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AG Damsbo
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15
DESCRIPTION
15
DESCRIPTION
@ -1,13 +1,14 @@
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Package: REDCapRITS
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Title: REDCap Repeating Instrument Table Splitter
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Title: REDCap Repeating Instrument Table Splitter Fork
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Version: 0.2.2.1
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Authors@R: c(
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person("Paul", "Egeler", email = "paul.egeler@spectrumhealth.org", role = c("aut", "cre")),
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person("Spectrum Health, Grand Rapids, MI", role = "cph"),
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person("Andreas Gammelgaard", "Damsbo", , "agdamsbo@clin.au.dk", role = c("ctb"),
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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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Copyright: Spectrum Health, Grand Rapids, MI
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Description: Split REDCap repeating instruments output into multiple tables.
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Description: This is a fork of REDCapRITS by Paul Egeler and Spectrum Health. See
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[https://github.com/SpectrumHealthResearch/REDCapRITS](https://github.com/SpectrumHealthResearch/REDCapRITS).
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Split REDCap repeating instruments output into multiple tables.
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This will take raw output from a REDCap export and split it into a base table
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and child tables for each repeating instrument.
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REDCap (Research Electronic Data Capture) is a secure, web-based software
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@ -29,8 +30,8 @@ License: GPL-3
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Encoding: UTF-8
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LazyData: true
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RoxygenNote: 7.2.3
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URL: https://github.com/SpectrumHealthResearch/REDCapRITS
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BugReports: https://github.com/SpectrumHealthResearch/REDCapRITS/issues
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URL: https://github.com/agdamsbo/REDCapRITS
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BugReports: https://github.com/agdamsbo/REDCapRITS/issues
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Collate:
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'utils.r'
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'process_user_input.r'
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@ -82,12 +82,11 @@
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REDCap_split <- function(records,
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metadata,
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primary_table_name = "",
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forms = c("repeating", "all")
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) {
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forms = c("repeating", "all")) {
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# Process user input
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records <- process_user_input(records)
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metadata <- as.data.frame(process_user_input(metadata)) # See issue #12
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metadata <-
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as.data.frame(process_user_input(metadata)) # See issue #12
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# Get the variable names in the dataset
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vars_in_data <- names(records)
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@ -96,7 +95,8 @@ REDCap_split <- function(records,
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forms <- match.arg(forms)
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# Check to see if there were any repeating instruments
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if (forms == "repeating" && !"redcap_repeat_instrument" %in% vars_in_data) {
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if (forms == "repeating" &&
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!"redcap_repeat_instrument" %in% vars_in_data) {
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stop("There are no repeating instruments in this dataset.")
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}
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@ -113,7 +113,8 @@ REDCap_split <- function(records,
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names(metadata) <- metadata_names
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# Make sure that no metadata columns are factors
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metadata <- rapply(metadata, as.character, classes = "factor", how = "replace")
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metadata <-
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rapply(metadata, as.character, classes = "factor", how = "replace")
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# Find the fields and associated form
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fields <- match_fields_to_form(metadata, vars_in_data)
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@ -131,22 +132,23 @@ REDCap_split <- function(records,
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if ("redcap_repeat_instrument" %in% vars_in_data) {
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# Variables to be at the beginning of each repeating instrument
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repeat_instrument_fields <- grep(
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"^redcap_repeat.*",
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repeat_instrument_fields <- grep("^redcap_repeat.*",
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vars_in_data,
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value = TRUE
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)
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value = TRUE)
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# Identify the subtables in the data
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subtables <- unique(records$redcap_repeat_instrument)
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subtables <- subtables[subtables != ""]
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# Split the table based on instrument
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out <- split.data.frame(records, records$redcap_repeat_instrument)
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out <-
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split.data.frame(records, records$redcap_repeat_instrument)
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primary_table_index <- which(names(out) == "")
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if (forms == "repeating" && primary_table_name %in% subtables) {
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warning("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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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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)
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primary_table_name <- ""
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} else if (primary_table_name > "") {
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names(out)[[primary_table_index]] <- primary_table_name
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@ -154,26 +156,16 @@ REDCap_split <- function(records,
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# Delete the variables that are not relevant
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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(
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vars_in_data %in% c(
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universal_fields,
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fields[!fields[,2] %in% subtables, 1]
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)
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)
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out[[primary_table_index]] <- out[[primary_table_index]][out_fields]
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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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out[[primary_table_index]] <-
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out[[primary_table_index]][out_fields]
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} else {
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out_fields <- which(
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vars_in_data %in% c(
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universal_fields,
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out_fields <- which(vars_in_data %in% c(universal_fields,
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repeat_instrument_fields,
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fields[fields[,2] == i, 1]
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)
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)
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fields[fields[, 2] == i, 1]))
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out[[i]] <- out[[i]][out_fields]
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}
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@ -181,20 +173,14 @@ REDCap_split <- function(records,
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}
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if (forms == "all") {
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out <- c(
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split_non_repeating_forms(
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out[[primary_table_index]],
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out <- c(split_non_repeating_forms(out[[primary_table_index]],
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universal_fields,
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fields[!fields[,2] %in% subtables,]
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),
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out[-primary_table_index]
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)
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fields[!fields[, 2] %in% subtables, ]),
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out[-primary_table_index])
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}
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} else {
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out <- split_non_repeating_forms(records, universal_fields, fields)
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}
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@ -17,7 +17,6 @@ process_user_input.data.frame <- function(x, ...) {
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}
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process_user_input.character <- function(x, ...) {
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if (!requireNamespace("jsonlite", quietly = TRUE)) {
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stop(
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"The package 'jsonlite' is needed to convert ",
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@ -35,7 +34,6 @@ process_user_input.character <- function(x, ...) {
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}
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process_user_input.response <- function(x, ...) {
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process_user_input(rawToChar(x$content))
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}
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47
R/utils.r
47
R/utils.r
@ -1,9 +1,6 @@
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match_fields_to_form <- function(metadata, vars_in_data) {
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fields <- metadata[
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!metadata$field_type %in% c("descriptive", "checkbox"),
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c("field_name", "form_name")
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]
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fields <- metadata[!metadata$field_type %in% c("descriptive", "checkbox"),
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c("field_name", "form_name")]
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# Process instrument status fields
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form_names <- unique(metadata$form_name)
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@ -16,9 +13,9 @@ match_fields_to_form <- function(metadata, vars_in_data) {
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fields <- rbind(fields, form_complete_fields)
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# Process survey timestamps
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timestamps <- intersect(vars_in_data, paste0(form_names, "_timestamp"))
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timestamps <-
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intersect(vars_in_data, paste0(form_names, "_timestamp"))
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if (length(timestamps)) {
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timestamp_fields <- data.frame(
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field_name = timestamps,
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form_name = sub("_timestamp$", "", timestamps),
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@ -31,17 +28,12 @@ match_fields_to_form <- function(metadata, vars_in_data) {
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# Process checkbox fields
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if (any(metadata$field_type == "checkbox")) {
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checkbox_basenames <- metadata[
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metadata$field_type == "checkbox",
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c("field_name", "form_name")
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]
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checkbox_basenames <- metadata[metadata$field_type == "checkbox",
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c("field_name", "form_name")]
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checkbox_fields <-
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do.call(
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"rbind",
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apply(
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checkbox_basenames,
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do.call("rbind",
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apply(checkbox_basenames,
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1,
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function(x, y)
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data.frame(
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@ -50,9 +42,7 @@ match_fields_to_form <- function(metadata, vars_in_data) {
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stringsAsFactors = FALSE,
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row.names = NULL
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),
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y = vars_in_data
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)
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)
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y = vars_in_data))
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fields <- rbind(fields, checkbox_fields)
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@ -60,12 +50,9 @@ match_fields_to_form <- function(metadata, vars_in_data) {
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# Process ".*\\.factor" fields supplied by REDCap's export data R script
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if (any(grepl("\\.factor$", vars_in_data))) {
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factor_fields <-
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do.call(
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"rbind",
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apply(
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fields,
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do.call("rbind",
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apply(fields,
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1,
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function(x, y) {
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field_indices <- grepl(paste0("^", x[1], "\\.factor$"), y)
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@ -77,9 +64,7 @@ match_fields_to_form <- function(metadata, vars_in_data) {
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row.names = NULL
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)
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},
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y = vars_in_data
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)
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)
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y = vars_in_data))
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fields <- rbind(fields, factor_fields)
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@ -90,12 +75,11 @@ match_fields_to_form <- function(metadata, vars_in_data) {
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}
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split_non_repeating_forms <- function(table, universal_fields, fields) {
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split_non_repeating_forms <-
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function(table, universal_fields, fields) {
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forms <- unique(fields[[2]])
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x <- lapply(
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forms,
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x <- lapply(forms,
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function (x) {
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table[names(table) %in% union(universal_fields, fields[fields[, 2] == x, 1])]
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})
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@ -103,4 +87,3 @@ split_non_repeating_forms <- function(table, universal_fields, fields) {
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structure(x, names = forms)
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}
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@ -36,22 +36,32 @@ REDCap_process_csv <- function(data) {
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#Setting Factors(will create new variable for factors)
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data$redcap_repeat_instrument.factor = factor(data$redcap_repeat_instrument,levels=c("sale"))
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data$redcap_repeat_instrument.factor = factor(data$redcap_repeat_instrument, levels =
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c("sale"))
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data$cyl.factor = factor(data$cyl, levels = c("3", "4", "5", "6", "7", "8"))
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data$vs.factor = factor(data$vs, levels = c("1", "0"))
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data$am.factor = factor(data$am, levels = c("0", "1"))
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data$gear.factor = factor(data$gear, levels = c("3", "4", "5"))
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data$carb.factor = factor(data$carb, levels = c("1", "2", "3", "4", "5", "6", "7", "8"))
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data$color_available___red.factor = factor(data$color_available___red,levels=c("0","1"))
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data$color_available___green.factor = factor(data$color_available___green,levels=c("0","1"))
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data$color_available___blue.factor = factor(data$color_available___blue,levels=c("0","1"))
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data$color_available___black.factor = factor(data$color_available___black,levels=c("0","1"))
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data$motor_trend_cars_complete.factor = factor(data$motor_trend_cars_complete,levels=c("0","1","2"))
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data$letter_group___a.factor = factor(data$letter_group___a,levels=c("0","1"))
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data$letter_group___b.factor = factor(data$letter_group___b,levels=c("0","1"))
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data$letter_group___c.factor = factor(data$letter_group___c,levels=c("0","1"))
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data$color_available___red.factor = factor(data$color_available___red, levels =
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c("0", "1"))
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data$color_available___green.factor = factor(data$color_available___green, levels =
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c("0", "1"))
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data$color_available___blue.factor = factor(data$color_available___blue, levels =
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c("0", "1"))
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data$color_available___black.factor = factor(data$color_available___black, levels =
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c("0", "1"))
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data$motor_trend_cars_complete.factor = factor(data$motor_trend_cars_complete, levels =
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c("0", "1", "2"))
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data$letter_group___a.factor = factor(data$letter_group___a, levels =
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c("0", "1"))
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data$letter_group___b.factor = factor(data$letter_group___b, levels =
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c("0", "1"))
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data$letter_group___c.factor = factor(data$letter_group___c, levels =
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c("0", "1"))
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data$choice.factor = factor(data$choice, levels = c("choice1", "choice2"))
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data$grouping_complete.factor = factor(data$grouping_complete,levels=c("0","1","2"))
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data$grouping_complete.factor = factor(data$grouping_complete, levels =
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c("0", "1", "2"))
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data$color.factor = factor(data$color, levels = c("1", "2", "3", "4"))
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data$sale_complete.factor = factor(data$sale_complete, levels = c("0", "1", "2"))
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@ -6,4 +6,5 @@
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# )
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# }
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get_data_location <- function(x) file.path("data", x)
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get_data_location <- function(x)
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file.path("data", x)
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@ -1,26 +1,12 @@
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context("Reading in JSON")
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# Check the RCurl export ---------------------------------------------------
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test_that("JSON character vector from RCurl matches reference", {
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metadata <- jsonlite::fromJSON(get_data_location("ExampleProject_metadata.json"))
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metadata <- jsonlite::fromJSON(
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get_data_location(
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"ExampleProject_metadata.json"
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)
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)
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records <- jsonlite::fromJSON(
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get_data_location(
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"ExampleProject_records.json"
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)
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)
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records <- jsonlite::fromJSON(get_data_location("ExampleProject_records.json"))
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redcap_output_json1 <- REDCap_split(records, metadata)
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expect_known_hash(redcap_output_json1, "2c8b6531597182af1248f92124161e0c")
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})
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# Check the httr export ---------------------------------------------------
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# Something will go here.
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@ -1,4 +1,3 @@
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context("CSV Exports")
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# Set up the path and data -------------------------------------------------
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metadata <- read.csv(
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@ -6,10 +5,8 @@ metadata <- read.csv(
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stringsAsFactors = TRUE
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)
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records <- read.csv(
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get_data_location("ExampleProject_DATA_2018-06-07_1129.csv"),
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stringsAsFactors = TRUE
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)
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records <- read.csv(get_data_location("ExampleProject_DATA_2018-06-07_1129.csv"),
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stringsAsFactors = TRUE)
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redcap_output_csv1 <- REDCap_split(records, metadata)
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@ -21,7 +18,8 @@ test_that("CSV export matches reference", {
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# Test that R code enhanced CSV export matches reference --------------------
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if (requireNamespace("Hmisc", quietly = TRUE)) {
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test_that("R code enhanced export matches reference", {
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redcap_output_csv2 <- REDCap_split(REDCap_process_csv(records), metadata)
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redcap_output_csv2 <-
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REDCap_split(REDCap_process_csv(records), metadata)
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expect_known_hash(redcap_output_csv2, "34f82cab35bf8aae47d08cd96f743e6b")
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})
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@ -29,33 +27,22 @@ if (requireNamespace("Hmisc", quietly = TRUE)) {
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||||
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if (requireNamespace("readr", quietly = TRUE)) {
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context("Compatibility with readr")
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metadata <- readr::read_csv(
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get_data_location(
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"ExampleProject_DataDictionary_2018-06-07.csv"
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)
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)
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metadata <- readr::read_csv(get_data_location("ExampleProject_DataDictionary_2018-06-07.csv"))
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records <- readr::read_csv(
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get_data_location(
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"ExampleProject_DATA_2018-06-07_1129.csv"
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)
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)
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records <- readr::read_csv(get_data_location("ExampleProject_DATA_2018-06-07_1129.csv"))
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redcap_output_readr <- REDCap_split(records, metadata)
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|
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expect_matching_elements <- function(FUN) {
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FUN <- match.fun(FUN)
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expect_identical(
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lapply(redcap_output_readr, FUN),
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lapply(redcap_output_csv1, FUN)
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)
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expect_identical(lapply(redcap_output_readr, FUN),
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lapply(redcap_output_csv1, FUN))
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}
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||||
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test_that("Result of data read in with `readr` will match result with `read.csv`", {
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||||
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||||
test_that("Result of data read in with `readr` will match result with `read.csv`",
|
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{
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# The list itself
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expect_identical(length(redcap_output_readr), length(redcap_output_csv1))
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expect_identical(names(redcap_output_readr), names(redcap_output_csv1))
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@ -1,63 +1,57 @@
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context("Using the `forms = 'all'` argument")
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||||
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||||
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# Global variables --------------------------------------------------------
|
||||
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||||
# Cars
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||||
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_json <- REDCap_split(records, metadata, forms = "all")
|
||||
|
||||
# Longitudinal
|
||||
file_paths <- sapply(
|
||||
c(
|
||||
records = "WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv",
|
||||
metadata = "WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv"
|
||||
), get_data_location
|
||||
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_output_long <- with(redcap, REDCap_split(records, metadata, forms = "all"))
|
||||
redcap_output_long <-
|
||||
with(redcap, REDCap_split(records, metadata, forms = "all"))
|
||||
redcap_long_names <- names(redcap[[1]])
|
||||
|
||||
# Tests -------------------------------------------------------------------
|
||||
|
||||
test_that("Each form is an element in the list", {
|
||||
|
||||
expect_length(redcap_output_json, 3L)
|
||||
expect_identical(names(redcap_output_json), c("motor_trend_cars", "grouping", "sale"))
|
||||
expect_identical(names(redcap_output_json),
|
||||
c("motor_trend_cars", "grouping", "sale"))
|
||||
|
||||
})
|
||||
|
||||
test_that("All variables land somewhere", {
|
||||
|
||||
expect_true(setequal(names(records), Reduce("union", sapply(redcap_output_json, names))))
|
||||
expect_true(setequal(names(records), Reduce(
|
||||
"union", sapply(redcap_output_json, names)
|
||||
)))
|
||||
|
||||
})
|
||||
|
||||
|
||||
test_that("Primary table name is ignored", {
|
||||
expect_identical(
|
||||
REDCap_split(records, metadata, "HELLO", "all"),
|
||||
redcap_output_json
|
||||
)
|
||||
expect_identical(REDCap_split(records, metadata, "HELLO", "all"),
|
||||
redcap_output_json)
|
||||
})
|
||||
|
||||
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))))
|
||||
expect_true(setequal(redcap_long_names, Reduce(
|
||||
"union", sapply(redcap_output_long, names)
|
||||
)))
|
||||
|
||||
})
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
context("Longitudinal data")
|
||||
## "Longitudinal data"
|
||||
|
||||
test_that("CSV export matches reference", {
|
||||
file_paths <- sapply(
|
||||
|
||||
@ -1,28 +1,17 @@
|
||||
context("Primary table name processing")
|
||||
## "Primary table name processing"
|
||||
|
||||
|
||||
# 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"),
|
||||
"primary table"
|
||||
)
|
||||
redcap_output_json1 <- expect_warning(REDCap_split(records, metadata, "sale"),
|
||||
"primary table")
|
||||
|
||||
expect_known_hash(redcap_output_json1, ref_hash)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user