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Merge pull request #14 from SpectrumHealthResearch/issue12
REDCap_split() now compatible with tibbles
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commit
c0c74e67d1
@ -1,6 +1,6 @@
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Package: REDCapRITS
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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
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Version: 0.2.0
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Version: 0.2.1
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Authors@R: c(
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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("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("Spectrum Health, Grand Rapids, MI", role = "cph"))
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@ -14,7 +14,8 @@ Suggests:
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httr,
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httr,
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jsonlite,
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jsonlite,
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testthat,
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testthat,
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Hmisc
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Hmisc,
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readr
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License: GPL-3
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License: GPL-3
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Encoding: UTF-8
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Encoding: UTF-8
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LazyData: true
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LazyData: true
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@ -1,4 +1,8 @@
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# REDCapRITS 0.2.0 (Release date: 2019-??-??)
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# REDCapRITS 0.2.1 (Release date: 2019-07-26)
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* [bug] Can now accept `"tbl_df"` objects and `NA`s in the `redcap_repeat_instrument` field. (#12)
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# REDCapRITS 0.2.0 (Release date: 2019-07-08)
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* [feature] User can now separate each form into its own data.frame, regardless if it is a repeating instrument or not. (#10)
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* [feature] User can now separate each form into its own data.frame, regardless if it is a repeating instrument or not. (#10)
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* [bug] Handles auto-generated form timestamp fields.
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* [bug] Handles auto-generated form timestamp fields.
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@ -87,7 +87,7 @@ REDCap_split <- function(records,
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# Process user input
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# Process user input
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records <- process_user_input(records)
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records <- process_user_input(records)
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metadata <- process_user_input(metadata)
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metadata <- 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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# Get the variable names in the dataset
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vars_in_data <- names(records)
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vars_in_data <- names(records)
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@ -100,6 +100,15 @@ REDCap_split <- function(records,
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stop("There are no repeating instruments in this dataset.")
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stop("There are no repeating instruments in this dataset.")
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}
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}
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# Remove NAs from `redcap_repeat_instrument` (see issue #12)
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if(any(is.na(records$redcap_repeat_instrument))) {
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records$redcap_repeat_instrument <- ifelse(
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is.na(records$redcap_repeat_instrument),
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"",
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as.character(records$redcap_repeat_instrument)
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)
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}
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# Standardize variable names for metadata
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# Standardize variable names for metadata
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names(metadata) <- metadata_names
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names(metadata) <- metadata_names
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@ -13,10 +13,10 @@ records <- read.csv(
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)
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)
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)
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)
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redcap_output_csv1 <- REDCap_split(records, metadata)
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# Test that basic CSV export matches reference ------------------------------
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# Test that basic CSV export matches reference ------------------------------
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test_that("CSV export matches reference", {
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test_that("CSV export matches reference", {
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redcap_output_csv1 <- REDCap_split(records, metadata)
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expect_known_hash(redcap_output_csv1, "f74558d1939c17d9ff0e08a19b956e26")
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expect_known_hash(redcap_output_csv1, "f74558d1939c17d9ff0e08a19b956e26")
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})
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})
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@ -29,3 +29,42 @@ if (requireNamespace("Hmisc", quietly = TRUE)) {
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})
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})
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}
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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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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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redcap_output_readr <- REDCap_split(records, metadata)
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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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}
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test_that("Result of data read in with `readr` will match result with `read.csv`", {
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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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# Each element of the list
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expect_matching_elements(names)
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expect_matching_elements(dim)
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})
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}
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