_short(x)\\n    \\n  }\\n}\\n\\ndata %>% \\n  get_tableone(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\"), summary_function = my_func) %>% \\n  filter(variable != \\\"Sex\\\" & variable != \\\"Age_Cat\\\") %>% \\n  render(title = TITLE, datasource = DATASOURCE)\",          opts = list(label = \"\\\"custom-func-tableone\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\", exercise.lines = \"35\"),          engine = \"r\")), code_check = NULL, error_check = NULL,      check = NULL, solution = NULL, tests = NULL, options = list(         eval = FALSE, echo = TRUE, results = \"markup\", tidy = FALSE,          tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA,          highlight = FALSE, size = \"normalsize\", background = \"#F7F7F7\",          strip.white = TRUE, cache = 0, cache.path = \"tte_tutorial_cache/html/\",          cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,          autodep = FALSE, cache.rebuild = FALSE, fig.keep = \"high\",          fig.show = \"asis\", fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"custom-func-tableone\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, exercise.lines = 35, code = c(\"\",          \"# basically default function return missing values.\",          \"my_func = function(x) {\", \"  \", \"  (.numeric(x)) {\",          \"  \", \"    dat = list(\", \"      `Mean (SD)` = paste0(format(mean(x, na.rm = TRUE), digits = 3), \",          \"                           \\\" (\\\", \", \"                           format(sd(x, na.rm = TRUE), digits = 3), \",          \"                           \\\")\\\"),\", \"      `Median (IQR)` = paste0(format(median(x, na.rm = TRUE), digits = 3), \",          \"                              \\\" (\\\", \", \"                              format(quantile(x, probs=0.25, na.rm = TRUE), digits = 3),\",          \"                              \\\"-\\\", \", \"                              format(quantile(x, probs=0.75, na.rm = TRUE), digits = 3), \",          \"                              \\\")\\\"),\", \"      `Min-max` = paste0(format(min(x, na.rm = TRUE), digits = 3), \",          \"                         \\\"-\\\", format(max(x, na.rm = TRUE), digits = 3))\",          \"    )\", \"    \", \"    list(dat)\", \"    \", \"  } else {\",          \"    \", \"    visR::summarize_short(x)\", \"    \", \"  }\",          \"}\", \"\", \"data %>% \", \"  get_tableone(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\"), summary_function = my_func) %>% \",          \"  filter(variable != \\\"Sex\\\" & variable != \\\"Age_Cat\\\") %>% \",          \"  render(title = TITLE, datasource = DATASOURCE)\"),          .width.px = 700, .height.px = 432.632880098888,          params.src = \"custom-func-tableone, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE, exercise.lines=35\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-rendering-engines-dt-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-rendering-engines-dt-code-editor`)), session) output$`tutorial-exercise-rendering-engines-dt-output` <- renderUI({   `tutorial-exercise-rendering-engines-dt-result`() })  learnr:::store_exercise_cache(structure(list(label = \"rendering-engines-dt\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"rendering-engines-dt\", code = \"\\nmod %>% \\n  get_pvalue() %>% \\n  render(title = TITLE, \\n         datasource = DATASOURCE, \\n         engine = \\\"kable\\\") # dt launches child process crashes shinyapps.io? \\n\",          opts = list(label = \"\\\"rendering-engines-dt\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"rendering-engines-dt\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"mod %>% \", \"  get_pvalue() %>% \",          \"  render(title = TITLE, \", \"         datasource = DATASOURCE, \",          \"         engine = \\\"kable\\\") # dt launches child process crashes shinyapps.io? \",          \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"rendering-engines-dt, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))  `tutorial-exercise-solution-figure5-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-solution-figure5-code-editor`)), session) output$`tutorial-exercise-solution-figure5-output` <- renderUI({   `tutorial-exercise-solution-figure5-result`() })  learnr:::store_exercise_cache(structure(list(label = \"solution-figure5\", global_setup = structure(c(\"library(\\\"learnr\\\")\",  \"library(\\\"visR\\\")\", \"library(\\\"ggplot2\\\")\", \"library(\\\"dplyr\\\")\",  \"tutorial_options(exercise.timelimit = 120)\", \"\", \"# prep work one chunk well can always reference one \",  \"# \\\"exercise-setup\\\" chunk\", \"data = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\",  \"data$Status = abs(data$Status - 2)\", \"data = data %>%\", \"   dplyr::mutate(\",  \"     Age_Cat = recode_factor(Age_Cat,\", \"                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\",  \"     Sex     = .factor(Sex))\", \"DATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\",  \"TITLE = \\\"Indian Covid Data\\\"\", \"mod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\",  \"\", \"assign(\\\"data\\\",       data,       envir = globalenv())\",  \"assign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\", \"assign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\",  \"assign(\\\"mod\\\",        mod,        envir = globalenv())\", \"\",  \"theme_set(theme_bw())\", \"\"), chunk_opts = list(label = \"setup\",      include = FALSE)), setup = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",      chunks = list(list(label = \"setup\", code = \"library(\\\"learnr\\\")\\nlibrary(\\\"visR\\\")\\nlibrary(\\\"ggplot2\\\")\\nlibrary(\\\"dplyr\\\")\\ntutorial_options(exercise.timelimit = 120)\\n\\n# prep work one chunk well can always reference one \\n# \\\"exercise-setup\\\" chunk\\ndata = read.csv(\\\"https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv\\\")\\ndata$Status = abs(data$Status - 2)\\ndata = data %>%\\n   dplyr::mutate(\\n     Age_Cat = recode_factor(Age_Cat,\\n                             `1` = \\\"<18yr\\\", `2` =\\\"18yr-60yr\\\", `3` = \\\">60yr\\\"),\\n     Sex     = .factor(Sex))\\nDATASOURCE = \\\"https://github.com/vntkumar8/covid-survival\\\"\\nTITLE = \\\"Indian Covid Data\\\"\\nmod = data %>% estimate_KM(strata = \\\"Sex\\\", CNSR = \\\"Status\\\", AVAL = \\\"Stay\\\")\\n\\nassign(\\\"data\\\",       data,       envir = globalenv())\\nassign(\\\"DATASOURCE\\\", DATASOURCE, envir = globalenv())\\nassign(\\\"TITLE\\\",      TITLE,      envir = globalenv())\\nassign(\\\"mod\\\",        mod,        envir = globalenv())\\n\\ntheme_set(theme_bw())\\n\",          opts = list(label = \"\\\"setup\\\"\", include = \"FALSE\"),          engine = \"r\"), list(label = \"solution-figure5\", code = \"\\nm = data %>% estimate_KM(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\"),\\n                         CNSR = \\\"Status\\\", \\n                         AVAL = \\\"Stay\\\")\\n\\nhalf_surv_df = m %>% \\n  get_quantile(probs = 0.5) %>% \\n  dplyr::filter(quantity == \\\"quantile\\\") %>%\\n  dplyr::mutate(pos1 = `50`, pos2 = 0.5) %>%\\n  dplyr::select(-c(quantity, `50`))\\n  \\nhalf_surv_df_horizontal = half_surv_df %>% dplyr::mutate(pos1 = 0)\\n  \\nhalf_surv_df_vertical = half_surv_df %>% dplyr::mutate(pos2 = 0)\\n  \\nhalf_surv_df = rbind(half_surv_df, half_surv_df_horizontal, half_surv_df_vertical)  \\n  \\nm %>%\\n  visr() %>%\\n  add_CI() %>%\\n  add_CNSR() +\\n  ggplot2::geom_line(data = half_surv_df, ggplot2::aes(pos1, pos2, group = strata), linetype = \\\"dashed\\\")\\n\",          opts = list(label = \"\\\"solution-figure5\\\"\", exercise = \"TRUE\",              exercise.setup = \"\\\"setup\\\"\", exercise.startover = \"FALSE\",              exercise.eval = \"FALSE\"), engine = \"r\")), code_check = NULL,      error_check = NULL, check = NULL, solution = NULL, tests = NULL,      options = list(eval = FALSE, echo = TRUE, results = \"markup\",          tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,          comment = NA, highlight = FALSE, size = \"normalsize\",          background = \"#F7F7F7\", strip.white = TRUE, cache = 0,          cache.path = \"tte_tutorial_cache/html/\", cache.vars = NULL,          cache.lazy = TRUE, dependson = NULL, autodep = FALSE,          cache.rebuild = FALSE, fig.keep = \"high\", fig.show = \"asis\",          fig.align = \"default\", fig.path = \"/private/var/folders/4f/x7rnzbxn0ys7dwprbsnq_2rc0000gn/T/RtmpVyK1qk/file16e315fb8f80b/articles/interactive_tte_tutorial/tte_tutorial_files/figure-html/\",          dev = \"ragg_png\", dev.args = list(bg = NA), dpi = 192L,          fig.ext = \"png\", fig.width = 7.29166666666667, fig.height = 4.50659250103008,          fig.env = \"figure\", fig.cap = NULL, fig.scap = NULL,          fig.lp = \"fig:\", fig.subcap = NULL, fig.pos = \"\", .width = 700,          .height = NULL, .extra = NULL, fig.retina = 2L,          external = TRUE, sanitize = FALSE, interval = 1, aniopts = \"controls,loop\",          warning = TRUE, error = FALSE, message = TRUE, render = NULL,          ref.label = NULL, child = NULL, engine = \"r\", split = FALSE,          include = TRUE, purl = TRUE, .parameters = list(),          exercise.timelimit = 120, label = \"solution-figure5\",          exercise = TRUE, exercise.setup = \"setup\", exercise.startover = FALSE,          exercise.eval = FALSE, code = c(\"\", \"m = data %>% estimate_KM(strata = c(\\\"Sex\\\", \\\"Age_Cat\\\"),\",          \"                         CNSR = \\\"Status\\\", \", \"                         AVAL = \\\"Stay\\\")\",          \"\", \"half_surv_df = m %>% \", \"  get_quantile(probs = 0.5) %>% \",          \"  dplyr::filter(quantity == \\\"quantile\\\") %>%\", \"  dplyr::mutate(pos1 = `50`, pos2 = 0.5) %>%\",          \"  dplyr::select(-c(quantity, `50`))\", \"  \", \"half_surv_df_horizontal = half_surv_df %>% dplyr::mutate(pos1 = 0)\",          \"  \", \"half_surv_df_vertical = half_surv_df %>% dplyr::mutate(pos2 = 0)\",          \"  \", \"half_surv_df = rbind(half_surv_df, half_surv_df_horizontal, half_surv_df_vertical)  \",          \"  \", \"m %>%\", \"  visr() %>%\", \"  add_CI() %>%\", \"  add_CNSR() +\",          \"  ggplot2::geom_line(data = half_surv_df, ggplot2::aes(pos1, pos2, group = strata), linetype = \\\"dashed\\\")\",          \"\"), .width.px = 700, .height.px = 432.632880098888,          params.src = \"solution-figure5, exercise=TRUE, exercise.setup = \\\"setup\\\", exercise.startover=FALSE, exercise.eval=FALSE\",          fig.num = 0, exercise.df_print = \"default\", exercise.checker = \"NULL\"),      engine = \"r\", version = \"4\"), class = c(\"r\", \"tutorial_exercise\" )))","code":"m = data %>% estimate_KM(strata = c(\"Sex\", \"Age_Cat\"),                          CNSR = \"Status\",                           AVAL = \"Stay\")  half_surv_df = m %>%    get_quantile(probs = 0.5) %>%    dplyr::filter(quantity == \"quantile\") %>%   dplyr::mutate(pos1 = `50`, pos2 = 0.5) %>%   dplyr::select(-c(quantity, `50`))    half_surv_df_horizontal = half_surv_df %>% dplyr::mutate(pos1 = 0)    half_surv_df_vertical = half_surv_df %>% dplyr::mutate(pos2 = 0)    half_surv_df = rbind(half_surv_df, half_surv_df_horizontal, half_surv_df_vertical)      m %>%   visr() %>%   add_CI() %>%   add_CNSR() +   ggplot2::geom_line(data = half_surv_df, ggplot2::aes(pos1, pos2, group = strata), linetype = \"dashed\")"},{"path":"https://openpharma.github.io/visR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Mark Baillie. Author, maintainer, copyright holder. Diego Saldana. Author. Charlotta Fruechtenicht. Author. Marc Vandemeulebroecke. Author. Thanos Siadimas. Author. Pawel Kawski. Author. Steven Haesendonckx. Author. James Black. Author. Pelagia Alexandra Papadopoulou. Author. Tim Treis. Author. Rebecca Albrecht. Author. Ardalan Mirshani. Contributor. Daniel D. Sjoberg. Author.","code":""},{"path":"https://openpharma.github.io/visR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Baillie M, Saldana D, Fruechtenicht C, Vandemeulebroecke M, Siadimas T, Kawski P, Haesendonckx S, Black J, Alexandra Papadopoulou P, Treis T, Albrecht R, Sjoberg D (2023). visR: Clinical Graphs Tables Adhering Graphical Principles. R package version 0.4.0, https://github.com/openpharma/visR.","code":"@Manual{,   title = {visR: Clinical Graphs and Tables Adhering to Graphical Principles},   author = {Mark Baillie and Diego Saldana and Charlotta Fruechtenicht and Marc Vandemeulebroecke and Thanos Siadimas and Pawel Kawski and Steven Haesendonckx and James Black and Pelagia {Alexandra Papadopoulou} and Tim Treis and Rebecca Albrecht and Daniel D. Sjoberg},   year = {2023},   note = {R package version 0.4.0},   url = {https://github.com/openpharma/visR}, }"},{"path":"https://openpharma.github.io/visR/contributing.html","id":null,"dir":"","previous_headings":"","what":"Contributing to visR","title":"Contributing to visR","text":"detailed info contributing, please see development contributing guide.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to visR","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to visR","text":"want make bigger contribution, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to visR","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"openpharma/visR\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/contributing.html","id":"general-coding-principles","dir":"","previous_headings":"Code style","what":"General coding principles","title":"Contributing to visR","text":"new functions preferably built using widely adapted tidyverse (please see namespace allowed package dependencies). Dependencies kept minimum. Please don’t restyle code nothing PR. Use roxygen2, Markdown syntax, updating creating documentation. Use testthat unit tests. Contributions accompanied extensive user acceptance testing. Please reach team need help. CRAN requires us use TRUE/FALSE, don’t use T/F. Subset using ‘[[’ rather ‘$’ avoid surprises partial matching","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"data-manipulation","dir":"","previous_headings":"Code style","what":"Data Manipulation","title":"Contributing to visR","text":"Data expected present tidy form (one row per observation) unless otherwise noted. Dataframes, rather tibbles returned functions. magrittr pipe (%>%) can used consecutive operations data. models broom package easily converts key information models tidy data tibbles subsequent wrangling visualisation. Note implemented methods lazy extract key information, used common downstream processing. visR custom tidiers extract informtation model objects dataframe users information required downstream processing.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"figures","dir":"","previous_headings":"Code style","what":"Figures","title":"Contributing to visR","text":"Plotting implemented using ggplot2 unless possible. Interactivity may provided using ggplotly - needed plotly packages.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"tables","dir":"","previous_headings":"Code style","what":"Tables","title":"Contributing to visR","text":"Tables always also available raw dataframes.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"testing","dir":"","previous_headings":"","what":"Testing","title":"Contributing to visR","text":"Write tests soon function somewhat usable state. Improving easier starting. Add new test every issue/bug identified. Use vdiffr comparison anything plotted library manually curated plots. Educate break: Rather just stopping parameters provided don’t make sense, like shape = NULL, warn user required argument set reasonable default.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"package-maintenance","dir":"","previous_headings":"","what":"Package maintenance","title":"Contributing to visR","text":"integrated several “watchdogs” testing routine ensure adherence certain standards ’ve set. routines scan codebase possible style violations:","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"cran-watchdog","dir":"","previous_headings":"Package maintenance","what":"CRAN watchdog","title":"Contributing to visR","text":"successful submission CRAN, certain rules set place CRAN team, example usage TRUE/FALSE T/F. continuously ensure compliance , CRAN watchdog scans respective files violations .","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"validation-watchdog-in-pr","dir":"","previous_headings":"Package maintenance","what":"Validation watchdog (in PR)","title":"Contributing to visR","text":"want make easy use package stringent environment might require packages validated. Based discussions similar projects decided implement traceability testing files. Therefore, tests well last change , automatically written files potential scraping. details last change gathered usage git log table contents unit test specifications generated based strings actual tests.","code":""},{"path":"https://openpharma.github.io/visR/contributing.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to visR","text":"Please note visR project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://openpharma.github.io/visR/index.html","id":"visr-","dir":"","previous_headings":"","what":"Clinical Graphs and Tables Adhering to Graphical Principles","title":"Clinical Graphs and Tables Adhering to Graphical Principles","text":"goal visR enable fit--purpose, reusable clinical medical research focused visualizations tables sensible defaults based sound graphical principles. Package documentation","code":""},{"path":"https://openpharma.github.io/visR/index.html","id":"motivation","dir":"","previous_headings":"","what":"Motivation","title":"Clinical Graphs and Tables Adhering to Graphical Principles","text":"using common package visualising data analysis results clinical development process, want positive influence choice visualisation making easy explore different visualisation use impactful visualisations fit--purpose effective visual communication making easy implement best practices judging visualisation chose research question, want facilitate support good practice. can read philosophy architecture repo wiki.","code":""},{"path":"https://openpharma.github.io/visR/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Clinical Graphs and Tables Adhering to Graphical Principles","text":"easiest way get visR install CRAN: Install development version GitHub :","code":"install.packages(\"visR\") # defaults to main branch devtools::install_github(\"openpharma/visR\")"},{"path":"https://openpharma.github.io/visR/index.html","id":"cite-visr","dir":"","previous_headings":"","what":"Cite visR","title":"Clinical Graphs and Tables Adhering to Graphical Principles","text":"","code":"> citation(\"visR\")"},{"path":"https://openpharma.github.io/visR/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Clinical Graphs and Tables Adhering to Graphical Principles","text":"Please note visR project released Contributor Code Conduct. contributing project, agree abide terms. Thank contributors: @AlexandraP-21, @ardeeshany, @bailliem, @cschaerfe, @ddsjoberg, @diego-s, @epijim, @galachad, @gdario, @ginberg, @jameshunterbr, @jinjooshim, @joanacmbarros, @Jonnie-Bevan, @kawap, @kawap93, @kentm4, @krystian8207, @kzalocusky, @lcomm, @lesniewa, @prabhushanmup, @rebecca-albrecht, @SHAESEN2, @thanos-siadimas, @therneau, @thomas-neitmann, @timtreis, @yonicd","code":""},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Survival Object from CDISC Data — Surv_CNSR","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"aim Surv_CNSR() map inconsistency convention survival package CDISC ADaM ADTTE data model. function creates survival object (e.g. survival::Surv()) uses CDISC ADaM ADTTE coding conventions converts arguments status/event variable convention used survival package. AVAL CNSR arguments passed survival::Surv(time = AVAL, event = 1 - CNSR, type = \"right\", origin = 0).","code":""},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"","code":"Surv_CNSR(AVAL, CNSR)"},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"AVAL follow-time. follow-time assumed originate zero. argument passed, default value column/vector named AVAL. CNSR censoring indicator 1=censored 0=death/event. argument passed, default value column/vector named CNSR.","code":""},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"Object class 'Surv'","code":""},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"Surv_CNSR() function creates survival object utilizing expected data structure CDISC ADaM ADTTE data model, mapping CDISC ADaM ADTTE coding conventions expected status/event variable convention used survival package---specifically, coding convention used status/event indicator. survival package expects status/event indicator following format: 0=alive, 1=dead. accepted choices TRUE/FALSE (TRUE = death) 1/2 (2=death). final risky option omit indicator variable, case subjects assumed event. CDISC ADaM ADTTE data model adopts different coding convention event/status indicator. Using convention, event/status variable named 'CNSR' uses following coding: censor = 1, status/event = 0.","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/Surv_CNSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Survival Object from CDISC Data — Surv_CNSR","text":"","code":"# Use the `Surv_CNSR()` function with visR functions adtte %>%   visR::estimate_KM(formula = visR::Surv_CNSR() ~ SEX) #> Call: ~survival::survfit(formula = visR::Surv_CNSR() ~ SEX, data = data) #>  #>         n events median 0.95LCL 0.95UCL #> SEX=F 143     80     64      47      96 #> SEX=M 111     72     41      30      57  # Use the `Surv_CNSR()` function with functions from other packages as well survival::survfit(visR::Surv_CNSR() ~ SEX, data = adtte) #> Call: survfit(formula = visR::Surv_CNSR() ~ SEX, data = adtte) #>  #>         n events median 0.95LCL 0.95UCL #> SEX=F 143     80     64      47      96 #> SEX=M 111     72     41      30      57 survival::survreg(visR::Surv_CNSR() ~ SEX + AGE, data = adtte) %>%   broom::tidy() #> # A tibble: 4 × 5 #>   term        estimate std.error statistic    p.value #>   <chr>          <dbl>     <dbl>     <dbl>      <dbl> #> 1 (Intercept)   3.97      0.993      4.00  0.0000645  #> 2 SEXM         -0.412     0.226     -1.82  0.0689     #> 3 AGE           0.0131    0.0131     0.997 0.319      #> 4 Log(scale)    0.326     0.0676     4.83  0.00000139"},{"path":"https://openpharma.github.io/visR/reference/add_CI.html","id":null,"dir":"Reference","previous_headings":"","what":"Add confidence interval (CI) to visR object — add_CI","title":"Add confidence interval (CI) to visR object — add_CI","text":"Method add pointwise confidence intervals object created visR S3 method. method set use pipe %>%. two options display CI's, \"ribbon\" \"step\" lines. default method available moment.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add confidence interval (CI) to visR object — add_CI","text":"","code":"add_CI(gg, ...)  # S3 method for ggsurvfit add_CI(gg, alpha = 0.1, style = \"ribbon\", linetype, ...)  # S3 method for ggtidycuminc add_CI(gg, alpha = 0.1, style = \"ribbon\", linetype, ...)"},{"path":"https://openpharma.github.io/visR/reference/add_CI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add confidence interval (CI) to visR object — add_CI","text":"gg ggplot created visR ... arguments passed method modify geom_ribbon alpha aesthetic ggplot2 geom_ribbon. Default 0.1. style aesthetic ggplot2 geom_ribbon. Default \"ribbon\". alternative option \"step\" uses line display interval bounds. linetype aesthetic ggplot2 geom_ribbon.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add confidence interval (CI) to visR object — add_CI","text":"Pointwise confidence interval overlayed visR ggplot","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add confidence interval (CI) to visR object — add_CI","text":"","code":"library(visR)  # Estimate KM curves by treatment group survfit_object <- survival::survfit(data = adtte, survival::Surv(AVAL, 1 - CNSR) ~ TRTP)  ## plot without confidence intervals (CI) p <- visR::visr(survfit_object) p   # add CI to plot with default settings p %>% add_CI()   # change transparency of CI ribbon p %>% add_CI(alpha = 0.9, style = \"ribbon\")   # plot CI as a step line instead of ribbon p %>% add_CI(alpha = 0.1, style = \"step\")   # change linetype of CI p %>% add_CI(style = \"step\", linetype = 1)"},{"path":"https://openpharma.github.io/visR/reference/add_CNSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Add censoring symbols to a visR object — add_CNSR","title":"Add censoring symbols to a visR object — add_CNSR","text":"Add censoring symbols visR ggplot S3 method. S3 method adding censoring symbols visR ggplot. method set use pipe %>%. default method available moment.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CNSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add censoring symbols to a visR object — add_CNSR","text":"","code":"add_CNSR(gg, ...)  # S3 method for ggsurvfit add_CNSR(gg, shape = 3, size = 2, ...)  # S3 method for ggtidycuminc add_CNSR(gg, shape = 3, size = 2, ...)"},{"path":"https://openpharma.github.io/visR/reference/add_CNSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add censoring symbols to a visR object — add_CNSR","text":"gg ggplot created visR ... arguments passed method modify geom_point shape aesthetic ggplot2 geom_point. Default 3. size aesthetic ggplot2 geom_point. Default 2.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CNSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add censoring symbols to a visR object — add_CNSR","text":"Censoring symbols overlayed visR ggplot","code":""},{"path":"https://openpharma.github.io/visR/reference/add_CNSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add censoring symbols to a visR object — add_CNSR","text":"","code":"library(visR)  # Estimate KM curves by treatment group survfit_object <- survival::survfit(data = adtte, survival::Surv(AVAL, 1 - CNSR) ~ TRTP)  ## plot without confidence intervals p <- visR::visr(survfit_object) p   # add censoring to plot p %>% visR::add_CNSR()   # change censor symbol shape p %>% visR::add_CNSR(shape = 1)   # change size and shape p %>% visR::add_CNSR(size = 4, shape = 2)"},{"path":"https://openpharma.github.io/visR/reference/add_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Add annotations to a visR object — add_annotation","title":"Add annotations to a visR object — add_annotation","text":"Wrapper around ggplot2::annotation_custom simplified annotation ggplot2 plots. function accepts string, dataframe, data.table, tibble customized objects class gtable places specified location ggplot. layout fixed: bold column headers plain body. font size type can chosen. initial plot individual annotation stored attribute component final object.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add annotations to a visR object — add_annotation","text":"","code":"add_annotation(   gg = NULL,   label = NULL,   base_family = \"sans\",   base_size = 11,   xmin = -Inf,   xmax = Inf,   ymin = -Inf,   ymax = Inf )"},{"path":"https://openpharma.github.io/visR/reference/add_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add annotations to a visR object — add_annotation","text":"gg Object class ggplot. label String, dataframe, data.table, tibble used annotate ggplot. base_family character. Base font family base_size numeric. Base font size pt xmin x coordinates giving horizontal location raster fit annotation. xmax x coordinates giving horizontal location raster fit annotation. ymin y coordinates giving vertical location raster fit annotation. ymax y coordinates giving vertical location raster fit annotation.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add annotations to a visR object — add_annotation","text":"Object class ggplot added annotation object class gtable.","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/add_annotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add annotations to a visR object — add_annotation","text":"","code":"## Estimate survival surv_object <- visR::estimate_KM(data = adtte, strata = \"TRTP\")  ## We want to annotate the survival KM plot with a simple string comment visR::visr(surv_object) %>%   visR::add_annotation(     label = \"My simple comment\",     base_family = \"sans\",     base_size = 15,     xmin = 110,     xmax = 180,     ymin = 0.80   )   ## Currently, care needs to be taken on the x-y values relative ## to the plot data area. Here we are plotting outside of the data area. visR::visr(surv_object) %>%   visR::add_annotation(     label = \"My simple comment\",     base_family = \"sans\",     base_size = 15,     xmin = 210,     xmax = 380,     ymin = 1.0   )    ## We may also want to annotate a KM plot with information ## from additional tests or estimates. This example we annotate ## with p-values contained in a tibble  ## we calculate p-values for \"Equality across strata\" lbl <- visR::get_pvalue(surv_object,   statlist = c(\"test\", \"pvalue\"),   type = \"All\" )  ## display p-values lbl #>   Equality across strata p-value #> 1               Log-Rank  <0.001 #> 2               Wilcoxon  <0.001 #> 3            Tarone-Ware  <0.001  ## Now annotate survival KM plot with the p-values visR::visr(surv_object) %>%   visR::add_annotation(     label = lbl,     base_family = \"sans\",     base_size = 9,     xmin = 100,     xmax = 180,     ymin = 0.80   )"},{"path":"https://openpharma.github.io/visR/reference/add_highlight.html","id":null,"dir":"Reference","previous_headings":"","what":"Highlight a specific strata — add_highlight","title":"Highlight a specific strata — add_highlight","text":"S3 method highlighting specific strata lowering opacity strata.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_highlight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Highlight a specific strata — add_highlight","text":"","code":"add_highlight(gg, ...)  # S3 method for ggsurvfit add_highlight(gg = NULL, strata = NULL, bg_alpha = 0.2, ...)"},{"path":"https://openpharma.github.io/visR/reference/add_highlight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Highlight a specific strata — add_highlight","text":"gg ggplot created visR ... arguments passed method strata String representing name value strata highlighted shown legend. bg_alpha numerical value 0 1 used decrease opacity strata chosen highlighted strata. strata's existing alpha values multiplied bg_alpha decrease opacity, highlighting target strata. works colour fill properties, example present applying visR::add_CI().","code":""},{"path":"https://openpharma.github.io/visR/reference/add_highlight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Highlight a specific strata — add_highlight","text":"input ggsurvfit object adjusted alpha values","code":""},{"path":"https://openpharma.github.io/visR/reference/add_highlight.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Highlight a specific strata — add_highlight","text":"","code":"adtte %>%   visR::estimate_KM(strata = \"SEX\") %>%   visR::visr() %>%   visR::add_CI(alpha = 0.4) %>%   visR::add_highlight(strata = \"M\", bg_alpha = 0.2)   strata <- c(\"Placebo\", \"Xanomeline Low Dose\")  adtte %>%   visR::estimate_KM(strata = \"TRTP\") %>%   visR::visr() %>%   visR::add_CI(alpha = 0.4) %>%   visR::add_highlight(strata = strata, bg_alpha = 0.2)"},{"path":"https://openpharma.github.io/visR/reference/add_quantiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Add quantile indicators to visR plot — add_quantiles","title":"Add quantile indicators to visR plot — add_quantiles","text":"Method add quantile lines plot.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_quantiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add quantile indicators to visR plot — add_quantiles","text":"","code":"add_quantiles(gg, ...)  # S3 method for ggsurvfit add_quantiles(   gg,   quantiles = 0.5,   linetype = \"dashed\",   linecolour = \"grey50\",   alpha = 1,   ... )"},{"path":"https://openpharma.github.io/visR/reference/add_quantiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add quantile indicators to visR plot — add_quantiles","text":"gg ggplot created visR ... arguments passed method modify geom_line quantiles vector quantiles displayed probability scale, default: 0.5 linetype string indicating linetype described aesthetics ggplot2 geom_line, default: dashed (also supports \"mixed\" -> horizontal lines solid, vertical ones dashed) linecolour string indicating linetype described aesthetics ggplot2 geom_line, default: grey, (also supports \"strata\" -> horizontal lines grey50, vertical ones colour respective strata) alpha numeric value 0 1 described aesthetics ggplot2 geom_line, default: 1","code":""},{"path":"https://openpharma.github.io/visR/reference/add_quantiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add quantile indicators to visR plot — add_quantiles","text":"Lines indicating quantiles overlayed visR ggplot","code":""},{"path":"https://openpharma.github.io/visR/reference/add_quantiles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add quantile indicators to visR plot — add_quantiles","text":"","code":"library(visR)  adtte %>%   estimate_KM(\"SEX\") %>%   visr() %>%   add_quantiles() #> Warning: no non-missing arguments to min; returning Inf   adtte %>%   estimate_KM(\"SEX\") %>%   visr() %>%   add_quantiles(quantiles = c(0.25, 0.50)) #> Warning: no non-missing arguments to min; returning Inf   adtte %>%   estimate_KM(\"SEX\") %>%   visr() %>%   add_quantiles(     quantiles = c(0.25, 0.50),     linetype = \"solid\",     linecolour = \"grey\"   ) #> Warning: no non-missing arguments to min; returning Inf   adtte %>%   estimate_KM(\"SEX\") %>%   visr() %>%   add_quantiles(     quantiles = c(0.25, 0.50),     linetype = \"mixed\",     linecolour = \"strata\"   ) #> Warning: no non-missing arguments to min; returning Inf"},{"path":"https://openpharma.github.io/visR/reference/add_risktable.html","id":null,"dir":"Reference","previous_headings":"","what":"Add risk tables to visR plots through an S3 method — add_risktable","title":"Add risk tables to visR plots through an S3 method — add_risktable","text":"S3 method adding risk tables visR plots. function following workflow: risktables calculated using get_risktable risktables placed underneath visR plots using plot_grid initial visR plot individual risktables stored attribute component final object allow post-modification individual plots desired","code":""},{"path":"https://openpharma.github.io/visR/reference/add_risktable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add risk tables to visR plots through an S3 method — add_risktable","text":"","code":"add_risktable(gg, ...)  # S3 method for ggsurvfit add_risktable(   gg,   times = NULL,   statlist = \"n.risk\",   label = NULL,   group = \"strata\",   collapse = FALSE,   rowgutter = 0.16,   ... )  # S3 method for ggtidycuminc add_risktable(   gg,   times = NULL,   statlist = \"n.risk\",   label = NULL,   group = \"strata\",   collapse = FALSE,   rowgutter = 0.16,   ... )"},{"path":"https://openpharma.github.io/visR/reference/add_risktable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add risk tables to visR plots through an S3 method — add_risktable","text":"gg visR plot class ggsurvfit ggtidycmprsk ... arguments passed method add_risktable times Numeric vector indicating times risk set, censored subjects, events calculated. statlist Character vector indicating summary data present. Current choices \"n.risk\" \"n.event\" \"n.censor\", \"cum.event\", \"cum.censor\". Default \"n.risk\". label Character vector labels statlist. Default matches \"n.risk\" \"risk\", \"n.event\" \"Events\", \"n.censor\" \"Censored\", \"cum.event\" \"Cum. Event\", \"cum.censor\" \"Cum. Censor\". group String indicating grouping variable risk tables. Current options : \"strata\": groups risk tables per stratum. label specifies label within risk table. strata levels used titles risk tables. default \"statlist\": groups risk tables per statlist. label specifies title risk table. strata levels used labeling within risk table. Default \"strata\". collapse Boolean, indicates whether present data overall. Default FALSE. rowgutter numeric relative value 0 1 indicates height used table versus height used plot, described cowplot::plot_grid(rel_heights=). default 0.16.","code":""},{"path":"https://openpharma.github.io/visR/reference/add_risktable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add risk tables to visR plots through an S3 method — add_risktable","text":"Object class ggplot added risk table.","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/add_risktable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add risk tables to visR plots through an S3 method — add_risktable","text":"","code":"## Display 2 risk tables, 1 per statlist adtte %>%   visR::estimate_KM(strata = \"TRTP\") %>%   visR::visr() %>%   visR::add_risktable(     label = c(\"Subjects at Risk\", \"Censored\"),     statlist = c(\"n.risk\", \"n.censor\", \"n.event\"),     group = \"statlist\"   )   ## Display overall risk table at selected times adtte %>%   visR::estimate_KM(strata = \"TRTP\") %>%   visR::visr() %>%   visR::add_risktable(     label = c(\"Subjects at Risk\", \"Censored\"),     statlist = c(\"n.risk\", \"n.censor\"),     collapse = TRUE,     times = c(0, 20, 40, 60)   )   ## Add risk set as specified times adtte %>%   visR::estimate_KM(strata = \"TRTP\") %>%   visR::visr() %>%   visR::add_risktable(times = c(0, 20, 40, 100, 111, 200))"},{"path":"https://openpharma.github.io/visR/reference/adtte.html","id":null,"dir":"Reference","previous_headings":"","what":"adtte - CDISC ADaM compliant time to event data set — adtte","title":"adtte - CDISC ADaM compliant time to event data set — adtte","text":"ADTTE data copied 2013 CDISC Pilot","code":""},{"path":"https://openpharma.github.io/visR/reference/adtte.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"adtte - CDISC ADaM compliant time to event data set — adtte","text":"","code":"adtte"},{"path":"https://openpharma.github.io/visR/reference/adtte.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"adtte - CDISC ADaM compliant time to event data set — adtte","text":"data frame 254 rows 26 variables: STUDYID Study Identifier SITEID Study Site Identifier USUBJID Unique Subject Identifier AGE Age AGEGR1 Pooled Age Group 1 AGEGR1N Pooled Age Group 1 (N) RACE Race RACEN Race (N) SEX Sex TRTSDT Date First Exposure Treatment TRTEDT Date Last Exposure Treatment TRTDUR Duration treatment (days) TRTP Planned Treatment TRTA Actual Treatment TRTAN Actual Treatment (N) PARAM Parameter Description PARAMCD Parameter Code AVAL Analysis Value STARTDT Time Event Origin Date Subject ADT Analysis Date CNSR Censor EVNTDESC Event Censoring Description SRCDOM Source Domain SRCVAR Source Variable SRCSEQ Source Sequence Number SAFFL Safety Population Flag","code":""},{"path":"https://openpharma.github.io/visR/reference/adtte.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"adtte - CDISC ADaM compliant time to event data set — adtte","text":"CDISC SDTM/ADAM Pilot Project. https://github.com/phuse-org/phuse-scripts/tree/master/data","code":""},{"path":"https://openpharma.github.io/visR/reference/adtte.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"adtte - CDISC ADaM compliant time to event data set — adtte","text":"","code":"data(\"adtte\")"},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Align multiple ggplot graphs, taking into account the legend — align_plots","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"function aligns multiple ggplot graphs making width taking account legend width.","code":""},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"","code":"align_plots(pltlist)"},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"pltlist list plots","code":""},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"List ggplot equal width.","code":""},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"https://stackoverflow.com/questions/26159495","code":""},{"path":"https://openpharma.github.io/visR/reference/align_plots.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Align multiple ggplot graphs, taking into account the legend — align_plots","text":"","code":"# \\donttest{  ## create 2 graphs p1 <- ggplot2::ggplot(adtte, ggplot2::aes(x = as.numeric(AGE), fill = \"Age\")) +   ggplot2::geom_histogram(bins = 15)  p2 <- ggplot2::ggplot(adtte, ggplot2::aes(x = as.numeric(AGE))) +   ggplot2::geom_histogram(bins = 15)  ## default alignment does not take into account legend size cowplot::plot_grid(   plotlist = list(p1, p2),   align = \"none\",   nrow = 2 )   ## align_plots() takes into account legend width cowplot::plot_grid(   plotlist = visR::align_plots(pltlist = list(p1, p2)),   align = \"none\",   nrow = 2 )  # }"},{"path":"https://openpharma.github.io/visR/reference/apply_attrition.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","title":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","text":"Apply list inclusion/exclusion criteria patient-level dataframe","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_attrition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","text":"","code":"apply_attrition(data, criteria_conditions)"},{"path":"https://openpharma.github.io/visR/reference/apply_attrition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","text":"data data.frame. Data set filtered criteria_conditions character dplyr-filter compatible conditions filtering criteria. conditions applied filter input data set obtain final analysis data set","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_attrition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","text":"Filtered data frame","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_attrition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply list of inclusion/exclusion criteria to a patient-level dataframe — apply_attrition","text":"","code":"adtte_filtered <- visR::apply_attrition(adtte,   criteria_conditions = c(     \"TRTP=='Placebo'\", \"AGE>=75\",     \"RACE=='WHITE'\", \"SITEID==709\"   ) )"},{"path":"https://openpharma.github.io/visR/reference/apply_theme.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a theme to a ggplot object. — apply_theme","title":"Applies a theme to a ggplot object. — apply_theme","text":"Takes styling options defined visR::define_theme applies plot.","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_theme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a theme to a ggplot object. — apply_theme","text":"","code":"apply_theme(gg, visR_theme_dict = NULL)"},{"path":"https://openpharma.github.io/visR/reference/apply_theme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a theme to a ggplot object. — apply_theme","text":"gg object class ggplot visR_theme_dict nested list containing possible font options","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_theme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies a theme to a ggplot object. — apply_theme","text":"object class ggplot","code":""},{"path":"https://openpharma.github.io/visR/reference/apply_theme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a theme to a ggplot object. — apply_theme","text":"","code":"library(visR)  theme <- visR::define_theme(   strata = list(     \"SEX\" = list(       \"F\" = \"red\",       \"M\" = \"blue\"     ),     \"TRTA\" = list(       \"Placebo\" = \"cyan\",       \"Xanomeline High Dose\" = \"purple\",       \"Xanomeline Low Dose\" = \"brown\"     )   ),   fontsizes = list(     \"axis\" = 12,     \"ticks\" = 10,     \"legend_title\" = 10,     \"legend_text\" = 8   ),   fontfamily = \"Helvetica\",   grid = FALSE,   bg = \"transparent\",   legend_position = \"top\" )   gg <- adtte %>%   visR::estimate_KM(strata = \"SEX\") %>%   visR::visr() %>%   visR::apply_theme(theme) gg"},{"path":"https://openpharma.github.io/visR/reference/brca_cohort.html","id":null,"dir":"Reference","previous_headings":"","what":"Cancer survival data — brca_cohort","title":"Cancer survival data — brca_cohort","text":"Creation script data-raw","code":""},{"path":"https://openpharma.github.io/visR/reference/brca_cohort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cancer survival data — brca_cohort","text":"","code":"brca_cohort"},{"path":"https://openpharma.github.io/visR/reference/brca_cohort.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cancer survival data — brca_cohort","text":"object class data.frame 1098 rows 10 columns.","code":""},{"path":"https://openpharma.github.io/visR/reference/define_theme.html","id":null,"dir":"Reference","previous_headings":"","what":"Provides a simple wrapper for themes — define_theme","title":"Provides a simple wrapper for themes — define_theme","text":"function collects several lists present. absent, reasonable defaults used. strata defined theme, default grey50 presented legend.","code":""},{"path":"https://openpharma.github.io/visR/reference/define_theme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provides a simple wrapper for themes — define_theme","text":"","code":"define_theme(   strata = NULL,   fontsizes = NULL,   fontfamily = \"Helvetica\",   grid = FALSE,   bg = \"transparent\",   legend_position = NULL )"},{"path":"https://openpharma.github.io/visR/reference/define_theme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provides a simple wrapper for themes — define_theme","text":"strata named list containing different strata name:colour value pairs fontsizes named list containing font sizes different options fontfamily string name supported font grid boolean specifies whether major minor grid drawn. drawing major minor gridlines can manipulated separately using boolean indicator named list elements major minor. bg string defining colour background plot legend_position string defining legend position. Valid options NULL, 'top' 'bottom' 'right' 'left'","code":""},{"path":"https://openpharma.github.io/visR/reference/define_theme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Provides a simple wrapper for themes — define_theme","text":"Nested list styling preferences ggplot object","code":""},{"path":"https://openpharma.github.io/visR/reference/define_theme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provides a simple wrapper for themes — define_theme","text":"","code":"theme <- visR::define_theme(   strata = list(\"SEX\" = list(     \"F\" = \"red\",     \"M\" = \"blue\"   )),   fontsizes = list(     \"axis\" = 12,     \"ticks\" = 10,     \"legend_title\" = 10,     \"legend_text\" = 8   ),   fontfamily = \"Helvetica\",   grid = list(     \"major\" = FALSE,     \"minor\" = FALSE   ),   bg = \"transparent\",   legend_position = \"top\" )"},{"path":"https://openpharma.github.io/visR/reference/dot-convert_alpha.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts an alpha value between its numeric and its hex-encoded form. — .convert_alpha","title":"Converts an alpha value between its numeric and its hex-encoded form. — .convert_alpha","text":"function accepts numeric (NULL/NA) two-character hex encoded alpha representation returns respective representation.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-convert_alpha.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts an alpha value between its numeric and its hex-encoded form. — .convert_alpha","text":"","code":".convert_alpha(numeric_alpha = NULL, hex_alpha = NULL)"},{"path":"https://openpharma.github.io/visR/reference/dot-convert_alpha.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts an alpha value between its numeric and its hex-encoded form. — .convert_alpha","text":"numeric_alpha numerical value 0 1. hex_alpha two-letter character string.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-convert_alpha.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts an alpha value between its numeric and its hex-encoded form. — .convert_alpha","text":"numeric_alpha specified, two-letter representation returned. hex_alpha specified, numeric representation returned.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_alpha_from_hex_colour.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the numerical alpha representation of #RRGGBBAA colour — .get_alpha_from_hex_colour","title":"Extract the numerical alpha representation of #RRGGBBAA colour — .get_alpha_from_hex_colour","text":"RGB colours can encoded hexadecimal values, example internally used ggplot2. , numerical RGB values mapped 0-255 value range two-character hex-values. yields string form '#RRGGBB'. Additionally, fourth optional block can present encoding alpha transparency colour. extends string '#RRGGBBAA'. function takes string input hex_colour, extracts 'AA' part returns numerical representation .","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_alpha_from_hex_colour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the numerical alpha representation of #RRGGBBAA colour — .get_alpha_from_hex_colour","text":"","code":".get_alpha_from_hex_colour(hex_colour = NULL)"},{"path":"https://openpharma.github.io/visR/reference/dot-get_alpha_from_hex_colour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the numerical alpha representation of #RRGGBBAA colour — .get_alpha_from_hex_colour","text":"hex_colour string format '#RRGGBBAA'","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_alpha_from_hex_colour.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the numerical alpha representation of #RRGGBBAA colour — .get_alpha_from_hex_colour","text":"numeric representation colors' alpha value, rounded 2 digits.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_strata.html","id":null,"dir":"Reference","previous_headings":"","what":"Get strata level combinations — .get_strata","title":"Get strata level combinations — .get_strata","text":"Extracts strata level combinations survival objects without specified strata.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_strata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get strata level combinations — .get_strata","text":"","code":".get_strata(strata)"},{"path":"https://openpharma.github.io/visR/reference/dot-get_strata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get strata level combinations — .get_strata","text":"strata strata survival object","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-get_strata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get strata level combinations — .get_strata","text":"strata level combinations survival objects without specified strata.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-replace_hex_alpha.html","id":null,"dir":"Reference","previous_headings":"","what":"Replaces the AA part of a #RRGGBBAA hex-colour. — .replace_hex_alpha","title":"Replaces the AA part of a #RRGGBBAA hex-colour. — .replace_hex_alpha","text":"RGB colours can encoded hexadecimal values, example internally used ggplot2. , numerical RGB values mapped 0-255 value range two-character hex-values. yields string form '#RRGGBB'. Additionally, fourth optional block can present encoding alpha transparency colour. extends string '#RRGGBBAA'. function takes '#RRGGBBAA' string input colour two-character hex-representation alpha value input new_alpha, replaces 'AA' part colour new_alpha returns new colour.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-replace_hex_alpha.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replaces the AA part of a #RRGGBBAA hex-colour. — .replace_hex_alpha","text":"","code":".replace_hex_alpha(colour, new_alpha)"},{"path":"https://openpharma.github.io/visR/reference/dot-replace_hex_alpha.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replaces the AA part of a #RRGGBBAA hex-colour. — .replace_hex_alpha","text":"colour character string format #RRGGBBAA. new_alpha two-character string new alpha value.","code":""},{"path":"https://openpharma.github.io/visR/reference/dot-replace_hex_alpha.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replaces the AA part of a #RRGGBBAA hex-colour. — .replace_hex_alpha","text":"hex-encoded RGBA colour.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"function wrapper around survival::survfit.formula() perform Kaplan-Meier analysis, assuming right-censored data. result object class survfit can used downstream functions methods rely survfit class. function can leverage conventions controlled vocabulary CDISC ADaM ADTTE data model, also works standard, non-CDISC datasets formula argument.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"","code":"estimate_KM(   data = NULL,   strata = NULL,   CNSR = \"CNSR\",   AVAL = \"AVAL\",   formula = NULL,   ... )"},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"data data frame. dataset expected one record per subject per analysis parameter. Rows missing observations included analysis removed. AVAL, CNSR, strata arguments used construct formula passed survival::survfit(formula=Surv(AVAL, 1-CNSR)~strata). arguments' default values follow naming conventions CDISC. AVAL Analysis value Time--Event analysis. Default \"AVAL\", per CDISC ADaM guiding principles. CNSR Censor Time--Event analysis. Default \"CNSR\", per CDISC ADaM guiding principles. expected CNSR = 1 censoring CNSR = 0 event interest. strata Character vector, representing strata Time--Event analysis. NULL, overall analysis performed. Default NULL. formula formula Surv() RHS stratifying variables LHS. Use ~1 LHS unstratified estimates. argument passed survival::survfit(formula=). argument used, arguments AVAL, CNSR, strata ignored. ... additional arguments passed ellipsis call survival::survfit.formula(...). Use ?survival::survfit.formula ?survival::survfitCI information.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"survfit object ready downstream processing estimation visualization functions methods.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"estimation-of-survfit-object","dir":"Reference","previous_headings":"","what":"Estimation of 'survfit' object","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"estimate_KM() function utilizes defaults survival::survfit(): Kaplan Meier estimate estimated directly (stype = 1). cumulative hazard estimated using Nelson-Aalen estimator (ctype = 1): H.tilde = cumsum(x$n.event/x$n.risk). MLE (H.hat(t) = -log(S.hat(t))) requested. two-sided pointwise 0.95 confidence interval estimated using log transformation (conf.type = \"log\"). strata present, returned survfit object supplemented named list stratum associated label. support full traceability, data set name captured named list call captured within corresponding environment.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"param-paramcd-and-cdisc","dir":"Reference","previous_headings":"","what":"PARAM/PARAMCD and CDISC","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"data frame includes columns PARAM/PARAMCD (part CDISC format), function expects data filtered parameter interest.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"https://github.com/therneau/survival","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/estimate_KM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrapper for Kaplan-Meier Time-to-Event analysis — estimate_KM","text":"","code":"## No stratification visR::estimate_KM(data = adtte) #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     1, data = data) #>  #>        n events median 0.95LCL 0.95UCL #> [1,] 254    152     51      44      70  ## Stratified Kaplan-Meier analysis by `TRTP` visR::estimate_KM(data = adtte, strata = \"TRTP\") #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     TRTP, data = data) #>  #>                            n events median 0.95LCL 0.95UCL #> TRTP=Placebo              86     29     NA      NA      NA #> TRTP=Xanomeline High Dose 84     61     36      25      47 #> TRTP=Xanomeline Low Dose  84     62     33      28      51  ## Stratified Kaplan-Meier analysis by `TRTP` and `SEX` visR::estimate_KM(data = adtte, strata = c(\"TRTP\", \"SEX\")) #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     TRTP + SEX, data = data) #>  #>                                   n events median 0.95LCL 0.95UCL #> TRTP=Placebo, SEX=F              53     19     NA      90      NA #> TRTP=Placebo, SEX=M              33     10     NA      NA      NA #> TRTP=Xanomeline High Dose, SEX=F 40     27     46      30      70 #> TRTP=Xanomeline High Dose, SEX=M 44     34     25      20      46 #> TRTP=Xanomeline Low Dose, SEX=F  50     34     43      29     100 #> TRTP=Xanomeline Low Dose, SEX=M  34     28     27      21      51  ## Stratification with one level visR::estimate_KM(data = adtte, strata = \"PARAMCD\") #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     PARAMCD, data = data) #>  #>        n events median 0.95LCL 0.95UCL #> [1,] 254    152     51      44      70  ## Analysis on subset of adtte visR::estimate_KM(data = adtte[adtte$SEX == \"F\", ]) #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     1, data = data) #>  #>        n events median 0.95LCL 0.95UCL #> [1,] 143     80     64      47      96  ## Modify the default analysis by using the ellipsis visR::estimate_KM(   data = adtte, strata = NULL,   type = \"kaplan-meier\", conf.int = FALSE, timefix = TRUE ) #> Call: ~survival::survfit(formula = survival::Surv(AVAL, 1 - CNSR) ~  #>     1, data = data, timefix = TRUE, type = \"kaplan-meier\", conf.int = FALSE) #>  #>        n events median #> [1,] 254    152     51  ## Example working with non CDISC data head(survival::veteran[c(\"time\", \"status\", \"trt\")]) #>   time status trt #> 1   72      1   1 #> 2  411      1   1 #> 3  228      1   1 #> 4  126      1   1 #> 5  118      1   1 #> 6   10      1   1  # Using non-CDSIC data visR::estimate_KM(data = survival::veteran, formula = Surv(time, status) ~ trt) #> Call: ~survival::survfit(formula = Surv(time, status) ~ trt, data = data) #>  #>        n events median 0.95LCL 0.95UCL #> trt=1 69     64  103.0      59     132 #> trt=2 68     64   52.5      44      95"},{"path":"https://openpharma.github.io/visR/reference/estimate_cuminc.html","id":null,"dir":"Reference","previous_headings":"","what":"Competing Events Cumulative Incidence — estimate_cuminc","title":"Competing Events Cumulative Incidence — estimate_cuminc","text":"Function creates cumulative incidence object using tidycmprsk::cuminc() function.","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_cuminc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Competing Events Cumulative Incidence — estimate_cuminc","text":"","code":"estimate_cuminc(   data = NULL,   strata = NULL,   CNSR = \"CNSR\",   AVAL = \"AVAL\",   conf.int = 0.95,   ... )"},{"path":"https://openpharma.github.io/visR/reference/estimate_cuminc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Competing Events Cumulative Incidence — estimate_cuminc","text":"data data frame. dataset expected one record per subject per analysis parameter. Rows missing observations included analysis removed. AVAL, CNSR, strata arguments used construct formula passed tidycmprsk::cuminc(formula=). AVAL Analysis value Time--Event analysis. Default \"AVAL\", per CDISC ADaM guiding principles. CNSR Column name indicating outcome censoring statuses. Column must factor first level indicates censoring, next level outcome interest, remaining levels competing events. Default \"CNSR\" strata Character vector, representing strata Time--Event analysis. NULL, overall analysis performed. Default NULL. conf.int Confidence internal level. Default 0.95. Parameter passed tidycmprsk::cuminc(conf.level=) ... Additional argument passed tidycmprsk::cuminc()","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_cuminc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Competing Events Cumulative Incidence — estimate_cuminc","text":"cumulative incidence object explained https://mskcc-epi-bio.github.io/tidycmprsk/reference/cuminc.html","code":""},{"path":"https://openpharma.github.io/visR/reference/estimate_cuminc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Competing Events Cumulative Incidence — estimate_cuminc","text":"","code":"cuminc <-   visR::estimate_cuminc(     data = tidycmprsk::trial,     strata = \"trt\",     CNSR = \"death_cr\",     AVAL = \"ttdeath\"   ) cuminc #>  #> ── cuminc() ──────────────────────────────────────────────────────────────────── #>  #> • Failure type \"death from cancer\" #> strata   time   n.risk   estimate   std.error   95% CI           #> Drug A   5.00   97       0.000      0.000       NA, NA           #> Drug A   10.0   94       0.020      0.014       0.004, 0.065     #> Drug A   15.0   83       0.071      0.026       0.031, 0.134     #> Drug A   20.0   61       0.173      0.039       0.106, 0.255     #> Drug B   5.00   102      0.000      0.000       NA, NA           #> Drug B   10.0   95       0.039      0.019       0.013, 0.090     #> Drug B   15.0   75       0.167      0.037       0.102, 0.246     #> Drug B   20.0   55       0.255      0.043       0.175, 0.343     #>  #> • Failure type \"death other causes\" #> strata   time   n.risk   estimate   std.error   95% CI           #> Drug A   5.00   97       0.010      0.010       0.001, 0.050     #> Drug A   10.0   94       0.020      0.014       0.004, 0.065     #> Drug A   15.0   83       0.082      0.028       0.038, 0.147     #> Drug A   20.0   61       0.204      0.041       0.131, 0.289     #> Drug B   5.00   102      0.000      0.000       NA, NA           #> Drug B   10.0   95       0.029      0.017       0.008, 0.077     #> Drug B   15.0   75       0.098      0.030       0.050, 0.165     #> Drug B   20.0   55       0.206      0.040       0.133, 0.289     #>  #> • Tests #> outcome              statistic   df     p.value     #> death from cancer    1.99        1.00   0.16        #> death other causes   0.089       1.00   0.77         cuminc %>%   visR::visr() %>%   visR::add_CI() %>%   visR::add_risktable(statlist = c(\"n.risk\", \"cum.event\"))"},{"path":"https://openpharma.github.io/visR/reference/get_COX_HR.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","title":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","text":"S3 method extracting information regarding Hazard Ratios. function allows survival object's formula updated. default method available moment.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_COX_HR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","text":"","code":"get_COX_HR(x, ...)  # S3 method for survfit get_COX_HR(x, update_formula = NULL, ...)"},{"path":"https://openpharma.github.io/visR/reference/get_COX_HR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","text":"x object class survfit ... arguments passed method survival::coxph update_formula Template specifies update formula survfit object update.formula","code":""},{"path":"https://openpharma.github.io/visR/reference/get_COX_HR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","text":"tidied object class coxph containing Hazard Ratios","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/get_COX_HR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize Hazard Ratio from a survival object using S3 method — get_COX_HR","text":"","code":"## treatment effect survfit_object_trt <- visR::estimate_KM(data = adtte, strata = c(\"TRTP\")) visR::get_COX_HR(survfit_object_trt) #> tidyme S3 default method (broom::tidy) used. #>                       term estimate std.error statistic      p.value #> 1 TRTPXanomeline High Dose 1.614618 0.2332605  6.921953 4.454580e-12 #> 2  TRTPXanomeline Low Dose 1.422555 0.2295098  6.198232 5.710099e-10  ## treatment and gender effect survfit_object_trt_sex <- visR::estimate_KM(data = adtte, strata = c(\"TRTP\", \"SEX\")) visR::get_COX_HR(survfit_object_trt_sex) #> tidyme S3 default method (broom::tidy) used. #>                       term  estimate std.error statistic      p.value #> 1 TRTPXanomeline High Dose 1.6159986 0.2339757  6.906695 4.960732e-12 #> 2  TRTPXanomeline Low Dose 1.4755033 0.2317331  6.367252 1.924446e-10 #> 3                     SEXM 0.3745729 0.1651854  2.267591 2.335412e-02  ## update formula of KM estimates by treatment to include \"SEX\" for HR estimation visR::get_COX_HR(survfit_object_trt, update_formula = \". ~ . + SEX\") #> tidyme S3 default method (broom::tidy) used. #>                       term  estimate std.error statistic      p.value #> 1 TRTPXanomeline High Dose 1.6159986 0.2339757  6.906695 4.960732e-12 #> 2  TRTPXanomeline Low Dose 1.4755033 0.2317331  6.367252 1.924446e-10 #> 3                     SEXM 0.3745729 0.1651854  2.267591 2.335412e-02  ## update formula of KM estimates by treatment to include \"AGE\" for ## HR estimation with ties considered via the efron method visR::get_COX_HR(survfit_object_trt,   update_formula = \". ~ . + survival::strata(AGE)\", ties = \"efron\" ) #> Warning: Loglik converged before variable  3,7,18,36,37 ; coefficient may be infinite.  #> tidyme S3 default method (broom::tidy) used. #>                           term    estimate    std.error     statistic #> 1     TRTPXanomeline High Dose   1.5676387 2.555278e-01  6.1349054151 #> 2      TRTPXanomeline Low Dose   1.3216405 2.530283e-01  5.2232906436 #> 3  survival::strata(AGE)AGE=52 -18.1640265 5.298844e+03 -0.0034279227 #> 4  survival::strata(AGE)AGE=54  -0.2090228 1.415797e+00 -0.1476361992 #> 5  survival::strata(AGE)AGE=56  -1.6206873 1.097690e+00 -1.4764526121 #> 6  survival::strata(AGE)AGE=57  -1.1398148 1.166518e+00 -0.9771082854 #> 7  survival::strata(AGE)AGE=59 -18.1578569 3.767502e+03 -0.0048196009 #> 8  survival::strata(AGE)AGE=60  -1.3242727 1.241054e+00 -1.0670547450 #> 9  survival::strata(AGE)AGE=61  -1.4515246 1.133242e+00 -1.2808598926 #> 10 survival::strata(AGE)AGE=62  -1.4077265 1.428274e+00 -0.9856138035 #> 11 survival::strata(AGE)AGE=63  -1.7199741 1.245995e+00 -1.3804019425 #> 12 survival::strata(AGE)AGE=64  -1.4654615 1.256398e+00 -1.1663993068 #> 13 survival::strata(AGE)AGE=65  -1.8714042 1.236610e+00 -1.5133340637 #> 14 survival::strata(AGE)AGE=66  -0.7392248 1.443026e+00 -0.5122740113 #> 15 survival::strata(AGE)AGE=67  -1.7065320 1.132933e+00 -1.5062959259 #> 16 survival::strata(AGE)AGE=68  -1.5389556 1.104752e+00 -1.3930331338 #> 17 survival::strata(AGE)AGE=69  -1.1214615 1.136235e+00 -0.9869979981 #> 18 survival::strata(AGE)AGE=70 -18.2495027 2.607234e+03 -0.0069995634 #> 19 survival::strata(AGE)AGE=71  -1.3835479 1.085266e+00 -1.2748469333 #> 20 survival::strata(AGE)AGE=72  -1.5861528 1.109620e+00 -1.4294562988 #> 21 survival::strata(AGE)AGE=73  -1.3311583 1.078529e+00 -1.2342349648 #> 22 survival::strata(AGE)AGE=74  -1.8664225 1.086626e+00 -1.7176312590 #> 23 survival::strata(AGE)AGE=75  -1.0701874 1.094268e+00 -0.9779939016 #> 24 survival::strata(AGE)AGE=76  -1.5673346 1.094736e+00 -1.4317009758 #> 25 survival::strata(AGE)AGE=77  -1.6854602 1.064611e+00 -1.5831705057 #> 26 survival::strata(AGE)AGE=78  -1.3770729 1.070742e+00 -1.2860927604 #> 27 survival::strata(AGE)AGE=79  -1.4209295 1.057330e+00 -1.3438841443 #> 28 survival::strata(AGE)AGE=80  -1.0569092 1.073982e+00 -0.9841035589 #> 29 survival::strata(AGE)AGE=81  -2.4638433 1.083968e+00 -2.2729862787 #> 30 survival::strata(AGE)AGE=82  -1.9384962 1.095070e+00 -1.7702034739 #> 31 survival::strata(AGE)AGE=83  -0.9201888 1.089830e+00 -0.8443416072 #> 32 survival::strata(AGE)AGE=84  -1.4853330 1.059185e+00 -1.4023361544 #> 33 survival::strata(AGE)AGE=85  -1.5477944 1.140473e+00 -1.3571508894 #> 34 survival::strata(AGE)AGE=86  -2.2322079 1.172337e+00 -1.9040670529 #> 35 survival::strata(AGE)AGE=87  -2.6816908 1.423263e+00 -1.8841844010 #> 36 survival::strata(AGE)AGE=88 -18.7682740 2.704813e+03 -0.0069388434 #> 37 survival::strata(AGE)AGE=89 -18.1983764 1.889752e+04 -0.0009630034 #>         p.value #> 1  8.520988e-10 #> 2  1.757712e-07 #> 3  9.972649e-01 #> 4  8.826299e-01 #> 5  1.398224e-01 #> 6  3.285155e-01 #> 7  9.961545e-01 #> 8  2.859471e-01 #> 9  2.002429e-01 #> 10 3.243227e-01 #> 11 1.674629e-01 #> 12 2.434530e-01 #> 13 1.301948e-01 #> 14 6.084593e-01 #> 15 1.319912e-01 #> 16 1.636098e-01 #> 17 3.236436e-01 #> 18 9.944152e-01 #> 19 2.023634e-01 #> 20 1.528731e-01 #> 21 2.171154e-01 #> 22 8.586389e-02 #> 23 3.280773e-01 #> 24 1.522294e-01 #> 25 1.133826e-01 #> 26 1.984107e-01 #> 27 1.789859e-01 #> 28 3.250646e-01 #> 29 2.302701e-02 #> 30 7.669325e-02 #> 31 3.984785e-01 #> 32 1.608149e-01 #> 33 1.747333e-01 #> 34 5.690145e-02 #> 35 5.954004e-02 #> 36 9.944636e-01 #> 37 9.992316e-01"},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate cohort attrition table — get_attrition","title":"Generate cohort attrition table — get_attrition","text":"experimental function may developed time. function calculates subjects counts excluded included step cohort selection process.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate cohort attrition table — get_attrition","text":"","code":"get_attrition(data, criteria_descriptions, criteria_conditions, subject_column_name)"},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate cohort attrition table — get_attrition","text":"data Dataframe. used input data count subjects meets criteria interest criteria_descriptions character contains descriptions inclusion/exclusion criteria. element vector corresponds description criterion. criteria_conditions character contains corresponding conditions criteria. conditions used table compute counts subjects. subject_column_name character column name table contains subject id.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate cohort attrition table — get_attrition","text":"counts percentages remaining excluded subjects step cohort selection table format.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate cohort attrition table — get_attrition","text":"criteria_descriptions criteria_conditions need length","code":""},{"path":"https://openpharma.github.io/visR/reference/get_attrition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate cohort attrition table — get_attrition","text":"","code":"visR::get_attrition(adtte,   criteria_descriptions =     c(       \"1. Placebo Group\", \"2. Be 75 years of age or older.\",       \"3. White\", \"4. Site 709\"     ),   criteria_conditions = c(     \"TRTP=='Placebo'\", \"AGE>=75\",     \"RACE=='WHITE'\", \"SITEID==709\"   ),   subject_column_name = \"USUBJID\" ) #> # A tibble: 5 × 6 #>   Criteria       Condition `Remaining N` `Remaining %` `Excluded N` `Excluded %` #>   <chr>          <chr>             <int>         <dbl>        <int>        <dbl> #> 1 Total cohort … none                254        100               0         0    #> 2 1. Placebo Gr… TRTP=='P…            86         33.9           168        66.1  #> 3 2. Be 75 year… AGE>=75              48         18.9            38        15.0  #> 4 3. White       RACE=='W…            45         17.7             3         1.18 #> 5 4. Site 709    SITEID==…             3          1.18           42        16.5"},{"path":"https://openpharma.github.io/visR/reference/get_pvalue.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","title":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","text":"Wrapper around survival::survdiff tests null hypothesis equality across strata.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_pvalue.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","text":"","code":"get_pvalue(   survfit_object,   ptype = \"All\",   rho = NULL,   statlist = c(\"test\", \"Chisq\", \"df\", \"pvalue\"),   ... )"},{"path":"https://openpharma.github.io/visR/reference/get_pvalue.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","text":"survfit_object object class survfit ptype Character vector containing type p-value desired. Current options \"Log-Rank\" \"Wilcoxon\" \"Tarone-Ware\" \"Custom\" \"\". \"Custom\" allows user specify weights Kaplan-Meier estimates using argument rho. default \"\" displaying types possible. rho specified context \"\", also custom p-value displayed. rho scalar parameter controls type test. statlist Character vector containing desired information displayed. order arguments determines order displayed final result. Default test name (\"test\"), Chi-squared test statistic (\"Chisq\"), degrees freedom (\"df\") p-value (\"pvalue\"). ... arguments passed method","code":""},{"path":"https://openpharma.github.io/visR/reference/get_pvalue.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","text":"data frame summary measures Test Equality Across Strata","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/get_pvalue.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize the test for equality across strata from a survival object using S3 method — get_pvalue","text":"","code":"## general examples survfit_object <- visR::estimate_KM(data = adtte, strata = \"TRTP\") visR::get_pvalue(survfit_object) #>   Equality across strata  Chisq df p-value #> 1               Log-Rank 60.270  2  <0.001 #> 2               Wilcoxon 48.023  2  <0.001 #> 3            Tarone-Ware 41.850  2  <0.001 visR::get_pvalue(survfit_object, ptype = \"All\") #>   Equality across strata  Chisq df p-value #> 1               Log-Rank 60.270  2  <0.001 #> 2               Wilcoxon 48.023  2  <0.001 #> 3            Tarone-Ware 41.850  2  <0.001  ## examples to obtain specific tests visR::get_pvalue(survfit_object, ptype = \"Log-Rank\") #>   Equality across strata  Chisq df p-value #> 1               Log-Rank 60.270  2  <0.001 visR::get_pvalue(survfit_object, ptype = \"Wilcoxon\") #>   Equality across strata  Chisq df p-value #> 1               Wilcoxon 48.023  2  <0.001 visR::get_pvalue(survfit_object, ptype = \"Tarone-Ware\") #>   Equality across strata  Chisq df p-value #> 1            Tarone-Ware 41.850  2  <0.001  ## Custom example - obtain Harrington and Fleming test visR::get_pvalue(survfit_object, ptype = \"Custom\", rho = 1) #>                  Equality across strata  Chisq df p-value #> 1 Harrington and Fleming test (rho = 1) 48.023  2  <0.001  ## Get specific information and statistics visR::get_pvalue(survfit_object, ptype = \"Log-Rank\", statlist = c(\"test\", \"Chisq\", \"df\", \"pvalue\")) #>   Equality across strata  Chisq df p-value #> 1               Log-Rank 60.270  2  <0.001 visR::get_pvalue(survfit_object, ptype = \"Wilcoxon\", statlist = c(\"pvalue\")) #>   p-value #> 1  <0.001"},{"path":"https://openpharma.github.io/visR/reference/get_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper around quantile methods — get_quantile","title":"Wrapper around quantile methods — get_quantile","text":"S3 method extracting quantiles. default method available moment.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper around quantile methods — get_quantile","text":"","code":"get_quantile(x, ...)  # S3 method for survfit get_quantile(   x,   ...,   probs = c(0.25, 0.5, 0.75),   conf.int = TRUE,   tolerance = sqrt(.Machine$double.eps) )"},{"path":"https://openpharma.github.io/visR/reference/get_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper around quantile methods — get_quantile","text":"x object class survfit ... arguments passed method probs probabilities Default = c(0.25,0.50,0.75) conf.int lower upper confidence limits returned? tolerance tolerance checking survival curve exactly     equals one quantiles","code":""},{"path":"https://openpharma.github.io/visR/reference/get_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper around quantile methods — get_quantile","text":"data frame quantiles object","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/get_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrapper around quantile methods — get_quantile","text":"","code":"## Kaplan-Meier estimates survfit_object <- visR::estimate_KM(data = adtte, strata = c(\"TRTP\"))  ## visR quantiles visR::get_quantile(survfit_object) #>                      strata quantity  25 50  75 #> 4              TRTP=Placebo    lower  35 NA  NA #> 1              TRTP=Placebo quantile  70 NA  NA #> 7              TRTP=Placebo    upper 177 NA  NA #> 5 TRTP=Xanomeline High Dose    lower   5 25  50 #> 2 TRTP=Xanomeline High Dose quantile  14 36  58 #> 8 TRTP=Xanomeline High Dose    upper  22 47  94 #> 6  TRTP=Xanomeline Low Dose    lower  15 28  57 #> 3  TRTP=Xanomeline Low Dose quantile  19 33  80 #> 9  TRTP=Xanomeline Low Dose    upper  27 51 126  ## survival quantiles quantile(survfit_object) #> $quantile #>                           25 50 75 #> TRTP=Placebo              70 NA NA #> TRTP=Xanomeline High Dose 14 36 58 #> TRTP=Xanomeline Low Dose  19 33 80 #>  #> $lower #>                           25 50 75 #> TRTP=Placebo              35 NA NA #> TRTP=Xanomeline High Dose  5 25 50 #> TRTP=Xanomeline Low Dose  15 28 57 #>  #> $upper #>                            25 50  75 #> TRTP=Placebo              177 NA  NA #> TRTP=Xanomeline High Dose  22 47  94 #> TRTP=Xanomeline Low Dose   27 51 126 #>"},{"path":"https://openpharma.github.io/visR/reference/get_risktable.html","id":null,"dir":"Reference","previous_headings":"","what":"Obtain risk tables for tables and plots — get_risktable","title":"Obtain risk tables for tables and plots — get_risktable","text":"Create risk table object using S3 method. Currently, default method defined.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_risktable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Obtain risk tables for tables and plots — get_risktable","text":"","code":"get_risktable(x, ...)  # S3 method for survfit get_risktable(   x,   times = NULL,   statlist = \"n.risk\",   label = NULL,   group = c(\"strata\", \"statlist\"),   collapse = FALSE,   ... )  # S3 method for tidycuminc get_risktable(   x,   times = pretty(x$tidy$time, 10),   statlist = \"n.risk\",   label = NULL,   group = c(\"strata\", \"statlist\"),   collapse = FALSE,   ... )"},{"path":"https://openpharma.github.io/visR/reference/get_risktable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Obtain risk tables for tables and plots — get_risktable","text":"x object class survfit tidycuminc ... arguments passed method times Numeric vector indicating times risk set, censored subjects, events calculated. statlist Character vector indicating summary data present. Current choices \"n.risk\" \"n.event\" \"n.censor\", \"cum.event\", \"cum.censor\". Default \"n.risk\". label Character vector labels statlist. Default matches \"n.risk\" \"risk\", \"n.event\" \"Events\", \"n.censor\" \"Censored\", \"cum.event\" \"Cum. Event\", \"cum.censor\" \"Cum. Censor\". group String indicating grouping variable risk tables. Current options : \"strata\": groups risk tables per stratum. label specifies label within risk table. strata levels used titles risk tables. default \"statlist\": groups risk tables per statlist. label specifies title risk table. strata levels used labeling within risk table. Default \"strata\". collapse Boolean, indicates whether present data overall. Default FALSE.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_risktable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Obtain risk tables for tables and plots — get_risktable","text":"return list attributes form risk table .e. number patients risk per strata","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/get_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","title":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","text":"S3 method extracting descriptive statistics across strata. default method available moment.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","text":"","code":"get_summary(x, ...)  # S3 method for survfit get_summary(   x,   statlist = c(\"strata\", \"records\", \"events\", \"median\", \"LCL\", \"UCL\", \"CI\"),   ... )"},{"path":"https://openpharma.github.io/visR/reference/get_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","text":"x object class survfit ... arguments passed method statlist Character vector containing desired information displayed. order arguments determines order displayed final result. Default strata (\"strata\"), number subjects (\"records\"), number events (\"events\"), median survival time (\"median\"), Confidence Interval (\"CI\"), Lower Confidence Limit (\"UCL\") Upper Confidence Limit (\"UCL\").","code":""},{"path":"https://openpharma.github.io/visR/reference/get_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","text":"list summary statistics survfit object data frame summary measures survfit object","code":""},{"path":"https://openpharma.github.io/visR/reference/get_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize the descriptive statistics across strata from a survival object using S3 method — get_summary","text":"","code":"survfit_object <- survival::survfit(data = adtte, survival::Surv(AVAL, 1 - CNSR) ~ TRTP) get_summary(survfit_object) #>                                              strata No. of subjects #> TRTP=Placebo                           TRTP=Placebo              86 #> TRTP=Xanomeline High Dose TRTP=Xanomeline High Dose              84 #> TRTP=Xanomeline Low Dose   TRTP=Xanomeline Low Dose              84 #>                           No. of events Median(surv.time) 0.95LCL 0.95UCL #> TRTP=Placebo                         29                NA      NA      NA #> TRTP=Xanomeline High Dose            61                36      25      47 #> TRTP=Xanomeline Low Dose             62                33      28      51 #>                            0.95CI #> TRTP=Placebo              (NA;NA) #> TRTP=Xanomeline High Dose (25;47) #> TRTP=Xanomeline Low Dose  (28;51)"},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate summary statistics — get_tableone","title":"Calculate summary statistics — get_tableone","text":"S3 method creating table summary statistics. summary statistics can used presentation tables table one baseline demography tables. summary statistics estimated conditional variable type: continuous, binary, categorical, etc. default following summary stats calculated: Numeric variables: mean, min, 25th-percentile, median, 75th-percentile, maximum, standard deviation Factor variables: proportion factor level overall dataset Default: number unique values number missing values","code":""},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate summary statistics — get_tableone","text":"","code":"get_tableone(   data,   strata = NULL,   overall = TRUE,   summary_function = summarize_short )  # S3 method for default get_tableone(   data,   strata = NULL,   overall = TRUE,   summary_function = summarize_short )"},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate summary statistics — get_tableone","text":"data dataset summarize dataframe tibble strata Stratifying/Grouping variable name(s) character vector. NULL, overall results returned overall TRUE, summary statistics overall dataset also calculated summary_function function defining summary statistics numeric categorical values","code":""},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate summary statistics — get_tableone","text":"object class tableone. list data specified summaries input variables.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate summary statistics — get_tableone","text":"possible provide summary function. Please loot summary inspiration.","code":""},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate summary statistics — get_tableone","text":"columns table summarized. columns shall used, please select variables prior creating summary table using dplyr::select()","code":""},{"path":"https://openpharma.github.io/visR/reference/get_tableone.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate summary statistics — get_tableone","text":"","code":"# Example using the ovarian data set  survival::ovarian %>%   dplyr::select(-fustat) %>%   dplyr::mutate(     age_group = factor(       dplyr::case_when(         age <= 50 ~ \"<= 50 years\",         age <= 60 ~ \"<= 60 years\",         age <= 70 ~ \"<= 70 years\",         TRUE ~ \"> 70 years\"       )     ),     rx = factor(rx),     ecog.ps = factor(ecog.ps)   ) %>%   dplyr::select(age, age_group, everything()) %>%   visR::get_tableone() #> # A tibble: 21 × 3 #>    variable  statistic    Total            #>    <chr>     <chr>        <chr>            #>  1 Sample    N            26               #>  2 age       Mean (SD)    56.2 (10.1)      #>  3 age       Median (IQR) 56.8 (50.2-62.4) #>  4 age       Min-max      38.9-74.5        #>  5 age       Missing      0 (0%)           #>  6 age_group <= 50 years  6 (23.1%)        #>  7 age_group <= 60 years  13 (50.0%)       #>  8 age_group <= 70 years  4 (15.4%)        #>  9 age_group > 70 years   3 (11.5%)        #> 10 futime    Mean (SD)    600 (340)        #> # ℹ 11 more rows  # Examples using ADaM data  # display patients in an analysis set adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(TRTA) %>%   visR::get_tableone() #> # A tibble: 4 × 3 #>   variable statistic            Total      #>   <chr>    <chr>                <chr>      #> 1 Sample   N                    254        #> 2 TRTA     Placebo              86 (33.9%) #> 3 TRTA     Xanomeline High Dose 84 (33.1%) #> 4 TRTA     Xanomeline Low Dose  84 (33.1%)  ## display overall summaries for demog adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, RACE) %>%   visR::get_tableone() #> # A tibble: 13 × 3 #>    variable statistic                        Total         #>    <chr>    <chr>                            <chr>         #>  1 Sample   N                                254           #>  2 AGE      Mean (SD)                        75.1 (8.25)   #>  3 AGE      Median (IQR)                     77 (70-81)    #>  4 AGE      Min-max                          51-89         #>  5 AGE      Missing                          0 (0%)        #>  6 AGEGR1   <65                              33 (13.0%)    #>  7 AGEGR1   >80                              77 (30.3%)    #>  8 AGEGR1   65-80                            144 (56.7%)   #>  9 SEX      F                                143 (56.3%)   #> 10 SEX      M                                111 (43.7%)   #> 11 RACE     AMERICAN INDIAN OR ALASKA NATIVE 1 (0.394%)    #> 12 RACE     BLACK OR AFRICAN AMERICAN        23 (9.055%)   #> 13 RACE     WHITE                            230 (90.551%)  ## By actual treatment adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, RACE, TRTA) %>%   visR::get_tableone(strata = \"TRTA\") #> # A tibble: 13 × 6 #>    variable statistic Total Placebo `Xanomeline High Dose` `Xanomeline Low Dose` #>    <chr>    <chr>     <chr> <chr>   <chr>                  <chr>                 #>  1 Sample   N         254   86      84                     84                    #>  2 AGE      Mean (SD) 75.1… 75.2 (… 74.4 (7.89)            75.7 (8.29)           #>  3 AGE      Median (… 77 (… 76 (69… 76 (70.8-80)           77.5 (71-82)          #>  4 AGE      Min-max   51-89 52-89   56-88                  51-88                 #>  5 AGE      Missing   0 (0… 0 (0%)  0 (0%)                 0 (0%)                #>  6 AGEGR1   <65       33 (… 14 (16… 11 (13.1%)             8 (9.52%)             #>  7 AGEGR1   >80       77 (… 30 (34… 18 (21.4%)             29 (34.52%)           #>  8 AGEGR1   65-80     144 … 42 (48… 55 (65.5%)             47 (55.95%)           #>  9 SEX      F         143 … 53 (61… 40 (47.6%)             50 (59.5%)            #> 10 SEX      M         111 … 33 (38… 44 (52.4%)             34 (40.5%)            #> 11 RACE     AMERICAN… 1 (0… NA      1 (1.19%)              NA                    #> 12 RACE     BLACK OR… 23 (… 8 (9.3… 9 (10.71%)             6 (7.14%)             #> 13 RACE     WHITE     230 … 78 (90… 74 (88.10%)            78 (92.86%)            ## By actual treatment, without overall adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, EVNTDESC, TRTA) %>%   visR::get_tableone(strata = \"TRTA\", overall = FALSE) #> # A tibble: 12 × 5 #>    variable statistic       Placebo `Xanomeline High Dose` `Xanomeline Low Dose` #>    <chr>    <chr>           <chr>   <chr>                  <chr>                 #>  1 Sample   N               86      84                     84                    #>  2 AGE      Mean (SD)       75.2 (… 74.4 (7.89)            75.7 (8.29)           #>  3 AGE      Median (IQR)    76 (69… 76 (70.8-80)           77.5 (71-82)          #>  4 AGE      Min-max         52-89   56-88                  51-88                 #>  5 AGE      Missing         0 (0%)  0 (0%)                 0 (0%)                #>  6 AGEGR1   <65             14 (16… 11 (13.1%)             8 (9.52%)             #>  7 AGEGR1   >80             30 (34… 18 (21.4%)             29 (34.52%)           #>  8 AGEGR1   65-80           42 (48… 55 (65.5%)             47 (55.95%)           #>  9 SEX      F               53 (61… 40 (47.6%)             50 (59.5%)            #> 10 SEX      M               33 (38… 44 (52.4%)             34 (40.5%)            #> 11 EVNTDESC Dematologic Ev… 29 (33… 61 (72.6%)             62 (73.8%)            #> 12 EVNTDESC Study Completi… 57 (66… 23 (27.4%)             22 (26.2%)"},{"path":"https://openpharma.github.io/visR/reference/legendopts.html","id":null,"dir":"Reference","previous_headings":"","what":"Translates options for legend into a list that can be passed to ggplot2 — legendopts","title":"Translates options for legend into a list that can be passed to ggplot2 — legendopts","text":"function takes legend position orientation, defined user puts list ggplot2.","code":""},{"path":"https://openpharma.github.io/visR/reference/legendopts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translates options for legend into a list that can be passed to ggplot2 — legendopts","text":"","code":"legendopts(legend_position = \"right\", legend_orientation = NULL)"},{"path":"https://openpharma.github.io/visR/reference/legendopts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translates options for legend into a list that can be passed to ggplot2 — legendopts","text":"legend_position Default = \"right\". legend_orientation Default = NULL.","code":""},{"path":"https://openpharma.github.io/visR/reference/legendopts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translates options for legend into a list that can be passed to ggplot2 — legendopts","text":"List legend options ggplot2.","code":""},{"path":"https://openpharma.github.io/visR/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. dplyr %>% survival Surv","code":""},{"path":"https://openpharma.github.io/visR/reference/render.html","id":null,"dir":"Reference","previous_headings":"","what":"Render a data.frame, risktable, or tableone object as a table — render","title":"Render a data.frame, risktable, or tableone object as a table — render","text":"Render previously created data.frame, tibble tableone object html, rtf latex","code":""},{"path":"https://openpharma.github.io/visR/reference/render.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Render a data.frame, risktable, or tableone object as a table — render","text":"","code":"render(   data,   title = \"\",   datasource,   footnote = \"\",   output_format = \"html\",   engine = \"gt\",   download_format = c(\"copy\", \"csv\", \"excel\") )"},{"path":"https://openpharma.github.io/visR/reference/render.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Render a data.frame, risktable, or tableone object as a table — render","text":"data Input data.frame tibble visualize title Specify title text string displayed rendered table. Default title. datasource String specifying data source underlying data set. Default title. footnote String specifying additional information displayed footnote alongside data source specifications statistical tests. output_format Type output returned, can \"html\" \"latex\". Default \"html\". engine \"html\" selected output_format, one can chose using kable, gt DT engine render output table. Default \"gt\". download_format Options formats generated downloading data. Default list \"c('copy', 'csv', 'excel')\".","code":""},{"path":"https://openpharma.github.io/visR/reference/render.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Render a data.frame, risktable, or tableone object as a table — render","text":"table data structure possible interactive functionality depending choice engine.","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":null,"dir":"Reference","previous_headings":"","what":"Step ribbon statistic — stat_stepribbon","title":"Step ribbon statistic — stat_stepribbon","text":"Provides stair-step values ribbon plots, often using conjunction ggplot2::geom_step(). step ribbon can added stat_stepribbon() identically ggplot2::geom_ribbon(stat = \"stepribbon\")","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Step ribbon statistic — stat_stepribbon","text":"","code":"stat_stepribbon(   mapping = NULL,   data = NULL,   geom = \"ribbon\",   position = \"identity\",   na.rm = FALSE,   show.legend = NA,   inherit.aes = TRUE,   direction = \"hv\",   ... )  StatStepribbon"},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Step ribbon statistic — stat_stepribbon","text":"object class StatStepRibbon (inherits Stat, ggproto, gg) length 3.","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Step ribbon statistic — stat_stepribbon","text":"mapping Set aesthetic mappings created aes(). specified inherit.aes = TRUE (default), combined default mapping top level plot. must supply mapping plot mapping. data data displayed layer. three options: NULL, default, data inherited plot data specified call ggplot(). data.frame, object, override plot data. objects fortified produce data frame. See fortify() variables created. function called single argument, plot data. return value must data.frame, used layer data. function can created formula (e.g. ~ head(.x, 10)). geom geom use; defaults \"ribbon\" position Position adjustment, either string naming adjustment (e.g. \"jitter\" use position_jitter), result call position adjustment function. Use latter need change settings adjustment. na.rm FALSE, default, missing values removed warning. TRUE, missing values silently removed. show.legend logical. layer included legends? NA, default, includes aesthetics mapped. FALSE never includes, TRUE always includes. can also named logical vector finely select aesthetics display. inherit.aes FALSE, overrides default aesthetics, rather combining . useful helper functions define data aesthetics inherit behaviour default plot specification, e.g. borders(). direction hv horizontal-vertical steps, vh vertical-horizontal steps ... arguments passed layer(). often aesthetics, used set aesthetic fixed value, like colour = \"red\" size = 3. may also parameters paired geom/stat.","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Step ribbon statistic — stat_stepribbon","text":"ggplot","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Step ribbon statistic — stat_stepribbon","text":"https://groups.google.com/forum/?fromgroups=#!topic/ggplot2/9cFWHaH1CPs","code":""},{"path":"https://openpharma.github.io/visR/reference/stat_stepribbon.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Step ribbon statistic — stat_stepribbon","text":"","code":"# using ggplot2::geom_ribbon() survival::survfit(survival::Surv(time, status) ~ 1, data = survival::lung) %>%   survival::survfit0() %>%   broom::tidy() %>%   ggplot2::ggplot(ggplot2::aes(x = time, y = estimate, ymin = conf.low, ymax = conf.high)) +   ggplot2::geom_step() +   ggplot2::geom_ribbon(stat = \"stepribbon\", alpha = 0.2)   # using stat_stepribbon() with the same result survival::survfit(survival::Surv(time, status) ~ 1, data = survival::lung) %>%   survival::survfit0() %>%   broom::tidy() %>%   ggplot2::ggplot(ggplot2::aes(x = time, y = estimate, ymin = conf.low, ymax = conf.high)) +   ggplot2::geom_step() +   visR::stat_stepribbon(alpha = 0.2)"},{"path":"https://openpharma.github.io/visR/reference/summarize_long.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate summary statistics from a vector — summarize_long","title":"Calculate summary statistics from a vector — summarize_long","text":"Calculates several summary statistics. summary statistics depend vector class","code":""},{"path":"https://openpharma.github.io/visR/reference/summarize_long.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate summary statistics from a vector — summarize_long","text":"","code":"summarize_long(x)  # S3 method for factor summarize_long(x)  # S3 method for integer summarize_long(x)  # S3 method for numeric summarize_long(x)  # S3 method for default summarize_long(x)"},{"path":"https://openpharma.github.io/visR/reference/summarize_long.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate summary statistics from a vector — summarize_long","text":"x object","code":""},{"path":"https://openpharma.github.io/visR/reference/summarize_long.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate summary statistics from a vector — summarize_long","text":"summarized version input.","code":""},{"path":"https://openpharma.github.io/visR/reference/summarize_short.html","id":null,"dir":"Reference","previous_headings":"","what":"Create abbreviated variable summary for table1 — summarize_short","title":"Create abbreviated variable summary for table1 — summarize_short","text":"function creates summaries combines multiple summary measures single formatted string. Create variable summary numeric variables. Calculates mean (standard deviation), median (IQR), min-max range N/% missing elements numeric vector. Create variable summary integer variables Calculates mean (standard deviation), median (IQR), min-max range N/% missing elements integer vector.","code":""},{"path":"https://openpharma.github.io/visR/reference/summarize_short.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create abbreviated variable summary for table1 — summarize_short","text":"","code":"summarize_short(x)  # S3 method for factor summarize_short(x)  # S3 method for numeric summarize_short(x)  # S3 method for integer summarize_short(x)  # S3 method for default summarize_short(x)"},{"path":"https://openpharma.github.io/visR/reference/summarize_short.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create abbreviated variable summary for table1 — summarize_short","text":"x vector summarized","code":""},{"path":"https://openpharma.github.io/visR/reference/summarize_short.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create abbreviated variable summary for table1 — summarize_short","text":"summarized less detailed version input.","code":""},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":null,"dir":"Reference","previous_headings":"","what":"Display a summary Table (i.e. table one) — tableone","title":"Display a summary Table (i.e. table one) — tableone","text":"Wrapper function produce summary table (.e. Table One). Create render summary table dataset. typical example summary table \"table one\", first table applied medical research manuscript. Calculate summary statistics present formatted table","code":""},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display a summary Table (i.e. table one) — tableone","text":"","code":"tableone(   data,   title,   datasource,   footnote = \"\",   strata = NULL,   overall = TRUE,   summary_function = summarize_short,   ... )"},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display a summary Table (i.e. table one) — tableone","text":"data dataframe tibble visualize title Table title include rendered table. Input text string. datasource String specifying datasource underlying data set footnote Table footnote include rendered table. Input text string. strata Character vector column names use stratification summary table. Default: NULL , indicates stratification. overall TRUE, summary statistics overall dataset also calculated summary_function function defining summary statistics numeric categorical values Pre-implemented functions summarize_long summarize_short ... Pass options render_table","code":""},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display a summary Table (i.e. table one) — tableone","text":"table-like data structure, possibly interactive depending choice engine","code":""},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":"example-output","dir":"Reference","previous_headings":"","what":"Example Output","title":"Display a summary Table (i.e. table one) — tableone","text":"tableone(engine = \"gt\")  tableone(engine = \"DT\")  tableone(engine = \"kable\")  tableone(engine = \"kable\", output_format = \"latex\")","code":""},{"path":"https://openpharma.github.io/visR/reference/tableone.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display a summary Table (i.e. table one) — tableone","text":"","code":"# metadata for table t1_title <- \"Cohort Summary\" t1_ds <- \"ADaM Interim Dataset for Time-to-Event Analysis\" t1_fn <- \"My table one footnote\"   ## table by treatment - without overall and render with GT tbl_gt <-   adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, EVNTDESC, TRTA) %>%   visR::tableone(     strata = \"TRTA\",     overall = FALSE,     title = t1_title,     datasource = t1_ds,     footnote = t1_fn,     engine = \"gt\"   )  ## table by treatment - without overall and render with DT tbl_DT <-   adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, EVNTDESC, TRTA) %>%   visR::tableone(     strata = \"TRTA\",     overall = FALSE,     title = t1_title,     datasource = t1_ds,     footnote = t1_fn,     engine = \"DT\"   )  ## table by treatment - without overall and render with kable tbl_kable_html <-   adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, EVNTDESC, TRTA) %>%   visR::tableone(     strata = \"TRTA\",     overall = FALSE,     title = t1_title,     datasource = t1_ds,     footnote = t1_fn,     engine = \"kable\"   )  ## table by treatment - without overall and render with kable as ## a latex table format rather than html tbl_kable_latex <-   adtte %>%   dplyr::filter(SAFFL == \"Y\") %>%   dplyr::select(AGE, AGEGR1, SEX, EVNTDESC, TRTA) %>%   visR::tableone(     strata = \"TRTA\",     overall = FALSE,     title = t1_title,     datasource = t1_ds,     footnote = t1_fn,     output_format = \"latex\",     engine = \"kable\"   )"},{"path":"https://openpharma.github.io/visR/reference/the_lhs.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the ","title":"Find the ","text":"function finds left-hand sided symbol magrittr pipe returns character.","code":""},{"path":"https://openpharma.github.io/visR/reference/the_lhs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the ","text":"","code":"the_lhs()"},{"path":"https://openpharma.github.io/visR/reference/the_lhs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the ","text":"Left-hand sided symbol string magrittr pipe.","code":""},{"path":"https://openpharma.github.io/visR/reference/the_lhs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Find the ","text":"https://github.com/tidyverse/magrittr/issues/115#issuecomment-173894787","code":""},{"path":"https://openpharma.github.io/visR/reference/the_lhs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find the ","text":"","code":"blah <- function(x) the_lhs() adtte %>%   blah() #> [1] \"adtte\""},{"path":"https://openpharma.github.io/visR/reference/tidyme.html","id":null,"dir":"Reference","previous_headings":"","what":"Extended tidy cleaning of selected objects using S3 method — tidyme","title":"Extended tidy cleaning of selected objects using S3 method — tidyme","text":"S3 method extended tidying selected model outputs. Note visR method retains original nomenclature objects, adds one broom::tidy ensure compatibility tidy workflows. default method relies broom::tidy return tidied object","code":""},{"path":"https://openpharma.github.io/visR/reference/tidyme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extended tidy cleaning of selected objects using S3 method — tidyme","text":"","code":"tidyme(x, ...)  # S3 method for default tidyme(x, ...)  # S3 method for survfit tidyme(x, ...)"},{"path":"https://openpharma.github.io/visR/reference/tidyme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extended tidy cleaning of selected objects using S3 method — tidyme","text":"x S3 object ... arguments passed method","code":""},{"path":"https://openpharma.github.io/visR/reference/tidyme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extended tidy cleaning of selected objects using S3 method — tidyme","text":"Data frame containing list elements S3 object columns. column 'strata' factor ensure strata sorted agreement order survfit object","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/tidyme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extended tidy cleaning of selected objects using S3 method — tidyme","text":"","code":"## Extended tidying for a survfit object surv_object <- visR::estimate_KM(data = adtte, strata = \"TRTA\") tidied <- visR::tidyme(surv_object)  ## Tidyme for non-included classes data <- cars lm_object <- stats::lm(data = cars, speed ~ dist) lm_tidied <- visR::tidyme(lm_object) #> tidyme S3 default method (broom::tidy) used. lm_tidied #>          term  estimate  std.error statistic      p.value #> 1 (Intercept) 8.2839056 0.87438449  9.473985 1.440974e-12 #> 2        dist 0.1655676 0.01749448  9.463990 1.489836e-12"},{"path":"https://openpharma.github.io/visR/reference/visR-Global.html","id":null,"dir":"Reference","previous_headings":"","what":"visR package — visR Global","title":"visR package — visR Global","text":"Set global variables","code":""},{"path":"https://openpharma.github.io/visR/reference/visR-package.html","id":null,"dir":"Reference","previous_headings":"","what":"visR: Clinical Graphs and Tables Adhering to Graphical Principles — visR-package","title":"visR: Clinical Graphs and Tables Adhering to Graphical Principles — visR-package","text":"enable fit--purpose, reusable clinical medical research focused visualizations tables sensible defaults based graphical principles described : \"Vandemeulebroecke et al. (2018)\" doi:10.1002/pst.1912 , \"Vandemeulebroecke et al. (2019)\" doi:10.1002/psp4.12455 , \"Morris et al. (2019)\" doi:10.1136/bmjopen-2019-030215 .","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/visR-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"visR: Clinical Graphs and Tables Adhering to Graphical Principles — visR-package","text":"Maintainer: Mark Baillie bailliem@gmail.com [copyright holder] Authors: Diego Saldana diego.saldana@roche.com Charlotta Fruechtenicht charlotta.fruechtenicht@roche.com Marc Vandemeulebroecke marc.vandemeulebroecke@novartis.com Thanos Siadimas thanos.siadimas@roche.com Pawel Kawski pawel.kawski@contractors.roche.com Steven Haesendonckx shaesen2@.jnj.com James Black james.black.jb2@roche.com Pelagia Alexandra Papadopoulou PPapadop@.jnj.com Tim Treis tim.treis@outlook.de Rebecca Albrecht rebecca.albrecht.dietsch@gmail.com Daniel D. Sjoberg danield.sjoberg@gmail.com (ORCID) contributors: Ardalan Mirshani ardeeshany@gmail.com [contributor]","code":""},{"path":"https://openpharma.github.io/visR/reference/visr.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a supported S3 object — visr","title":"Plot a supported S3 object — visr","text":"S3 method creating plots directly objects using ggplot2, similar base R plot() function. Methods visr.survfit() visr.tidycuminc() deprecated favor ggsurvfit::ggsurvfit() ggsurvfit::ggcuminc(), respectively. visr.attrition() function draw Consort flow diagram chart currently questioned.","code":""},{"path":"https://openpharma.github.io/visR/reference/visr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a supported S3 object — visr","text":"","code":"visr(x, ...)  # S3 method for default visr(x, ...)  # S3 method for survfit visr(   x = NULL,   x_label = NULL,   y_label = NULL,   x_units = NULL,   x_ticks = NULL,   y_ticks = NULL,   fun = \"surv\",   legend_position = \"right\",   ... )  # S3 method for attrition visr(   x,   description_column_name = \"Criteria\",   value_column_name = \"Remaining N\",   complement_column_name = \"\",   box_width = 50,   font_size = 12,   fill = \"white\",   border = \"black\",   ... )  # S3 method for tidycuminc visr(   x = NULL,   x_label = \"Time\",   y_label = \"Cumulative Incidence\",   x_units = NULL,   x_ticks = pretty(x$tidy$time, 10),   y_ticks = pretty(c(0, 1), 5),   legend_position = \"right\",   ... )"},{"path":"https://openpharma.github.io/visR/reference/visr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a supported S3 object — visr","text":"x Object class attritiontable ... arguments passed method x_label character Label x-axis. specified, function look \"PARAM\" \"PARAMCD\" information original data set (CDISC standards). \"PARAM\"/\"PARAMCD\" information available, default x-axis label \"Time\". y_label character Label y-axis. specified, default proposal, depending fun argument. x_units Unit added x_label (x_label (x_unit)). Default NULL. x_ticks Ticks x-axis. specified, default proposal. y_ticks Ticks y-axis. specified, default proposal based fun argument. fun Function represents scale estimate. current options : legend_position Specifies legend position plot. Character values allowed \"top\" \"left\" \"bottom\" \"right\". Numeric coordinates also allowed. Default \"right\". description_column_name character Name column containing inclusion descriptions value_column_name character Name column containing remaining sample counts complement_column_name character Optional: Name column containing exclusion descriptions box_width character box width box flow chart font_size character fontsize pt fill color (string hexcode) use fill boxes flowchart border color (string hexcode) use borders boxes flowchart","code":""},{"path":"https://openpharma.github.io/visR/reference/visr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a supported S3 object — visr","text":"Object class ggplot ggsurvplot survfit objects.","code":""},{"path":[]},{"path":"https://openpharma.github.io/visR/reference/visr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a supported S3 object — visr","text":"","code":"# fit KM km_fit <- survival::survfit(survival::Surv(AVAL, 1 - CNSR) ~ TRTP, data = adtte)  # plot curves using survival plot function plot(km_fit)   # plot same curves using visR::visr plotting function visR::visr(km_fit)   # estimate KM using visR wrapper survfit_object <- visR::estimate_KM(data = adtte, strata = \"TRTP\")  # Plot survival probability visR::visr(survfit_object, fun = \"surv\")   # Plot survival percentage visR::visr(survfit_object, fun = \"pct\")   # Plot cumulative hazard visR::visr(survfit_object, fun = \"cloglog\") #> Warning: NAs introduced by y-axis transformation.    ## Create attrition attrition <- visR::get_attrition(adtte,   criteria_descriptions = c(     \"1. Not in Placebo Group\",     \"2. Be 75 years of age or older.\",     \"3. White\",     \"4. Female\"   ),   criteria_conditions = c(     \"TRTP != 'Placebo'\",     \"AGE >= 75\",     \"RACE=='WHITE'\",     \"SEX=='F'\"   ),   subject_column_name = \"USUBJID\" )  ## Draw a CONSORT attrition chart without specifying extra text for the complement attrition %>%   visr(\"Criteria\", \"Remaining N\")   ## Add detailed complement descriptions to the \"exclusion\" part of the CONSORT diagram # Step 1. Add new column to attrition dataframe attrition$Complement <- c(   \"NA\",   \"Placebo Group\",   \"Younger than 75 years\",   \"Non-White\",   \"Male\" )  # Step 2. Define the name of the column in the call to the plotting function attrition %>%   visr(\"Criteria\", \"Remaining N\", \"Complement\")   ## Styling the CONSORT flowchart # Change the fill and outline of the boxes in the flowchart attrition %>%   visr(\"Criteria\", \"Remaining N\", \"Complement\", fill = \"lightblue\", border = \"grey\")   ## Adjust the font size in the boxes attrition %>%   visr(\"Criteria\", \"Remaining N\", font_size = 10)"},{"path":"https://openpharma.github.io/visR/news/index.html","id":"visr-040","dir":"Changelog","previous_headings":"","what":"visR 0.4.0","title":"visR 0.4.0","text":"CRAN release: 2023-11-20 Functions estimate_KM() visr.survfit() deprecated favor ggsurvfit::ggsurvfit().","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"visr-031","dir":"Changelog","previous_headings":"","what":"visR 0.3.1","title":"visR 0.3.1","text":"CRAN release: 2022-08-17 now zoom figure (e.g. Kaplan-Meier figure) ggplot2::coord_cartesian() instead using scale_x_continuous(limits=) scale_y_continuous(limits=). latter first removes data outside limits, constructs line. Zooming constructs full line, zooms limits. useful risktable often reports estimates near end KM figure, line cutoff shown last time point. (#402) README update contributor listing. (#435)","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"visr-030","dir":"Changelog","previous_headings":"","what":"visR 0.3.0","title":"visR 0.3.0","text":"CRAN release: 2022-06-24","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"new-functions-0-3-0","dir":"Changelog","previous_headings":"","what":"New functions","title":"visR 0.3.0","text":"Highlight specific strata survival plots using add_highlight(). Indicate quantiles survival plots using add_quantiles(). Estimation cumulative incidence presence competing risks now possible estimate_cuminc(). risk estimates can plotted similarly estimates estimate_KM() using visr() function. Function Surv_CNSR() used CDISC ADTTE conventions default values time status indicators time--event analyses. note, status variable must coded 0/1 1 indicating observation censored. (#391)","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"breaking-changes-0-3-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"visR 0.3.0","text":"AlignPlots() renamed align_plots().","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"visR 0.3.0","text":"Fix get_pvalue() estimate_KM() objects data piped modifications (e.g. dplyr::filter(), dplyr::mutate()). Fix add_risktable() estimates assigned incorrect label. Review unit testing estimate_KM(formula=) argument. (#399) Fixed bug define_theme(strata=). (#388) Fix discrepancy issue saving survfit() call estimate_KM(). (#365) Added check class(x)==. (#358) Fix documentation S3 method visr(). (#301)","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"other-updates-0-3-0","dir":"Changelog","previous_headings":"","what":"Other Updates","title":"visR 0.3.0","text":"Stratifying variable names removed legend visr.survfit() figures, legend title now describes stratifying variable(s). (#343) estimate_KM() function gains experimental formula= argument. argument used, AVAL, CNSR, strata arguments typically used construct formula ignored. (#379) add_risktable rowgutter argument allow spacing plot risktables strata variable now removed body tableone() results. (#254) visr.survfit() function longer warns x-axis label PARAM column found original data set. (#378) call saved estimate_KM() object updated quosure–ensuring original function call can always recalled. define_theme()updated ensure strata present theme displayed. (#388) Removed external dependency easyalluvial package. (#383) Reduced number exported functions. (#381) Improved documentation visr() generic functions. (#301) (#357) Unit testing updated relevant 100% code coverage function requirements tested. README page updated additional examples. (#425) lifecycle badges added function level indicate functions still experimental questioning phases. (#398)","code":""},{"path":"https://openpharma.github.io/visR/news/index.html","id":"visr-020","dir":"Changelog","previous_headings":"","what":"visR 0.2.0","title":"visR 0.2.0","text":"CRAN release: 2021-06-14 Initial CRAN release.","code":""}]