Merge pull request #30 from katilingban/v0.1.3

V0.1.3
katilingban · Nov 11, 2020 · fb47e78 · fb47e78
2 parents 4a6fbb8 + b83d3e5
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diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,10 @@
+# sudanCMAM v0.1.4
+
+In this version:
+
+* added visualisation of MUAC at admission, average weight gain and
+length-of-stay
+
 # sudanCMAM v0.1.3
 
 In this version:

diff --git a/README.md b/README.md
@@ -2,7 +2,7 @@
 
 <!--- start badges -->
 [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
-![version](https://img.shields.io/badge/version-0.1.3-blue.svg)
+![version](https://img.shields.io/badge/version-0.1.4-blue.svg)
 ![license](https://img.shields.io/badge/license-GPL3-blue.svg)
 [![DOI](https://zenodo.org/badge/290984010.svg)](https://zenodo.org/badge/latestdoi/290984010)
 <!--- end badges -->

diff --git a/sudanCMAMdashboard.Rmd b/sudanCMAMdashboard.Rmd
@@ -2484,10 +2484,238 @@ output$locality_defaulters <- renderPlotly({
 plotlyOutput(outputId = "locality_defaulters")
 ```
 
-<!---
+
 Case-finding
 ================================================================================
---->
+
+```{r muac1}
+## Tidy the list
+x1 <- muac_admission[[1]] %>%
+  pivot_longer(cols = `Tahdi Osisi`:Baryay, 
+               names_to = "health_facilities", 
+               values_to = "n")
+
+x2 <- muac_admission[[2]] %>%
+  pivot_longer(cols = `Alhara alula`:Omrahaw, 
+               names_to = "health_facilities", 
+               values_to = "n")
+
+x3 <- muac_admission[[3]] %>%
+  pivot_longer(cols = `Mukram`:`Al Kuaty hospital`, 
+               names_to = "health_facilities", 
+               values_to = "n")
+
+x4 <- muac_admission[[4]] %>%
+  pivot_longer(cols = `8 - Arab`:`Reara`, 
+               names_to = "health_facilities", 
+               values_to = "n")
+
+## Concatenate
+xx <- rbind(x1, x2, x3, x4)
+```
+
+Row <!--- {.tabset .tabset-fade} --->
+--------------------------------------------------------------------------------
+
+### By state
+
+```{r muac2, fig.height = 6}
+muac_state <- aggregate(n ~ state + muac, data = xx, FUN = sum)
+
+median_muac <- muac_state %>%
+  split(f = muac_state$state) %>%
+  lapply(FUN = function(x) median(rep(x$muac, x$n), na.rm = TRUE)) %>%
+  unlist()
+
+median_muac <- names(median_muac) %>%
+  data.frame(median_muac)
+
+names(median_muac) <- c("state", "median muac")
+row.names(median_muac) <- 1:nrow(median_muac)
+  
+output$state_muac <- renderPlotly({
+  z <- muac_state %>%
+    ggplot(mapping = aes(x = muac, y = n)) +
+    geom_col(fill = unicef_blue, alpha = 0.7) +
+    geom_vline(data = median_muac,
+               mapping = aes(xintercept = `median muac`), 
+               colour = unicef_darkblue, 
+               linetype = 2, size = 0.5) +
+    scale_x_reverse(breaks = seq(from = 115, to = 60, by = -5)) +
+    labs(title = "MUAC at Admission by State", x = "MUAC (mm)") +
+    facet_wrap(. ~ state, ncol = 3) +
+    unicef_theme
+  
+  ggplotly(p = z)
+})
+
+plotlyOutput(outputId = "state_muac")
+```
+
+Row
+--------------------------------------------------------------------------------
+
+### By locality
+
+```{r muac3, fig.height = 8}
+muac_locality <- aggregate(n ~ state + locality + muac, data = xx, FUN = sum)
+
+median_muac_locality <- muac_locality %>%
+  split(f = list(muac_locality$state, muac_locality$locality)) %>%
+  lapply(FUN = function(x) median(rep(x$muac, x$n), na.rm = TRUE)) %>%
+  unlist()
+
+
+median_muac_locality <- stringr::str_split(string = names(median_muac_locality), 
+                                           pattern = "\\.", 
+                                           simplify = TRUE) %>%
+  data.frame(median_muac_locality) %>%
+  rename(state = X1, locality = X2, `median muac` = median_muac_locality)
+
+row.names(median_muac_locality) <- 1:nrow(median_muac_locality)
+
+output$locality_muac <- renderPlotly({
+  z <- muac_locality %>%
+    ggplot(mapping = aes(x = muac, y = n)) +
+    geom_col(fill = unicef_blue, alpha = 0.7) +
+    geom_vline(data = median_muac_locality,
+               mapping = aes(xintercept = `median muac`), 
+               colour = unicef_darkblue, 
+               linetype = 2, size = 0.5) +
+    scale_x_reverse(breaks = seq(from = 115, to = 60, by = -5)) +
+    labs(title = "MUAC at Admission by Locality", x = "MUAC (mm)") +
+    facet_grid(locality ~ state) +
+    unicef_theme
+  
+  ggplotly(p = z)
+})
+
+plotlyOutput(outputId = "locality_muac")
+```
+
+Effectiveness
+================================================================================
+
+Row {.tabset .tabset-fade}
+--------------------------------------------------------------------------------
+
+```{r los1}
+length_of_stay <- otp_beneficiaries %>%
+  calculate_los_df(admission_date = "admDate", discharge_date = "disDate") %>%
+  mutate(state = "Kassala") %>%
+  dplyr::relocate(state, .before = locality)
+
+median_los_state <- length_of_stay %>%
+  split(f = length_of_stay$state) %>%
+  lapply(FUN = function(x) median(x$los, na.rm = TRUE)) %>%
+  unlist()
+
+median_los_state <- names(median_los_state) %>%
+  data.frame(median_los_state)
+
+names(median_los_state) <- c("state", "median length-of-stay")
+row.names(median_los_state) <- 1:nrow(median_los_state)
+
+median_los_locality <- length_of_stay %>%
+  split(f = list(length_of_stay$state, length_of_stay$locality)) %>%
+  lapply(FUN = function(x) median(x$los, na.rm = TRUE)) %>%
+  unlist()
+
+median_los_locality <- stringr::str_split(string = names(median_los_locality), 
+                                          pattern = "\\.", 
+                                          simplify = TRUE) %>%
+  data.frame(median_los_locality) %>%
+  rename(state = X1, locality = X2, `median length-of-stay` = median_los_locality)
+
+row.names(median_los_locality) <- 1:nrow(median_los_locality)
+```
+
+### By state
+
+```{r los2, fig.height = 8}
+output$los_state <- renderPlotly({
+  los_state_plot <- length_of_stay %>%
+    ggplot(mapping = aes(x = state, y = los)) +
+    geom_boxplot(colour = unicef_blue, width = 0.5) +
+    labs(title = "Median length-of-stay by state",
+         x = "",
+         y = "Length-of-stay (days)") +
+    unicef_theme
+  
+  ggplotly(p = los_state_plot)
+})
+
+plotlyOutput(outputId = "los_state")
+```
+
+### By locality
+
+```{r los3, fig.height = 8}
+output$los_locality <- renderPlotly({
+  los_locality_plot <- length_of_stay %>%
+    ggplot(mapping = aes(x = locality, y = los)) +
+    geom_boxplot(colour = unicef_blue, width = 0.5) +
+    labs(title = "Median length-of-stay by locality",
+         x = "",
+         y = "Length-of-stay (days)") +
+    facet_wrap(state ~ .) +
+    unicef_theme
+  
+  ggplotly(p = los_locality_plot)
+})
+
+plotlyOutput(outputId = "los_locality")
+```
+
+Row {.tabset .tabset-fade}
+--------------------------------------------------------------------------------
+
+```{r wtGain1}
+wt_gain <- otp_beneficiaries %>%
+  mutate(state = "Kassala") %>%
+  dplyr::relocate(state, .before = locality) %>%
+  calculate_los_df(admission_date = "admDate", discharge_date = "disDate") %>%
+  mutate(wtGain = ((diswt - wt) * 1000) / (wt * los)) %>%
+  filter(wtGain >= 0)
+```
+
+### By state
+
+```{r wtGain2, fig.height = 8}
+output$wt_gain_state <- renderPlotly({
+  z <- wt_gain %>%
+    ggplot(mapping = aes(x = state, y = wtGain)) +
+    geom_boxplot(colour = unicef_blue, width = 0.5) +
+    labs(title = "Average weight gain by state",
+         x = "",
+         y = "Average weight gain (g/kg/day)") +
+    unicef_theme
+  
+  ggplotly(p = z)
+})
+
+plotlyOutput(outputId = "wt_gain_state")
+```
+
+### By locality
+
+```{r wtGain3, fig.height = 8}
+output$wt_gain_locality <- renderPlotly({
+  z <- wt_gain %>%
+    ggplot(mapping = aes(x = locality, y = wtGain)) +
+    geom_boxplot(colour = unicef_blue, width = 0.5) +
+    labs(title = "Average weight gain by locality",
+         x = "",
+         y = "Average weight gain (g/kg/day)") +
+    facet_wrap(state ~ .) +
+    unicef_theme
+  
+  ggplotly(p = z)
+})
+
+plotlyOutput(outputId = "wt_gain_locality")
+```
+
 
 About
 ================================================================================
@@ -2500,7 +2728,7 @@ This dashboard has been developed in support of the evaluation of the **Sudan Co
 
 **How to use the dashboard**
 
-This dashboard has two main sections - *Performance* and *Responsiveness* - which can be accessed via the header tabs. The *Performance* section (shown on dashboard startup) presents analysis and visualisation of CMAM performance indicators at national, state and locality level across various time periods. The *Responsiveness* section presents analysis and visualisation of CMAM responsiveness indicators at national, state and locality level across various time periods. The dashboard has a fixed sidebar that contains various user interfaces for selecting appropriate settings for respective sections. The various settings groups include an info button which when clicked shows a modal dialog box providing information and guidance on how to choose appropriate settings.
+This dashboard has four main sections - *Performance*, *Responsiveness*, *Case-finding*, and *Effectiveness* - which can be accessed via the header tabs. The *Performance* section (shown on dashboard startup) presents analysis and visualisation of CMAM performance indicators at national, state and locality level across various time periods. The *Responsiveness* section presents analysis and visualisation of CMAM responsiveness indicators at national, state and locality level across various time periods. The *Case-finding* section shows MUAC-at-admission which is an indicator of how good the case-finding mechanism of the programme is - the closer the MUAC-at-admissions are to the admitting MUAC threshold (i.e. 115 mm) the earlier that they are found in the community and the earlier the cases are found the better their treatment outcomes are. The *Effectiveness* section presents indicators of length-of-stay in the programme and average weight gain. These indicators assess clinical effectiveness. The dashboard has a fixed sidebar that contains various user interfaces for selecting appropriate settings for respective sections. The various settings groups include an info button which when clicked shows a modal dialog box providing information and guidance on how to choose appropriate settings.
 
 <br>