Merge pull request #35 from RhoInc/v1.0_fixes

V1.0 fixes

.Rbuildignore

@@ -1,3 +1,5 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^README\.md$
+^\.travis\.yml$
+^cran-comments\.md$

.travis.yml

@@ -0,0 +1,10 @@
+# R for travis: see documentation at https://docs.travis-ci.com/user/languages/r
+
+language: R
+sudo: false
+cache: packages
+
+os:
+  - linux
+  - osx
+

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: datadigest
 Type: Package
-Title: Create an Interactive Codebook from R
-Version: 1.0
+Title: Create an Interactive Data Summary from R
+Version: 1.0.1
 Authors@R: c(
 	person("Becca", "Krouse", email = "rebecca_krouse@rhoworld.com", role = c("cre","aut")),
 	person("Spencer", "Childress", role = c("aut")),
@@ -11,9 +11,9 @@ Description: An R interface for Rho's web-codebook JavaScript charting library,
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 6.0.1
+RoxygenNote: 6.1.0
 Depends:
-    R (>= 3.3.3)
+    R (>= 3.3.0)
 Imports: htmlwidgets,
     shiny,
     miniUI,

NEWS.md

@@ -0,0 +1,5 @@
+# datadigest 1.0.1
+
+Initial release for datadigest, an R interface for Rho's [web-codebook](https://github.com/RhoInc/web-codebook) JavaScript library. The r version of the codebook has been built using the [htmlwidgets](htmlwidgets.org) framework and consists of two key functions: ```codebook()``` and ```explorer()```.  ```codebook()``` delivers an interactive interface for a single data frame while ```explorer()``` extends this functionality, allowing the user to navigate through multiple data frames. 
+
+While ```codebook()``` and ```explorer()``` can be loaded as HTML widgets using their respective R functions, the package also includes a dedicated Shiny application for each. The shiny applications offer easy access to data currently loaded in the R session, the ability to upload data files, and/or the ability to download a static or interactive data summary. Following package installation, RStudio users may access the two applications via the toolbar as RStudio add-ins.

R/codebook.R

@@ -17,7 +17,7 @@
 #' }
 #'
 #' @examples
-#' codebook(data=mtcars)
+#' codebook(data = mtcars)
 #'
 #' @import htmlwidgets
 #' @importFrom tibble is_tibble

R/explorer.R

@@ -31,7 +31,7 @@
 #' @import datasets
 #'
 #' @export
-explorer <- function(data = NULL, addEnv=TRUE, demo=FALSE) {
+explorer <- function(data = NULL, addEnv = TRUE, demo = FALSE) {
 
   # get names of files in dataList as text string
   if (is.list(data)){
@@ -72,9 +72,7 @@ explorer <- function(data = NULL, addEnv=TRUE, demo=FALSE) {
         df <- fileList[[i]]
         df <- as.data.frame(df)
         for (j in 1:ncol(df)){
-       #   if (is.factor(df[,j])){
             df[,j] <- as.character(df[,j])
-      #    }  
         }
         df[is.na(df)] <- ""
         fileList_formatted[[i]] <- list(
@@ -89,9 +87,7 @@ explorer <- function(data = NULL, addEnv=TRUE, demo=FALSE) {
         df <- get(fileList[i])
         df <- as.data.frame(df)
         for (j in 1:ncol(df)){
-      #    if (is.factor(df[,j])){
             df[,j] <- as.character(df[,j])
-       #   }
         }
         df[is.na(df)] <- ""
         fileList_formatted[[i]] <- list(
@@ -112,7 +108,15 @@ explorer <- function(data = NULL, addEnv=TRUE, demo=FALSE) {
       rSettings[["settings"]][["files"]] = c(rSettings[["settings"]][["files"]], data_list_formatted)
     }
 
-  # (3) Prep an array of objects for the environment files (if addEnv=T)
+  # (3) load 20 datasets and put them in the environment (if demo=T)
+    if(demo){
+      addEnv <- FALSE # force addEnv to be false
+      demo_list = ls("package:datasets")[sapply(ls("package:datasets"), function(x) inherits(get(x), "data.frame"))]
+      demo_list_formatted <- formatFileList(demo_list)
+      rSettings[["settings"]][["files"]] = demo_list_formatted #ignores other settings
+    }
+
+  # (4) Prep an array of objects for the environment files (if addEnv=T)
   if(addEnv){
     if(length(ls(pos=1))>0){
       env_list <- ls(pos=1)[sapply(ls(pos=1), function(x) inherits(get(x), "data.frame"))]
@@ -133,14 +137,8 @@ explorer <- function(data = NULL, addEnv=TRUE, demo=FALSE) {
 
   }
 
-  # (4) load 20 datasets and put them in the environment (if demo=T)
-  if(demo){
-    demo_list = ls("package:datasets")[sapply(ls("package:datasets"), function(x) inherits(get(x), "data.frame"))]
-    demo_list_formatted <- formatFileList(demo_list)
-    rSettings[["settings"]][["files"]] = demo_list_formatted #ignores other settings
-  }
-
-  # (4) create widget
+
+  # (5) create widget
   htmlwidgets::createWidget(
     name = 'explorer',
     rSettings,

README.md

@@ -1,3 +1,5 @@
+[![Travis-CI Build Status](https://travis-ci.org/RhoInc/datadigest.svg?branch=master)](https://travis-ci.org/RhoInc/datadigest)
+
 # Data Digest
 
 The **datadigest** package provides a simple interactive framework for exploring data. You can:

cran-comments.md

@@ -0,0 +1,17 @@
+# Version 1.0.1
+This is the first submission of datadigest.
+
+## Test environments
+* ubuntu 14.04, R 3.5.0 (on travis-ci)
+* Mac osx 10.13.3, R 3.5.0  (on travis-ci)
+* win-builder (devel)
+
+## R CMD check results
+There were no ERRORs or WARNINGs.
+
+There was 1 NOTE:
+
+* checking CRAN incoming feasibility ... NOTE
+Maintainer: 'Becca Krouse <rebecca_krouse@rhoworld.com>'
+
+New submission

vignettes/datadigest.Rmd

@@ -23,12 +23,12 @@ The **datadigest** package provides a simple interactive framework for exploring
 
 ## Background
 
-The codebook tool was developed to aid in the exploration of clinical trial data.  Any given trial requires vigorous monitoring and exploration of the incoming data.  These technical tasks are imperative for ensuring data quality and proper interpretation of study results. The interactive codebook was designed to streamline data exploration for any data set.
+The interactive codebook tool was developed to aid in the exploration of clinical trial data.  Any given trial requires vigorous monitoring and exploration of the incoming data.  These technical tasks are imperative for ensuring data quality and proper interpretation of study results. The interactive codebook was designed to streamline data exploration for any data set.
 
 The interactive codebook builds upon the existing use of statistical graphics and data visualization in clinical trials by creating a simple interactive framework for exploring data.  In particular, codebook takes inspiration from Frank Harrell's excellent `describe` method from the ```Hmisc``` R package to create concise summaries of every variable in a dataset with minimal user configuration.  Like its static codebook predecessors, the web-codebook includes paneled displays, comprehensive data listings, and charts for each variable type, but it expands on these tools by providing interactivity via dynamic filters, collapsible/expandable sections, across-chart data linking, and customizable controls. The resulting tool is well suited for use in many aspects of clinical trial research, including data exploration, anomaly detection, key end point and safety monitoring surveillance.
 
 ## R package
-The **datadigest** package consists of two key functions: ```codebook()``` and ```explorer()```.  ```codebook()``` delivers an interactive interface for a single data frame. ```explorer()``` extends this functionality, allowing the user to navigate through multiple data frames. While ```codebook()``` and ```explorer()``` can be loaded as HTML widgets using their respective R functions, the package also includes a dedicated Shiny application for each. The shiny applications offer easy access to data currently loaded in the R session, the ability to upload data files, and/or the ability to download a static or interactive data summary. Following package installation, RStudio users may access the two applications via the toolbar as RStudio add-ins.
+The **datadigest** package provides an interface for the interactive codebook tool.  **datadigest** consists of two key functions: ```codebook()``` and ```explorer()```.  ```codebook()``` delivers an interactive interface for a single data frame. ```explorer()``` extends this functionality, allowing the user to navigate through multiple data frames. While ```codebook()``` and ```explorer()``` can be loaded as HTML widgets using their respective R functions, the package also includes a dedicated Shiny application for each. The shiny applications offer easy access to data currently loaded in the R session, the ability to upload data files, and/or the ability to download a static or interactive data summary. Following package installation, RStudio users may access the two applications via the toolbar as RStudio add-ins.
 
 ## Codebook/explorer data summaries
 Together, the codebook and explorer tools have 5 views that share interactive functionality: