Merge pull request #159 from Biogen-Inc/prep_cran020_rtf

Prep cran `v0.2.0`

Author: AARON-CLARK

.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:

DESCRIPTION

@@ -1,37 +1,37 @@
 Package: tidyCDISC
 Title: Quick Table Generation & Exploratory Analyses on ADaM-Ish Datasets
-Version: 0.1.1.9001
+Version: 0.2.0
 Authors@R: c(
-    person("Aaron", "Clark", , "clark.aaronchris@gmail.com",
-      role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0123-0970")),
+    person("Aaron", "Clark", , "clark.aaronchris@gmail.com", role = c("aut", "cre"),
+           comment = c(ORCID = "0000-0002-0123-0970")),
+    person("Jeff", "Thompson", , "jeff.thompson51317@gmail.com", role = "aut"),
+    person("Teresa", "Wilson", , "teresadwilson@gmail.com", role = "aut"),
     person("Nate", "Mockler", , "nate.mockler@biogen.com", role = c("ccp", "led")),
     person("Maya", "Gans", , "maya.gans@biogen.com", role = "aut"),
     person("Robert", "Krajcik", , "robert.krajcik@biogen.com", role = "ctb"),
     person("Marly", "Gotti", , "marly.cormar@biogen.com", role = "ctb"),
-    person(given = "Jeff", family = "Thompson", 
-        email = "jeff.thompson51317@gmail.com", 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

@@ -1,30 +1,31 @@
-# tidyCDISC (development version)
-* Add option for RTF download to Table Generator.
-* Clean up JS to input `RECIPE`s (aka stanard analyses).
-* Created "ALL" as dropdown option for Standard Analyses 41 instead of listing all available weeks.
-* Make the list of columns available to filter on in the Population Explorer depend upon the data frame(s) selected for filtering
-* Added protocol / study id in prominent locaiton on each tab
+
+# 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 
@@ -51,20 +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_tableGen_fct_methods.R

@@ -21,28 +21,30 @@
 #' @export
 #' @keywords tabGen_repro
 #' 
-#' @examples 
-#' data(example_dat1, package = "tidyCDISC")
+#' @examples
+#' if(interactive()){
+#'   data(example_dat1, package = "tidyCDISC")
 #' 
-#' # Create non-missing table section
-#' app_methods("NON_MISSING", 
-#'             structure("USUBJID", class = c("character", "ADSL")), NA, 
-#'             "TRT01P", example_dat1$AE, example_dat1$totals)
+#'   # Create non-missing table section
+#'   app_methods("NON_MISSING", 
+#'              structure("USUBJID", class = c("character", "ADSL")), NA, 
+#'              "TRT01P", example_dat1$AE, example_dat1$totals)
 #'             
-#' # Create ANOVA table section
-#' app_methods("ANOVA", 
+#'   # Create ANOVA table section
+#'   app_methods("ANOVA", 
 #'             structure("TEMP", class = c("character", "BDS")), "Week 2", 
 #'             "TRT01P", example_dat1$BDS, example_dat1$totals)
 #' 
-#' # Create change table section
-#' app_methods("CHG", 
+#'   # Create change table section
+#'   app_methods("CHG", 
 #'             structure("WEIGHT", class = c("character", "BDS")), "Week 12", 
 #'             "TRT01P", example_dat1$BDS, example_dat1$totals)
 #' 
-#' # Create mean table section
-#' app_methods("MEAN", 
+#'   # Create mean table section
+#'   app_methods("MEAN", 
 #'             structure("PULSE", class = c("character", "BDS")), "Baseline", 
 #'             "TRT01P", example_dat1$BDS, example_dat1$totals)
+#' }
 app_methods <- function(agg, column, week, group, data, totals, filter = NA) {
   # informative error in case the selected variable doesn't exist in data
   # if no data in the source, do not run the pmap, just show this msg: