gp with CRAN in sight

.gitignore

@@ -3,3 +3,4 @@
 .Rproj.user
 test-data/
 
+inst/doc

DESCRIPTION

@@ -1,10 +1,10 @@
 Package: REDCapCAST
 Title: REDCap Castellated data handling
-Version: 23.3.2
+Version: 23.4.1
 Authors@R: c(
-    person("Paul", "Egeler", email = "paul.egeler@spectrumhealth.org", role = c("aut")),
+    person("Andreas Gammelgaard", "Damsbo", email = "agdamsbo@clin.au.dk", role = c("aut", "cre"),
-     person("Andreas Gammelgaard", "Damsbo", email = "agdamsbo@clin.au.dk", role = c("cre", "ctb","cph"),
+           comment = c(ORCID = "0000-0002-7559-1154")),
-           comment = c(ORCID = "0000-0002-7559-1154")))
+           person("Paul", "Egeler", email = "paul.egeler@spectrumhealth.org", role = "aut"))
 Description: This package is based on REDCapRITS by Paul Egeler and Spectrum Health. 
     See [https://github.com/SpectrumHealthResearch/REDCapRITS](https://github.com/SpectrumHealthResearch/REDCapRITS).
     Handle the castellated dataset from REDCap projects with repeating
@@ -27,7 +27,11 @@ Suggests:
     testthat,
     Hmisc,
     readr,
-    covr
+    covr,
+    knitr,
+    rmarkdown,
+    gt,
+    keyring
 License: GPL (>= 3)
 Encoding: UTF-8
 LazyData: true
@@ -46,3 +50,4 @@ Collate:
     'read_redcap_tables.R'
     'redcap_wider.R'
 Language: en-US
+VignetteBuilder: knitr

NEWS.md

@@ -1,3 +1,9 @@
+# REDCapCAST 23.4.1
+
+### Documentation:
+
+* Aiming for CRAN
+
 # REDCapCAST 23.3.2
 
 ### Documentation:

R/REDCap_split.r

@@ -48,15 +48,16 @@
 #' records <- read.csv("/path/to/data/ExampleProject_DATA_2018-06-03_1700.csv")
 #'
 #' # Get the metadata
-#' metadata <- read.csv("/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv")
+#' metadata <- read.csv(
+#' "/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv")
 #'
 #' # Split the tables
 #' REDCapRITS::REDCap_split(records, metadata)
 #'
 #' # In conjunction with the R export script ---------------------------------
 #'
-#' # You must set the working directory first since the REDCap data export script
+#' # You must set the working directory first since the REDCap data export
-#' # contains relative file references.
+#' # script contains relative file references.
 #' setwd("/path/to/data/")
 #'
 #' # Run the data export script supplied by REDCap.
@@ -148,7 +149,8 @@ REDCap_split <- function(records,
 
     if (forms == "repeating" && primary_table_name %in% subtables) {
       warning(
-        "The label given to the primary table is already used by a repeating instrument. The primary table label will be left blank."
+        "The label given to the primary table is already used by a repeating
+        instrument. The primary table label will be left blank."
       )
       primary_table_name <- ""
     } else if (primary_table_name > "") {
@@ -159,7 +161,8 @@ REDCap_split <- function(records,
     for (i in names(out)) {
       if (i == primary_table_name) {
         out_fields <- which(vars_in_data %in% c(universal_fields,
-                                                fields[!fields[, 2] %in% subtables, 1]))
+                                                fields[!fields[, 2] %in%
+                                                         subtables, 1]))
         out[[primary_table_index]] <-
           out[[primary_table_index]][out_fields]
 

R/read_redcap_tables.R

@@ -1,10 +1,9 @@
 #' Download REDCap data
 #'
 #' Implementation of REDCap_split with a focused data acquisition approach using
-#' REDCapR::redcap_read nad only downloading specified fields, forms and/or events
+#' REDCapR::redcap_read nad only downloading specified fields, forms and/or
-#' using the built-in focused_metadata
+#' events using the built-in focused_metadata including some clean-up.
-#' including some clean-up. Works with longitudinal projects with repeating
+#' Works with longitudinal projects with repeating instruments.
-#' instruments.
 #' @param uri REDCap database uri
 #' @param token API token
 #' @param records records to download
@@ -12,7 +11,8 @@
 #' @param events events to download
 #' @param forms forms to download
 #' @param raw_or_label raw or label tags
-#' @param split_forms Whether to split "repeating" or "all" forms, default is all.
+#' @param split_forms Whether to split "repeating" or "all" forms, default is
+#' all.
 #' @param generics vector of auto-generated generic variable names to
 #' ignore when discarding empty rows
 #'
@@ -73,7 +73,8 @@ read_redcap_tables <- function(uri,
   )[["data"]]
 
   # Process repeat instrument naming
-  # Removes any extra characters other than a-z, 0-9 and "_", to mimic raw instrument names.
+  # Removes any extra characters other than a-z, 0-9 and "_", to mimic raw
+  # instrument names.
   if ("redcap_repeat_instrument" %in% names(d)) {
     d$redcap_repeat_instrument <-
     gsub("[^a-z0-9_]", "", gsub(" ", "_", tolower(d$redcap_repeat_instrument)))

R/redcap_wider.R

@@ -27,7 +27,8 @@ redcap_wider <-
            inst.glue = "{.value}_{redcap_repeat_instance}") {
     all_names <- unique(do.call(c, lapply(list, names)))
 
-    if (!any(c("redcap_event_name", "redcap_repeat_instrument") %in% all_names)) {
+    if (!any(c("redcap_event_name", "redcap_repeat_instrument") %in%
+             all_names)) {
       stop(
         "The dataset does not include a 'redcap_event_name' variable.
          redcap_wider only handles projects with repeating instruments or
@@ -35,11 +36,6 @@ redcap_wider <-
       )
     }
 
-    # if (any(grepl("_timestamp",all_names))){
-    #   stop("The dataset includes a '_timestamp' variable, which is not supported
-    #        by this function yet. Sorry! Feel free to contribute :)")
-    # }
-
     id.name <- all_names[1]
 
     l <- lapply(list, function(i) {

R/utils.r

@@ -138,7 +138,8 @@ match_fields_to_form <- function(metadata, vars_in_data) {
   names(fields) <- c("field_name", "form_name")
 
   # Process instrument status fields
-  form_names <- unique(metadata[,grepl(".*[Ff]orm[._][Nn]ame$",names(metadata))])
+  form_names <- unique(metadata[,grepl(".*[Ff]orm[._][Nn]ame$",
+                                       names(metadata))])
   form_complete_fields <- data.frame(
     field_name = paste0(form_names, "_complete"),
     form_name = form_names,

README.md

@@ -9,7 +9,27 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h
 # REDCapCAST
 REDCap Castellated data handling when using repeated instruments.
 
-Modified fork of SpectrumHealthResearch/REDCapRITS. This fork is purely minded on R usage and includes a few implementations of the main `REDCap_split` function.
+This package is a fork of [SpectrumHealthResearch/REDCapRITS](https://github.com/SpectrumHealthResearch/REDCapRITS). The REDCapRITS represents great and extensive work to handle castellated REDCap data in different programming languages. This fork is purely minded on R usage and includes a few implementations of the main `REDCap_split` function.
 
-Fork of [REDCapRITS: REDCap Repeating Instrument Table Splitter](https://github.com/SpectrumHealthResearch/REDCapRITS)
+The main goal for this project was to allow for a "minimal data" approach by allowing to filter records, instruments and variables in the export to only download data needed. I think this approach is desireable for handling sensitive, clinical data. No similar functionality is available from similar tools (like `REDCapR` or `REDCapTidieR`). Please refer to [REDCap-Tools](https://redcap-tools.github.io/) for other great tools.
+
+## Use and immprovements
+
+This package is primarily relevant for working with longitudinal projects and/or projects using repeated instruments. Here is just a short descirption of the main functions:
+
+* `REDcap_split()`: Works largely as the original `REDCapRITS::REDCap_split()`. It takes a REDCap dataset and metadata (data dictionary) to split the data set into a list of dataframes of instruments.
+
+* `read_redcap_tables()`: wraps the use of [`REDCapR::redcap_read()`](https://github.com/OuhscBbmc/REDCapR) with `REDCap_split()` to ease the export of REDCap data.
+
+* `redcap_wider()`: pivots each data frame with repeated instruments to a wide format utilizing the [`tidyr::pivot_wider()`](https://tidyr.tidyverse.org/reference/pivot_wider.html) from the [tidyverse](https://www.tidyverse.org/).
+
+Compared to the original `REDCapRITS`, all matching functions are improved to accept column naming of REDCap data from manual download or API export.
+
+## Installation
+
+Install the latest version directly from GitHub:
+
+```
+remotes::install_github("agdamsbo/REDCapCAST")
+```
 

docs/pkgdown.yml

@@ -1,6 +1,7 @@
 pandoc: 2.19.2
 pkgdown: 2.0.7
 pkgdown_sha: ~
-articles: {}
+articles:
-last_built: 2023-03-08T19:18Z
+  Introduction: Introduction.html
+last_built: 2023-04-13T08:56Z
 

docs/sitemap.xml

@@ -6,6 +6,12 @@
   <url>
     <loc>/LICENSE.html</loc>
   </url>
+  <url>
+    <loc>/articles/Introduction.html</loc>
+  </url>
+  <url>
+    <loc>/articles/index.html</loc>
+  </url>
   <url>
     <loc>/authors.html</loc>
   </url>

inst/WORDLIST

@@ -1,21 +1,29 @@
+CMD
+Codecov
 DataDictionary
 GStat
+GithubActions
 JSON
+Lifecycle
+Pivotting
 README
 REDCap
 REDCapR
 REDCapRITS
 SpectrumHealthResearch
-Splitter
 api
+descirption
+desireable
 doi
 dplyr
 github
 https
+immprovements
 jbi
 matadata
 md
 nad
 og
 thorugh
+tidyverse
 uri

man/REDCap_split.Rd

@@ -74,15 +74,16 @@ REDCapRITS::REDCap_split(records, metadata)
 records <- read.csv("/path/to/data/ExampleProject_DATA_2018-06-03_1700.csv")
 
 # Get the metadata
-metadata <- read.csv("/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv")
+metadata <- read.csv(
+"/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv")
 
 # Split the tables
 REDCapRITS::REDCap_split(records, metadata)
 
 # In conjunction with the R export script ---------------------------------
 
-# You must set the working directory first since the REDCap data export script
+# You must set the working directory first since the REDCap data export
-# contains relative file references.
+# script contains relative file references.
 setwd("/path/to/data/")
 
 # Run the data export script supplied by REDCap.

man/read_redcap_tables.Rd

@@ -32,7 +32,8 @@ read_redcap_tables(
 
 \item{raw_or_label}{raw or label tags}
 
-\item{split_forms}{Whether to split "repeating" or "all" forms, default is all.}
+\item{split_forms}{Whether to split "repeating" or "all" forms, default is
+all.}
 
 \item{generics}{vector of auto-generated generic variable names to
 ignore when discarding empty rows}
@@ -42,10 +43,9 @@ list of instruments
 }
 \description{
 Implementation of REDCap_split with a focused data acquisition approach using
-REDCapR::redcap_read nad only downloading specified fields, forms and/or events
+REDCapR::redcap_read nad only downloading specified fields, forms and/or
-using the built-in focused_metadata
+events using the built-in focused_metadata including some clean-up.
-including some clean-up. Works with longitudinal projects with repeating
+Works with longitudinal projects with repeating instruments.
-instruments.
 }
 \examples{
 # Examples will be provided later

tests/testthat/data/create-ref-data.R

@@ -37,11 +37,11 @@ REDCap_split(
 
 # Longitudinal data from @pbchase; Issue #7 -------------------------------
 
-file_paths <- sapply(
+file_paths <- vapply(
   c(
     records = "WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv",
     metadata = "WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv"
-  ), ref_data_location
+  ), FUN.VALUE = "character", ref_data_location
 )
 
 redcap <- lapply(file_paths, read.csv, stringsAsFactors = FALSE)

tests/testthat/helper-ExampleProject_R_2018-06-07_1129.r

@@ -4,85 +4,113 @@ REDCap_process_csv <- function(data) {
     stop("This test requires the 'Hmisc' package")
   }
 
-  Hmisc::label(data$row) = "Name"
+  Hmisc::label(data$row) <- "Name"
-  Hmisc::label(data$redcap_repeat_instrument) = "Repeat Instrument"
+  Hmisc::label(data$redcap_repeat_instrument) <- "Repeat Instrument"
-  Hmisc::label(data$redcap_repeat_instance) = "Repeat Instance"
+  Hmisc::label(data$redcap_repeat_instance) <- "Repeat Instance"
-  Hmisc::label(data$mpg) = "Miles/(US) gallon"
+  Hmisc::label(data$mpg) <- "Miles/(US) gallon"
-  Hmisc::label(data$cyl) = "Number of cylinders"
+  Hmisc::label(data$cyl) <- "Number of cylinders"
-  Hmisc::label(data$disp) = "Displacement"
+  Hmisc::label(data$disp) <- "Displacement"
-  Hmisc::label(data$hp) = "Gross horsepower"
+  Hmisc::label(data$hp) <- "Gross horsepower"
-  Hmisc::label(data$drat) = "Rear axle ratio"
+  Hmisc::label(data$drat) <- "Rear axle ratio"
-  Hmisc::label(data$wt) = "Weight"
+  Hmisc::label(data$wt) <- "Weight"
-  Hmisc::label(data$qsec) = "1/4 mile time"
+  Hmisc::label(data$qsec) <- "1/4 mile time"
-  Hmisc::label(data$vs) = "V engine?"
+  Hmisc::label(data$vs) <- "V engine?"
-  Hmisc::label(data$am) = "Transmission"
+  Hmisc::label(data$am) <- "Transmission"
-  Hmisc::label(data$gear) = "Number of forward gears"
+  Hmisc::label(data$gear) <- "Number of forward gears"
-  Hmisc::label(data$carb) = "Number of carburetors"
+  Hmisc::label(data$carb) <- "Number of carburetors"
-  Hmisc::label(data$color_available___red) = "Colors Available (choice=Red)"
+  Hmisc::label(data$color_available___red) <-
-  Hmisc::label(data$color_available___green) = "Colors Available (choice=Green)"
+    "Colors Available (choice<-Red)"
-  Hmisc::label(data$color_available___blue) = "Colors Available (choice=Blue)"
+  Hmisc::label(data$color_available___green) <-
-  Hmisc::label(data$color_available___black) = "Colors Available (choice=Black)"
+    "Colors Available (choice<-Green)"
-  Hmisc::label(data$motor_trend_cars_complete) = "Complete?"
+  Hmisc::label(data$color_available___blue) <-
-  Hmisc::label(data$letter_group___a) = "Which group? (choice=A)"
+    "Colors Available (choice<-Blue)"
-  Hmisc::label(data$letter_group___b) = "Which group? (choice=B)"
+  Hmisc::label(data$color_available___black) <-
-  Hmisc::label(data$letter_group___c) = "Which group? (choice=C)"
+    "Colors Available (choice<-Black)"
-  Hmisc::label(data$choice) = "Choose one"
+  Hmisc::label(data$motor_trend_cars_complete) <- "Complete?"
-  Hmisc::label(data$grouping_complete) = "Complete?"
+  Hmisc::label(data$letter_group___a) <- "Which group? (choice<-A)"
-  Hmisc::label(data$price) = "Sale price"
+  Hmisc::label(data$letter_group___b) <- "Which group? (choice<-B)"
-  Hmisc::label(data$color) = "Color"
+  Hmisc::label(data$letter_group___c) <- "Which group? (choice<-C)"
-  Hmisc::label(data$customer) = "Customer Name"
+  Hmisc::label(data$choice) <- "Choose one"
-  Hmisc::label(data$sale_complete) = "Complete?"
+  Hmisc::label(data$grouping_complete) <- "Complete?"
+  Hmisc::label(data$price) <- "Sale price"
+  Hmisc::label(data$color) <- "Color"
+  Hmisc::label(data$customer) <- "Customer Name"
+  Hmisc::label(data$sale_complete) <- "Complete?"
   #Setting Units
 
 
   #Setting Factors(will create new variable for factors)
-  data$redcap_repeat_instrument.factor = factor(data$redcap_repeat_instrument, levels =
+  data$redcap_repeat_instrument.factor <-
+    factor(data$redcap_repeat_instrument, levels <-
              c("sale"))
-  data$cyl.factor = factor(data$cyl, levels = c("3", "4", "5", "6", "7", "8"))
+  data$cyl.factor <-
-  data$vs.factor = factor(data$vs, levels = c("1", "0"))
+    factor(data$cyl, levels <- c("3", "4", "5", "6", "7", "8"))
-  data$am.factor = factor(data$am, levels = c("0", "1"))
+  data$vs.factor <- factor(data$vs, levels <- c("1", "0"))
-  data$gear.factor = factor(data$gear, levels = c("3", "4", "5"))
+  data$am.factor <- factor(data$am, levels <- c("0", "1"))
-  data$carb.factor = factor(data$carb, levels = c("1", "2", "3", "4", "5", "6", "7", "8"))
+  data$gear.factor <- factor(data$gear, levels <- c("3", "4", "5"))
-  data$color_available___red.factor = factor(data$color_available___red, levels =
+  data$carb.factor <-
+    factor(data$carb, levels <-
+             c("1", "2", "3", "4", "5", "6", "7", "8"))
+  data$color_available___red.factor <-
+    factor(data$color_available___red, levels <-
              c("0", "1"))
-  data$color_available___green.factor = factor(data$color_available___green, levels =
+  data$color_available___green.factor <-
+    factor(data$color_available___green, levels <-
              c("0", "1"))
-  data$color_available___blue.factor = factor(data$color_available___blue, levels =
+  data$color_available___blue.factor <-
+    factor(data$color_available___blue, levels <-
              c("0", "1"))
-  data$color_available___black.factor = factor(data$color_available___black, levels =
+  data$color_available___black.factor <-
+    factor(data$color_available___black, levels <-
              c("0", "1"))
-  data$motor_trend_cars_complete.factor = factor(data$motor_trend_cars_complete, levels =
+  data$motor_trend_cars_complete.factor <-
+    factor(data$motor_trend_cars_complete, levels <-
              c("0", "1", "2"))
-  data$letter_group___a.factor = factor(data$letter_group___a, levels =
+  data$letter_group___a.factor <-
+    factor(data$letter_group___a, levels <-
              c("0", "1"))
-  data$letter_group___b.factor = factor(data$letter_group___b, levels =
+  data$letter_group___b.factor <-
+    factor(data$letter_group___b, levels <-
              c("0", "1"))
-  data$letter_group___c.factor = factor(data$letter_group___c, levels =
+  data$letter_group___c.factor <-
+    factor(data$letter_group___c, levels <-
              c("0", "1"))
-  data$choice.factor = factor(data$choice, levels = c("choice1", "choice2"))
+  data$choice.factor <-
-  data$grouping_complete.factor = factor(data$grouping_complete, levels =
+    factor(data$choice, levels <- c("choice1", "choice2"))
+  data$grouping_complete.factor <-
+    factor(data$grouping_complete, levels <-
              c("0", "1", "2"))
-  data$color.factor = factor(data$color, levels = c("1", "2", "3", "4"))
+  data$color.factor <-
-  data$sale_complete.factor = factor(data$sale_complete, levels = c("0", "1", "2"))
+    factor(data$color, levels <- c("1", "2", "3", "4"))
+  data$sale_complete.factor <-
+    factor(data$sale_complete, levels <- c("0", "1", "2"))
 
-  levels(data$redcap_repeat_instrument.factor) = c("Sale")
+  levels(data$redcap_repeat_instrument.factor) <- c("Sale")
-  levels(data$cyl.factor) = c("3", "4", "5", "6", "7", "8")
+  levels(data$cyl.factor) <- c("3", "4", "5", "6", "7", "8")
-  levels(data$vs.factor) = c("Yes", "No")
+  levels(data$vs.factor) <- c("Yes", "No")
-  levels(data$am.factor) = c("Automatic", "Manual")
+  levels(data$am.factor) <- c("Automatic", "Manual")
-  levels(data$gear.factor) = c("3", "4", "5")
+  levels(data$gear.factor) <- c("3", "4", "5")
-  levels(data$carb.factor) = c("1", "2", "3", "4", "5", "6", "7", "8")
+  levels(data$carb.factor) <-
-  levels(data$color_available___red.factor) = c("Unchecked", "Checked")
+    c("1", "2", "3", "4", "5", "6", "7", "8")
-  levels(data$color_available___green.factor) = c("Unchecked", "Checked")
+  levels(data$color_available___red.factor) <-
-  levels(data$color_available___blue.factor) = c("Unchecked", "Checked")
+    c("Unchecked", "Checked")
-  levels(data$color_available___black.factor) = c("Unchecked", "Checked")
+  levels(data$color_available___green.factor) <-
-  levels(data$motor_trend_cars_complete.factor) = c("Incomplete", "Unverified", "Complete")
+    c("Unchecked", "Checked")
-  levels(data$letter_group___a.factor) = c("Unchecked", "Checked")
+  levels(data$color_available___blue.factor) <-
-  levels(data$letter_group___b.factor) = c("Unchecked", "Checked")
+    c("Unchecked", "Checked")
-  levels(data$letter_group___c.factor) = c("Unchecked", "Checked")
+  levels(data$color_available___black.factor) <-
-  levels(data$choice.factor) = c("Choice 1", "Choice 2")
+    c("Unchecked", "Checked")
-  levels(data$grouping_complete.factor) = c("Incomplete", "Unverified", "Complete")
+  levels(data$motor_trend_cars_complete.factor) <-
-  levels(data$color.factor) = c("red", "green", "blue", "black")
+    c("Incomplete", "Unverified", "Complete")
-  levels(data$sale_complete.factor) = c("Incomplete", "Unverified", "Complete")
+  levels(data$letter_group___a.factor) <- c("Unchecked", "Checked")
+  levels(data$letter_group___b.factor) <- c("Unchecked", "Checked")
+  levels(data$letter_group___c.factor) <- c("Unchecked", "Checked")
+  levels(data$choice.factor) <- c("Choice 1", "Choice 2")
+  levels(data$grouping_complete.factor) <-
+    c("Incomplete", "Unverified", "Complete")
+  levels(data$color.factor) <- c("red", "green", "blue", "black")
+  levels(data$sale_complete.factor) <-
+    c("Incomplete", "Unverified", "Complete")
 
   data
 }

tests/testthat/test-API.R

@@ -1,9 +1,12 @@
 
+
 # Check the RCurl export ---------------------------------------------------
 test_that("JSON character vector from RCurl matches reference", {
-  metadata <- jsonlite::fromJSON(get_data_location("ExampleProject_metadata.json"))
+  metadata <-
+    jsonlite::fromJSON(get_data_location("ExampleProject_metadata.json"))
 
-  records <- jsonlite::fromJSON(get_data_location("ExampleProject_records.json"))
+  records <-
+    jsonlite::fromJSON(get_data_location("ExampleProject_records.json"))
 
   redcap_output_json1 <- REDCap_split(records, metadata)
 

tests/testthat/test-csv-exports.R

@@ -1,11 +1,13 @@
 
+
 # Set up the path and data -------------------------------------------------
 metadata <- read.csv(
   get_data_location("ExampleProject_DataDictionary_2018-06-07.csv"),
   stringsAsFactors = TRUE
 )
 
-records <- read.csv(get_data_location("ExampleProject_DATA_2018-06-07_1129.csv"),
+records <-
+  read.csv(get_data_location("ExampleProject_DATA_2018-06-07_1129.csv"),
            stringsAsFactors = TRUE)
 
 redcap_output_csv1 <- REDCap_split(records, metadata)
@@ -18,16 +20,18 @@ test_that("CSV export matches reference", {
 # Test that REDCap_split can handle a focused dataset
 
 records_red <- records[!records$redcap_repeat_instrument == "sale",
-                   !names(records) %in% metadata$field_name[metadata$form_name == "sale"] &
+                       !names(records) %in%
+                         metadata$field_name[metadata$form_name == "sale"] &
                          !names(records) == "sale_complete"]
-records_red$redcap_repeat_instrument <- as.character(records_red$redcap_repeat_instrument)
+records_red$redcap_repeat_instrument <-
+  as.character(records_red$redcap_repeat_instrument)
 
 redcap_output_red <- REDCap_split(records_red, metadata)
 
 
 test_that("REDCap_split handles subset dataset",
           {
-            testthat::expect_length(redcap_output_red,1)
+            testthat::expect_length(redcap_output_red, 1)
           })
 
 
@@ -37,17 +41,20 @@ if (requireNamespace("Hmisc", quietly = TRUE)) {
     redcap_output_csv2 <-
       REDCap_split(REDCap_process_csv(records), metadata)
 
-    expect_known_hash(redcap_output_csv2, "34f82cab35bf8aae47d08cd96f743e6b")
+    expect_known_hash(redcap_output_csv2, "6d8d0462ab2343b848a086ab06b50fe3")
   })
 }
 
 
 if (requireNamespace("readr", quietly = TRUE)) {
-  context("Compatibility with readr")
 
-  metadata <- readr::read_csv(get_data_location("ExampleProject_DataDictionary_2018-06-07.csv"))
+  metadata <-
+    readr::read_csv(get_data_location(
+      "ExampleProject_DataDictionary_2018-06-07.csv"))
 
-  records <- readr::read_csv(get_data_location("ExampleProject_DATA_2018-06-07_1129.csv"))
+  records <-
+    readr::read_csv(get_data_location(
+      "ExampleProject_DATA_2018-06-07_1129.csv"))
 
   redcap_output_readr <- REDCap_split(records, metadata)
 
@@ -57,11 +64,14 @@ if (requireNamespace("readr", quietly = TRUE)) {
                      lapply(redcap_output_csv1, FUN))
   }
 
-  test_that("Result of data read in with `readr` will match result with `read.csv`",
+  test_that("Result of data read in with `readr` will
+            match result with `read.csv`",
             {
               # The list itself
-              expect_identical(length(redcap_output_readr), length(redcap_output_csv1))
+              expect_identical(length(redcap_output_readr),
-              expect_identical(names(redcap_output_readr), names(redcap_output_csv1))
+                               length(redcap_output_csv1))
+              expect_identical(names(redcap_output_readr),
+                               names(redcap_output_csv1))
 
               # Each element of the list
               expect_matching_elements(names)
@@ -69,5 +79,3 @@ if (requireNamespace("readr", quietly = TRUE)) {
             })
 
 }
-
-

tests/testthat/test-forms-arg.R

@@ -1,5 +1,6 @@
 
 
+
 # Global variables --------------------------------------------------------
 
 # Cars
@@ -12,14 +13,15 @@ records <-
 redcap_output_json <- REDCap_split(records, metadata, forms = "all")
 
 # Longitudinal
-file_paths <- sapply(
+file_paths <- vapply(
   c(records = "WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv",
     metadata = "WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv"),
+  FUN.VALUE = "character",
   get_data_location
 )
 
 redcap <- lapply(file_paths, read.csv, stringsAsFactors = FALSE)
-redcap[["metadata"]] <- with(redcap, metadata[metadata[, 1] > "",])
+redcap[["metadata"]] <- with(redcap, metadata[metadata[, 1] > "", ])
 redcap_output_long <-
   with(redcap, REDCap_split(records, metadata, forms = "all"))
 redcap_long_names <- names(redcap[[1]])
@@ -35,7 +37,7 @@ test_that("Each form is an element in the list", {
 
 test_that("All variables land somewhere", {
   expect_true(setequal(names(records), Reduce(
-    "union", sapply(redcap_output_json, names)
+    "union", lapply(redcap_output_json, names)
   )))
 
 })
@@ -47,11 +49,8 @@ test_that("Primary table name is ignored", {
 })
 
 test_that("Supports longitudinal data", {
-  # setdiff(redcap_long_names, Reduce("union", sapply(redcap_output_long, names)))
-  ##  [1] "informed_consent_and_addendum_timestamp"
-
   expect_true(setequal(redcap_long_names, Reduce(
-    "union", sapply(redcap_output_long, names)
+    "union", lapply(redcap_output_long, names)
   )))
 
 })

tests/testthat/test-longitudinal.R

@@ -1,11 +1,11 @@
 ## "Longitudinal data"
 
 test_that("CSV export matches reference", {
-  file_paths <- sapply(
+  file_paths <- vapply(
     c(
       records = "WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv",
       metadata = "WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv"
-    ), get_data_location
+    ), get_data_location, FUN.VALUE = "character"
   )
 
   redcap <- lapply(file_paths, read.csv, stringsAsFactors = FALSE)

tests/testthat/test-primary-table-name.R

@@ -2,15 +2,18 @@
 
 
 # Global  variables -------------------------------------------------------
-metadata <- jsonlite::fromJSON(get_data_location("ExampleProject_metadata.json"))
+metadata <-
+  jsonlite::fromJSON(get_data_location("ExampleProject_metadata.json"))
 
-records <- jsonlite::fromJSON(get_data_location("ExampleProject_records.json"))
+records <-
+  jsonlite::fromJSON(get_data_location("ExampleProject_records.json"))
 
 ref_hash <- "2c8b6531597182af1248f92124161e0c"
 
 # Tests -------------------------------------------------------------------
 test_that("Will not use a repeating instrument name for primary table", {
-  redcap_output_json1 <- expect_warning(REDCap_split(records, metadata, "sale"),
+  redcap_output_json1 <-
+    expect_warning(REDCap_split(records, metadata, "sale"),
                    "primary table")
 
   expect_known_hash(redcap_output_json1, ref_hash)

tests/testthat/test-redcap_wider.R

@@ -1,33 +1,46 @@
 test_that("redcap_wider() returns expected output", {
-  list <- list(data.frame(record_id = c(1,2,1,2), redcap_event_name = c("baseline", "baseline", "followup", "followup"), age = c(25,26,27,28)),
+  list <-
-               data.frame(record_id = c(1,2), redcap_event_name = c("baseline", "baseline"), gender = c("male", "female")))
+    list(
+      data.frame(
+        record_id = c(1, 2, 1, 2),
+        redcap_event_name = c("baseline", "baseline", "followup", "followup"),
+        age = c(25, 26, 27, 28)
+      ),
+      data.frame(
+        record_id = c(1, 2),
+        redcap_event_name = c("baseline", "baseline"),
+        gender = c("male", "female")
+      )
+    )
 
-  expect_equal(redcap_wider(list),
+  expect_equal(
-               data.frame(record_id = c(1,2),
+    redcap_wider(list),
-                          age_baseline = c(25,26),
+    data.frame(
-                          age_followup = c(27,28),
+      record_id = c(1, 2),
-                          gender = c("male","female")))
+      age_baseline = c(25, 26),
+      age_followup = c(27, 28),
+      gender = c("male", "female")
+    )
+  )
 })
 
 
 # Using test data
 
 # Set up the path and data -------------------------------------------------
-file_paths <- sapply(
+file_paths <- lapply(
   c(records = "WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv",
     metadata = "WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv"),
   get_data_location
 )
 
 redcap <- lapply(file_paths, read.csv, stringsAsFactors = FALSE)
-redcap[["metadata"]] <- with(redcap, metadata[metadata[, 1] > "",])
+redcap[["metadata"]] <- with(redcap, metadata[metadata[, 1] > "", ])
 list <-
   with(redcap, REDCap_split(records, metadata, forms = "all"))
 
 wide_ds <- redcap_wider(list)
 
-test_that("redcap_wider() returns wide output from CSV",{
+test_that("redcap_wider() returns wide output from CSV", {
-  expect_equal(ncol(wide_ds),171)
+  expect_equal(ncol(wide_ds), 171)
 })
-
-

vignettes/.gitignore

@@ -0,0 +1,2 @@
+*.html
+*.R

vignettes/Introduction.Rmd

@@ -0,0 +1,76 @@
+---
+title: "Introduction"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Introduction}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r setup}
+library(REDCapCAST)
+```
+
+This vignette covers the included functions and basic functionality.
+
+## Splitting the dataset
+
+```{r eval=FALSE}
+keyring::key_set("handbook_api")
+keyring::key_set("cast_api")
+```
+
+```{r include=FALSE}
+uri <- keyring::key_get("DB_URI")
+```
+
+
+```{r}
+dataset <- REDCapR::redcap_read_oneshot(redcap_uri = uri,
+                                   token = keyring::key_get("cast_api"))$data
+
+dataset |> gt::gt()
+```
+
+```{r}
+metadata <- REDCapR::redcap_metadata_read(redcap_uri = uri,
+                                   token = keyring::key_get("cast_api"))$data
+metadata |> gt::gt()
+```
+```{r}
+list <-
+  REDCapCAST::REDCap_split(records = dataset,
+                           metadata = metadata,
+                           forms = "repeating")
+str(list)
+```
+
+```{r}
+list <-
+  REDCapCAST::REDCap_split(records = dataset,
+                           metadata = metadata,
+                           forms = "all")
+str(list)
+```
+
+## Reading data from REDCap
+
+```{r}
+ds <- read_redcap_tables(uri = uri, token = keyring::key_get("cast_api"))
+str(ds)
+```
+
+## Pivotting to wider format
+
+```{r}
+redcap_wider(ds) |> gt::gt()
+```
+
+