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gp with CRAN in sight
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AG Damsbo
parent
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commit
349ff695e1
1
.gitignore
vendored
1
.gitignore
vendored
@ -3,3 +3,4 @@
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.Rproj.user
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test-data/
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inst/doc
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15
DESCRIPTION
15
DESCRIPTION
@ -1,10 +1,10 @@
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Package: REDCapCAST
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Title: REDCap Castellated data handling
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Version: 23.3.2
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Version: 23.4.1
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Authors@R: c(
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person("Paul", "Egeler", email = "paul.egeler@spectrumhealth.org", role = c("aut")),
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person("Andreas Gammelgaard", "Damsbo", email = "agdamsbo@clin.au.dk", role = c("cre", "ctb","cph"),
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comment = c(ORCID = "0000-0002-7559-1154")))
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person("Andreas Gammelgaard", "Damsbo", email = "agdamsbo@clin.au.dk", role = c("aut", "cre"),
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comment = c(ORCID = "0000-0002-7559-1154")),
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person("Paul", "Egeler", email = "paul.egeler@spectrumhealth.org", role = "aut"))
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Description: This package is based on REDCapRITS by Paul Egeler and Spectrum Health.
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See [https://github.com/SpectrumHealthResearch/REDCapRITS](https://github.com/SpectrumHealthResearch/REDCapRITS).
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Handle the castellated dataset from REDCap projects with repeating
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@ -27,7 +27,11 @@ Suggests:
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testthat,
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Hmisc,
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readr,
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covr
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covr,
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knitr,
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rmarkdown,
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gt,
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keyring
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License: GPL (>= 3)
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Encoding: UTF-8
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LazyData: true
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@ -46,3 +50,4 @@ Collate:
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'read_redcap_tables.R'
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'redcap_wider.R'
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Language: en-US
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VignetteBuilder: knitr
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6
NEWS.md
6
NEWS.md
@ -1,3 +1,9 @@
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# REDCapCAST 23.4.1
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### Documentation:
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* Aiming for CRAN
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# REDCapCAST 23.3.2
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### Documentation:
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@ -48,15 +48,16 @@
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#' records <- read.csv("/path/to/data/ExampleProject_DATA_2018-06-03_1700.csv")
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#'
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#' # Get the metadata
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#' metadata <- read.csv("/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv")
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#' metadata <- read.csv(
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#' "/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv")
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#'
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#' # Split the tables
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#' REDCapRITS::REDCap_split(records, metadata)
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#'
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#' # In conjunction with the R export script ---------------------------------
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#'
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#' # You must set the working directory first since the REDCap data export script
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#' # contains relative file references.
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#' # You must set the working directory first since the REDCap data export
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#' # script contains relative file references.
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#' setwd("/path/to/data/")
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#'
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#' # Run the data export script supplied by REDCap.
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@ -148,7 +149,8 @@ REDCap_split <- function(records,
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if (forms == "repeating" && primary_table_name %in% subtables) {
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warning(
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"The label given to the primary table is already used by a repeating instrument. The primary table label will be left blank."
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"The label given to the primary table is already used by a repeating
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instrument. The primary table label will be left blank."
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)
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primary_table_name <- ""
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} else if (primary_table_name > "") {
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@ -159,7 +161,8 @@ REDCap_split <- function(records,
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for (i in names(out)) {
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if (i == primary_table_name) {
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out_fields <- which(vars_in_data %in% c(universal_fields,
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fields[!fields[, 2] %in% subtables, 1]))
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fields[!fields[, 2] %in%
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subtables, 1]))
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out[[primary_table_index]] <-
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out[[primary_table_index]][out_fields]
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@ -1,10 +1,9 @@
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#' Download REDCap data
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#'
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#' Implementation of REDCap_split with a focused data acquisition approach using
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#' REDCapR::redcap_read nad only downloading specified fields, forms and/or events
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#' using the built-in focused_metadata
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#' including some clean-up. Works with longitudinal projects with repeating
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#' instruments.
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#' REDCapR::redcap_read nad only downloading specified fields, forms and/or
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#' events using the built-in focused_metadata including some clean-up.
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#' Works with longitudinal projects with repeating instruments.
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#' @param uri REDCap database uri
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#' @param token API token
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#' @param records records to download
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@ -12,7 +11,8 @@
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#' @param events events to download
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#' @param forms forms to download
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#' @param raw_or_label raw or label tags
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#' @param split_forms Whether to split "repeating" or "all" forms, default is all.
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#' @param split_forms Whether to split "repeating" or "all" forms, default is
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#' all.
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#' @param generics vector of auto-generated generic variable names to
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#' ignore when discarding empty rows
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#'
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@ -73,7 +73,8 @@ read_redcap_tables <- function(uri,
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)[["data"]]
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# Process repeat instrument naming
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# Removes any extra characters other than a-z, 0-9 and "_", to mimic raw instrument names.
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# Removes any extra characters other than a-z, 0-9 and "_", to mimic raw
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# instrument names.
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if ("redcap_repeat_instrument" %in% names(d)) {
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d$redcap_repeat_instrument <-
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gsub("[^a-z0-9_]", "", gsub(" ", "_", tolower(d$redcap_repeat_instrument)))
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@ -27,7 +27,8 @@ redcap_wider <-
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inst.glue = "{.value}_{redcap_repeat_instance}") {
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all_names <- unique(do.call(c, lapply(list, names)))
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if (!any(c("redcap_event_name", "redcap_repeat_instrument") %in% all_names)) {
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if (!any(c("redcap_event_name", "redcap_repeat_instrument") %in%
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all_names)) {
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stop(
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"The dataset does not include a 'redcap_event_name' variable.
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redcap_wider only handles projects with repeating instruments or
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@ -35,11 +36,6 @@ redcap_wider <-
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)
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}
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# if (any(grepl("_timestamp",all_names))){
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# stop("The dataset includes a '_timestamp' variable, which is not supported
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# by this function yet. Sorry! Feel free to contribute :)")
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# }
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id.name <- all_names[1]
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l <- lapply(list, function(i) {
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@ -138,7 +138,8 @@ match_fields_to_form <- function(metadata, vars_in_data) {
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names(fields) <- c("field_name", "form_name")
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# Process instrument status fields
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form_names <- unique(metadata[,grepl(".*[Ff]orm[._][Nn]ame$",names(metadata))])
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form_names <- unique(metadata[,grepl(".*[Ff]orm[._][Nn]ame$",
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names(metadata))])
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form_complete_fields <- data.frame(
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field_name = paste0(form_names, "_complete"),
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form_name = form_names,
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24
README.md
24
README.md
@ -9,7 +9,27 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h
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# REDCapCAST
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REDCap Castellated data handling when using repeated instruments.
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Modified fork of SpectrumHealthResearch/REDCapRITS. This fork is purely minded on R usage and includes a few implementations of the main `REDCap_split` function.
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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.
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Fork of [REDCapRITS: REDCap Repeating Instrument Table Splitter](https://github.com/SpectrumHealthResearch/REDCapRITS)
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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.
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## Use and immprovements
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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:
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* `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.
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* `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.
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* `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/).
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Compared to the original `REDCapRITS`, all matching functions are improved to accept column naming of REDCap data from manual download or API export.
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## Installation
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Install the latest version directly from GitHub:
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```
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remotes::install_github("agdamsbo/REDCapCAST")
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```
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@ -1,6 +1,7 @@
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pandoc: 2.19.2
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pkgdown: 2.0.7
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pkgdown_sha: ~
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articles: {}
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last_built: 2023-03-08T19:18Z
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articles:
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Introduction: Introduction.html
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last_built: 2023-04-13T08:56Z
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File diff suppressed because one or more lines are too long
@ -6,6 +6,12 @@
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<url>
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<loc>/LICENSE.html</loc>
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</url>
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<url>
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<loc>/articles/Introduction.html</loc>
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</url>
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<url>
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<loc>/articles/index.html</loc>
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</url>
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<url>
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<loc>/authors.html</loc>
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</url>
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@ -1,21 +1,29 @@
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CMD
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Codecov
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DataDictionary
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GStat
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GithubActions
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JSON
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Lifecycle
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Pivotting
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README
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REDCap
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REDCapR
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REDCapRITS
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SpectrumHealthResearch
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Splitter
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api
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descirption
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desireable
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doi
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dplyr
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github
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https
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immprovements
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jbi
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matadata
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md
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nad
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og
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thorugh
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tidyverse
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uri
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@ -74,15 +74,16 @@ REDCapRITS::REDCap_split(records, metadata)
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records <- read.csv("/path/to/data/ExampleProject_DATA_2018-06-03_1700.csv")
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# Get the metadata
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metadata <- read.csv("/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv")
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metadata <- read.csv(
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"/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv")
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# Split the tables
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REDCapRITS::REDCap_split(records, metadata)
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# In conjunction with the R export script ---------------------------------
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# You must set the working directory first since the REDCap data export script
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# contains relative file references.
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# You must set the working directory first since the REDCap data export
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# script contains relative file references.
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setwd("/path/to/data/")
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# Run the data export script supplied by REDCap.
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@ -32,7 +32,8 @@ read_redcap_tables(
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\item{raw_or_label}{raw or label tags}
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\item{split_forms}{Whether to split "repeating" or "all" forms, default is all.}
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\item{split_forms}{Whether to split "repeating" or "all" forms, default is
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all.}
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\item{generics}{vector of auto-generated generic variable names to
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ignore when discarding empty rows}
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@ -42,10 +43,9 @@ list of instruments
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}
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\description{
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Implementation of REDCap_split with a focused data acquisition approach using
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REDCapR::redcap_read nad only downloading specified fields, forms and/or events
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using the built-in focused_metadata
|
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including some clean-up. Works with longitudinal projects with repeating
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instruments.
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REDCapR::redcap_read nad only downloading specified fields, forms and/or
|
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events using the built-in focused_metadata including some clean-up.
|
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Works with longitudinal projects with repeating instruments.
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}
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\examples{
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# Examples will be provided later
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@ -37,11 +37,11 @@ REDCap_split(
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# Longitudinal data from @pbchase; Issue #7 -------------------------------
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file_paths <- sapply(
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file_paths <- vapply(
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c(
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records = "WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv",
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metadata = "WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv"
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), ref_data_location
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), FUN.VALUE = "character", ref_data_location
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)
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redcap <- lapply(file_paths, read.csv, stringsAsFactors = FALSE)
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@ -4,85 +4,113 @@ REDCap_process_csv <- function(data) {
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stop("This test requires the 'Hmisc' package")
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}
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Hmisc::label(data$row) = "Name"
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Hmisc::label(data$redcap_repeat_instrument) = "Repeat Instrument"
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Hmisc::label(data$redcap_repeat_instance) = "Repeat Instance"
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Hmisc::label(data$mpg) = "Miles/(US) gallon"
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Hmisc::label(data$cyl) = "Number of cylinders"
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Hmisc::label(data$disp) = "Displacement"
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Hmisc::label(data$hp) = "Gross horsepower"
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Hmisc::label(data$drat) = "Rear axle ratio"
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Hmisc::label(data$wt) = "Weight"
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Hmisc::label(data$qsec) = "1/4 mile time"
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Hmisc::label(data$vs) = "V engine?"
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Hmisc::label(data$am) = "Transmission"
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Hmisc::label(data$gear) = "Number of forward gears"
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Hmisc::label(data$carb) = "Number of carburetors"
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Hmisc::label(data$color_available___red) = "Colors Available (choice=Red)"
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Hmisc::label(data$color_available___green) = "Colors Available (choice=Green)"
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Hmisc::label(data$color_available___blue) = "Colors Available (choice=Blue)"
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Hmisc::label(data$color_available___black) = "Colors Available (choice=Black)"
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Hmisc::label(data$motor_trend_cars_complete) = "Complete?"
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Hmisc::label(data$letter_group___a) = "Which group? (choice=A)"
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Hmisc::label(data$letter_group___b) = "Which group? (choice=B)"
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Hmisc::label(data$letter_group___c) = "Which group? (choice=C)"
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Hmisc::label(data$choice) = "Choose one"
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Hmisc::label(data$grouping_complete) = "Complete?"
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Hmisc::label(data$price) = "Sale price"
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Hmisc::label(data$color) = "Color"
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Hmisc::label(data$customer) = "Customer Name"
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Hmisc::label(data$sale_complete) = "Complete?"
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Hmisc::label(data$row) <- "Name"
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Hmisc::label(data$redcap_repeat_instrument) <- "Repeat Instrument"
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Hmisc::label(data$redcap_repeat_instance) <- "Repeat Instance"
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Hmisc::label(data$mpg) <- "Miles/(US) gallon"
|
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Hmisc::label(data$cyl) <- "Number of cylinders"
|
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Hmisc::label(data$disp) <- "Displacement"
|
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Hmisc::label(data$hp) <- "Gross horsepower"
|
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Hmisc::label(data$drat) <- "Rear axle ratio"
|
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Hmisc::label(data$wt) <- "Weight"
|
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Hmisc::label(data$qsec) <- "1/4 mile time"
|
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Hmisc::label(data$vs) <- "V engine?"
|
||||
Hmisc::label(data$am) <- "Transmission"
|
||||
Hmisc::label(data$gear) <- "Number of forward gears"
|
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Hmisc::label(data$carb) <- "Number of carburetors"
|
||||
Hmisc::label(data$color_available___red) <-
|
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"Colors Available (choice<-Red)"
|
||||
Hmisc::label(data$color_available___green) <-
|
||||
"Colors Available (choice<-Green)"
|
||||
Hmisc::label(data$color_available___blue) <-
|
||||
"Colors Available (choice<-Blue)"
|
||||
Hmisc::label(data$color_available___black) <-
|
||||
"Colors Available (choice<-Black)"
|
||||
Hmisc::label(data$motor_trend_cars_complete) <- "Complete?"
|
||||
Hmisc::label(data$letter_group___a) <- "Which group? (choice<-A)"
|
||||
Hmisc::label(data$letter_group___b) <- "Which group? (choice<-B)"
|
||||
Hmisc::label(data$letter_group___c) <- "Which group? (choice<-C)"
|
||||
Hmisc::label(data$choice) <- "Choose one"
|
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Hmisc::label(data$grouping_complete) <- "Complete?"
|
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Hmisc::label(data$price) <- "Sale price"
|
||||
Hmisc::label(data$color) <- "Color"
|
||||
Hmisc::label(data$customer) <- "Customer Name"
|
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Hmisc::label(data$sale_complete) <- "Complete?"
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#Setting Units
|
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|
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|
||||
#Setting Factors(will create new variable for factors)
|
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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"))
|
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data$vs.factor = factor(data$vs, levels = c("1", "0"))
|
||||
data$am.factor = factor(data$am, levels = c("0", "1"))
|
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data$gear.factor = factor(data$gear, levels = c("3", "4", "5"))
|
||||
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 =
|
||||
data$cyl.factor <-
|
||||
factor(data$cyl, levels <- c("3", "4", "5", "6", "7", "8"))
|
||||
data$vs.factor <- factor(data$vs, levels <- c("1", "0"))
|
||||
data$am.factor <- factor(data$am, levels <- c("0", "1"))
|
||||
data$gear.factor <- factor(data$gear, levels <- c("3", "4", "5"))
|
||||
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$grouping_complete.factor = factor(data$grouping_complete, levels =
|
||||
data$choice.factor <-
|
||||
factor(data$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$sale_complete.factor = factor(data$sale_complete, levels = c("0", "1", "2"))
|
||||
data$color.factor <-
|
||||
factor(data$color, levels <- c("1", "2", "3", "4"))
|
||||
data$sale_complete.factor <-
|
||||
factor(data$sale_complete, levels <- c("0", "1", "2"))
|
||||
|
||||
levels(data$redcap_repeat_instrument.factor) = c("Sale")
|
||||
levels(data$cyl.factor) = c("3", "4", "5", "6", "7", "8")
|
||||
levels(data$vs.factor) = c("Yes", "No")
|
||||
levels(data$am.factor) = c("Automatic", "Manual")
|
||||
levels(data$gear.factor) = c("3", "4", "5")
|
||||
levels(data$carb.factor) = c("1", "2", "3", "4", "5", "6", "7", "8")
|
||||
levels(data$color_available___red.factor) = c("Unchecked", "Checked")
|
||||
levels(data$color_available___green.factor) = c("Unchecked", "Checked")
|
||||
levels(data$color_available___blue.factor) = c("Unchecked", "Checked")
|
||||
levels(data$color_available___black.factor) = c("Unchecked", "Checked")
|
||||
levels(data$motor_trend_cars_complete.factor) = c("Incomplete", "Unverified", "Complete")
|
||||
levels(data$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")
|
||||
levels(data$redcap_repeat_instrument.factor) <- c("Sale")
|
||||
levels(data$cyl.factor) <- c("3", "4", "5", "6", "7", "8")
|
||||
levels(data$vs.factor) <- c("Yes", "No")
|
||||
levels(data$am.factor) <- c("Automatic", "Manual")
|
||||
levels(data$gear.factor) <- c("3", "4", "5")
|
||||
levels(data$carb.factor) <-
|
||||
c("1", "2", "3", "4", "5", "6", "7", "8")
|
||||
levels(data$color_available___red.factor) <-
|
||||
c("Unchecked", "Checked")
|
||||
levels(data$color_available___green.factor) <-
|
||||
c("Unchecked", "Checked")
|
||||
levels(data$color_available___blue.factor) <-
|
||||
c("Unchecked", "Checked")
|
||||
levels(data$color_available___black.factor) <-
|
||||
c("Unchecked", "Checked")
|
||||
levels(data$motor_trend_cars_complete.factor) <-
|
||||
c("Incomplete", "Unverified", "Complete")
|
||||
levels(data$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
|
||||
}
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
@ -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)
|
||||
@ -18,9 +20,11 @@ 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)
|
||||
|
||||
@ -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(names(redcap_output_readr), names(redcap_output_csv1))
|
||||
expect_identical(length(redcap_output_readr),
|
||||
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)) {
|
||||
})
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
|
||||
|
||||
|
||||
# Global variables --------------------------------------------------------
|
||||
|
||||
# Cars
|
||||
@ -12,9 +13,10 @@ 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
|
||||
)
|
||||
|
||||
@ -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)
|
||||
)))
|
||||
|
||||
})
|
||||
|
||||
@ -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)
|
||||
|
||||
@ -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)
|
||||
|
||||
@ -1,19 +1,34 @@
|
||||
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)),
|
||||
data.frame(record_id = c(1,2), redcap_event_name = c("baseline", "baseline"), gender = c("male", "female")))
|
||||
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)
|
||||
),
|
||||
data.frame(
|
||||
record_id = c(1, 2),
|
||||
redcap_event_name = c("baseline", "baseline"),
|
||||
gender = c("male", "female")
|
||||
)
|
||||
)
|
||||
|
||||
expect_equal(redcap_wider(list),
|
||||
data.frame(record_id = c(1,2),
|
||||
expect_equal(
|
||||
redcap_wider(list),
|
||||
data.frame(
|
||||
record_id = c(1, 2),
|
||||
age_baseline = c(25, 26),
|
||||
age_followup = c(27, 28),
|
||||
gender = c("male","female")))
|
||||
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
|
||||
@ -29,5 +44,3 @@ wide_ds <- redcap_wider(list)
|
||||
test_that("redcap_wider() returns wide output from CSV", {
|
||||
expect_equal(ncol(wide_ds), 171)
|
||||
})
|
||||
|
||||
|
||||
|
||||
2
vignettes/.gitignore
vendored
Normal file
2
vignettes/.gitignore
vendored
Normal file
@ -0,0 +1,2 @@
|
||||
*.html
|
||||
*.R
|
||||
76
vignettes/Introduction.Rmd
Normal file
76
vignettes/Introduction.Rmd
Normal file
@ -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()
|
||||
```
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user