Merge pull request #160 from Biogen-Inc/submit_cran020

Next Release: `v0.2.0`

.Rbuildignore

@@ -27,3 +27,4 @@ vignettes/x0*
 ^doc$
 ^Meta$
 ^CRAN-SUBMISSION$
+^scripts$

.github/workflows/pkgdown.yaml

@@ -4,7 +4,7 @@ on:
   push:
     branches: [main, master]
   pull_request:
-    branches: [main, master, for_cranny]
+    branches: [main, master, devel]
   release:
     types: [published]
   workflow_dispatch:

CRAN-SUBMISSION

@@ -0,0 +1,3 @@
+Version: 0.2.0
+Date: 2023-02-03 16:27:30 UTC
+SHA: d30f87b5819d7624f7ef1a8527430cbe747255ce

DESCRIPTION

@@ -1,37 +1,37 @@
 Package: tidyCDISC
 Title: Quick Table Generation & Exploratory Analyses on ADaM-Ish Datasets
-Version: 0.1.1
+Version: 0.2.0
 Authors@R: c(
-    person("Aaron", "Clark", , "[email protected]",
-      role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0123-0970")),
+    person("Aaron", "Clark", , "[email protected]", role = c("aut", "cre"),
+           comment = c(ORCID = "0000-0002-0123-0970")),
+    person("Jeff", "Thompson", , "[email protected]", role = "aut"),
+    person("Teresa", "Wilson", , "[email protected]", role = "aut"),
     person("Nate", "Mockler", , "[email protected]", role = c("ccp", "led")),
     person("Maya", "Gans", , "[email protected]", role = "aut"),
     person("Robert", "Krajcik", , "[email protected]", role = "ctb"),
     person("Marly", "Gotti", , "[email protected]", role = "ctb"),
-    person(given = "Jeff", family = "Thompson", 
-        email = "[email protected]", role = "ctb"),
     person("Biogen", "Inc", role = "cph")
   )
 Description: Provides users a quick exploratory dive into common
     visualizations without writing a single line of code given the users
-    data follows the Analysis Data Model (ADaM) standards put forth by 
-    the Clinical Data Interchange Standards Consortium 
-    (CDISC) <https://www.cdisc.org>. Prominent modules/ features of the application are the Table 
-    Generator, Population Explorer, and the Individual Explorer. The Table 
-    Generator allows users to drag and drop variables and desired statistics
-    (frequencies, means, ANOVA, t-test, and other summary statistics) into
-    bins that automagically create stunning tables with validated
-    information. The Population Explorer offers various plots to visualize
-    general trends in the population from various vantage points. Plot
-    modules currently include scatter plot, spaghetti plot, box plot,
-    histogram, means plot, and bar plot. Each plot type allows the user to
-    plot uploaded variables against one another, and dissect the
-    population by filtering out certain subjects. Last, the Individual
-    Explorer establishes a cohesive patient narrative, allowing the user
-    to interact with patient metrics (params) by visit or plotting
-    important patient events on a timeline. All modules allow for concise
-    filtering & downloading bulk outputs into html or pdf formats to save
-    for later.
+    data follows the Analysis Data Model (ADaM) standards put forth by the
+    Clinical Data Interchange Standards Consortium (CDISC)
+    <https://www.cdisc.org>. Prominent modules/ features of the
+    application are the Table Generator, Population Explorer, and the
+    Individual Explorer. The Table Generator allows users to drag and drop
+    variables and desired statistics (frequencies, means, ANOVA, t-test,
+    and other summary statistics) into bins that automagically create
+    stunning tables with validated information. The Population Explorer
+    offers various plots to visualize general trends in the population
+    from various vantage points. Plot modules currently include scatter
+    plot, spaghetti plot, box plot, histogram, means plot, and bar plot.
+    Each plot type allows the user to plot uploaded variables against one
+    another, and dissect the population by filtering out certain subjects.
+    Last, the Individual Explorer establishes a cohesive patient
+    narrative, allowing the user to interact with patient metrics (params)
+    by visit or plotting important patient events on a timeline. All
+    modules allow for concise filtering & downloading bulk outputs into
+    html or pdf formats to save for later.
 License: AGPL (>= 3)
 URL: https://Biogen-Inc.github.io/tidyCDISC/
 BugReports: https://github.com/Biogen-Inc/tidyCDISC/issues
@@ -65,11 +65,11 @@ Imports:
     tippy (== 0.1.0)
 Suggests: 
     knitr,
-    pkgdown,
     spelling,
     testthat
+VignetteBuilder: 
+    knitr
 Encoding: UTF-8
 Language: en-US
 LazyData: true
 RoxygenNote: 7.2.0
-VignetteBuilder: knitr

NEWS.md

@@ -1,23 +1,31 @@
+
+# tidyCDISC 0.2.0 (CRAN Release)
+
+* Added RTF export option for download from the Table Generator.
+* Cleaned up JS to 'standard analyses' drop down.
+* Created "ALL" as dropdown option for 'standard analysis' Table 41 instead of auto populating all available weeks in the drop zone.
+* added protocol / study id in prominent location on each tab
+* designed `ATPT` variable integration in the Table Generator (when present in data)
+* added `OS Health` & `riskmetric` badges to the `README`
+* engineered more user-friendly function to create the table generator output from the block data 
+* ensured Table Generator output automatically updates the table name when a 'standard analysis' is selected
+* Fixed some Population Explorer filtering issues
+* Added an `app.R` file so that shiny app can be ran directly from `shiny::runGitHub()` or `runUrl()`
+
+
 # tidyCDISC 0.1.1
 
 * automated a footnote with **Source** and **Run Date** in table generation
-
 * allowed custom user-defined footnotes
-
 * allowed the selection of 'All' when a time/visit-based statistic is chosen in the table generator
-
 * cleaned up downloadable R Script to replicate table generator output
-
 * fixed bug where `data_from` was erroneously listed as a grouping option in the population explorer
 
 
 # tidyCDISC 0.1.0 (CRAN Release)
 * cleaning up `devtools::check()` & preparing for CRAN release.
-
 * unfortunately, had to revert back to importing `tippy 0.1.0` since it is the latest available version on CRAN and CRAN pkgs cannot depend on package versions under development.
-
 * created a new toggle on the `Data` tab that allows users to select which CDISC pilot study data sources they wish to use in the app.
-
 * Minor bug improvements
 
 # tidyCDISC 0.0.4 
@@ -44,20 +52,20 @@ This release (PR #65) performs a lot of functions, testing on many non-pilot ADS
 This release adds meat to the previous skeleton version of `tidyCDISC`. The initial development release was intended to be a "bare bones" platform that could serve as a foundation to build a great clinical data exploratory tool. Version `0.0.1` enriches the user experience by adding useful & commonly needed features for analysis. For more on this release, see [the announcement](https://biogen-inc.github.io/tidyCDISC/articles/Blog.html#announcing-tidycdisc-0-0-1-1) on our blog.
 
 
-- Major Features added, by tab:
-  - **Table Generator**
-    - New "STAT Blocks", such as `Y FREQ`, `MAX FREQ`, `NON-MISSING`, and a pair of `NESTED FREQ` blocks
-    - Interact with `ADAE`
-    - 17 new 'standard analysis' tables in the `ADAE` and `ADLB` space.
-    - An R Script download-er to reproduce analysis performed in the app
-  - **Population Explorer**
-    - New Chart types
-      - line plot - means over time
-      - Heat map - endpoint correlations
-      - Kaplan Meier Curve (when `TTE` class data uploaded)
-- Upgraded `pkgdown` site documentation to be more robust and hassle-free for developers
-- Depends on updated version of IDEAFilter (>= 0.1.0.9000)
-- Squashed bugs and other minor improvements
+* Major Features added, by tab:
+  * **Table Generator**
+    * New "STAT Blocks", such as `Y FREQ`, `MAX FREQ`, `NON-MISSING`, and a pair of `NESTED FREQ` blocks
+    * Interact with `ADAE`
+    * 17 new 'standard analysis' tables in the `ADAE` and `ADLB` space.
+    * An R Script download-er to reproduce analysis performed in the app
+  * **Population Explorer**
+    * New Chart types
+      * line plot - means over time
+      * Heat map - endpoint correlations
+      * Kaplan Meier Curve (when `TTE` class data uploaded)
+* Upgraded `pkgdown` site documentation to be more robust and hassle-free for developers
+* Depends on updated version of IDEAFilter (>= 0.1.0.9000)
+* Squashed bugs and other minor improvements
 
 # tidyCDISC 0.0.0.9000
 

R/data-phuse_TDF_ADaM.R

@@ -5,7 +5,6 @@
 #'
 #' @source <https://github.com/phuse-org/TestDataFactory/blob/master/Updated/TDF_ADaM/adsl.xpt>, downloaded 2020-06-17
 #' @format Data frame with 254 features and 51 fields
-#' 
 "adsl"
 
 
@@ -16,7 +15,6 @@
 #' @source <https://github.com/phuse-org/TestDataFactory/blob/master/Updated/TDF_ADaM/adlbc.xpt>, downloaded 2020-06-17
 #'
 #' @format Data frame with 32,740 features and 58 fields
-#' 
 "adlbc"
 
 
@@ -27,7 +25,6 @@
 #' @source <https://github.com/phuse-org/TestDataFactory/blob/master/Updated/TDF_ADaM/advs.xpt>, downloaded 2020-06-17
 #'
 #' @format Data frame with 32,139 features and 34 fields
-#' 
 "advs"
 
 
@@ -38,7 +35,6 @@
 #' @source <https://github.com/phuse-org/TestDataFactory/blob/master/Updated/TDF_ADaM/adae.xpt>, downloaded 2020-06-17
 #'
 #' @format Data frame with 32,139 features and 34 fields
-#' 
 "adae"
 
 
@@ -49,5 +45,4 @@
 #' @source <https://github.com/phuse-org/TestDataFactory/blob/master/Updated/TDF_ADaM/adtte.xpt>, downloaded 2021-01-26
 #'
 #' @format Data frame with 32,740 features and 58 fields
-#' 
 "adtte"

R/golem_utils_ui.R

@@ -246,40 +246,47 @@ col_12 <- function(...){
   column(12, ...)
 }
 
+#' @noRd
 #' @importFrom shiny column
 col_10 <- function(...){
   column(10, ...)
 }
 
+#' @noRd
 #' @importFrom shiny column
 col_8 <- function(...){
   column(8, ...)
 }
 
+#' @noRd
 #' @importFrom shiny column
 col_6 <- function(...){
   column(6, ...)
 }
 
 
+#' @noRd
 #' @importFrom shiny column
 col_4 <- function(...){
   column(4, ...)
 }
 
 
+#' @noRd
 #' @importFrom shiny column
 col_3 <- function(...){
   column(3, ...)
 }
 
 
+#' @noRd
 #' @importFrom shiny column
 col_2 <- function(...){
   column(2, ...)
 }
 
 
+#' @noRd
 #' @importFrom shiny column
 col_1 <- function(...){
   column(1, ...)

R/mod_dataComplyRules_fct_helpers.R

@@ -79,7 +79,7 @@ df_incl_rules <-
 #'
 #' @family dataComply Functions
 #' @keywords internal
-#' 
+#' @noRd
 gather_rules <- function(input, output, session,
                          all_df_rules = list(error = c(""), warn = c("") ),
                          expl_rules = list( list(error = c(""), warn = c("")) ),

R/mod_dataUpload.R

@@ -33,6 +33,7 @@ mod_dataUpload_ui <- function(id){
         tooltip = shinyWidgets::tooltipOptions(title = "Click to change pilot data selections!")
       ))
     ),
+    div(uiOutput(ns("study_data_upload")), style = "padding-left: 20px", class = "studyid"),
     fluidRow(
       style = "padding: 20px",
       column(3,
@@ -167,6 +168,12 @@ mod_dataUpload_server <- function(input, output, session){
     paste0("Warning: data uploaded from multiple studies: ", paste(studies(), collapse = " & "))
   }) 
 
+  output$study_data_upload <- renderUI({
+    req(studies())
+    study_ids <- paste(studies(), collapse = " & ")
+    h4(paste("Study ID: ", study_ids))
+  })
+
   # upon a dataset being uploaded and selected, generate data preview
   output$datapreview_header <- renderUI({
     data_selected()

R/mod_indvExpPatEvents.R

@@ -104,12 +104,12 @@ mod_indvExpPatEvents_server <- function(input, output, session,
         shinyjs::show(id = "eventsPlot")
 
         if("MH_" %in% substr(uni_rec$DOMAIN,1,3)){
-          tab <- uni_rec %>% select(-START, -END, -DOMAIN)
+          tab <- uni_rec %>% select(EVENTTYP, tab_st, tab_en, DECODE)
           date_cols <- c("Start of Event","End of Event")
         }
         else{
           date_cols <- "Date of Event"
-          tab <- uni_rec %>% select(-END, -tab_st, -tab_en, -DOMAIN)
+          tab <- uni_rec %>% select(EVENTTYP, START, DECODE)
         }
 
         output$eventsTable <- DT::renderDataTable(server = FALSE, {  # This allows for downloading entire data set

R/mod_indvExp_fct_buildEvents.R

@@ -89,10 +89,10 @@ build_events <- function(
              tab_st = ifelse(as.character(START) == "", NA_character_, as.character(START)), # disp chr in DT
              tab_en = ifelse(as.character(END) == "", NA_character_, as.character(END))      # disp chr in DT
       ) %>%
-      distinct(USUBJID, EVENTTYP, START, END,
-               tab_st,
-               tab_en,
-               DECODE, DOMAIN)%>%
+      distinct(USUBJID, START,
+               DECODE, EVENTTYP,
+               DOMAIN, END,
+               tab_st, tab_en)%>%
       select(-starts_with("DS"))
 
 

R/mod_indvExp_srv.R

@@ -39,6 +39,14 @@ mod_indvExp_server <- function(input, output, session, datafile){
     }
   })
 
+  output$study_indv_exp <- renderUI({
+    req(datafile())
+
+    studies <- unique(unlist(lapply(datafile(), `[[`, "STUDYID")))
+    study_ids <- paste(studies, collapse = " & ")
+    h4(paste("Study ID: ", study_ids))
+  })
+
   # Only select data that starts with AD followed by one or more alphanumerics or underscore
   my_loaded_adams <- reactive({
     req(!is.null(datafile()))

R/mod_indvExp_ui.R

@@ -25,6 +25,7 @@ mod_indvExp_ui <- function(id){
   tagList(
     h1("Individual Explorer", align = "center"),
     br(), br(), br(),
+    div(uiOutput(ns("study_indv_exp")), style = "padding-bottom: 20px", class = "studyid"),
     fluidRow(
       column(3,
          wellPanel(