Split REDCap repeating instruments table into multiple tables

This will take output from a REDCap export and split it into a base table and child tables for each repeating instrument. Metadata is used to determine which fields should be included in each resultant table.

Usage

REDCap_split(
  records,
  metadata,
  primary_table_name = "",
  forms = c("repeating", "all")
)

Arguments

records: Exported project records. May be a data.frame, response, or character vector containing JSON from an API call.
metadata: Project metadata (the data dictionary). May be a data.frame, response, or character vector containing JSON from an API call.
primary_table_name: Name given to the list element for the primary output table (as described in README.md). Ignored if forms = 'all'.
forms: Indicate whether to create separate tables for repeating instruments only or for all forms.

Value

A list of "data.frame"s. The number of tables will differ depending on the forms option selected.

'repeating': one base table and one or more tables for each repeating instrument.
'all': a data.frame for each instrument, regardless of whether it is a repeating instrument or not.

Author

Paul W. Egeler, M.S., GStat

Examples

if (FALSE) {
# Using an API call -------------------------------------------------------

library(RCurl)

# Get the records
records <- postForm(
  uri = api_url, # Supply your site-specific URI
  token = api_token, # Supply your own API token
  content = "record",
  format = "json",
  returnFormat = "json"
)

# Get the metadata
metadata <- postForm(
  uri = api_url, # Supply your site-specific URI
  token = api_token, # Supply your own API token
  content = "metadata",
  format = "json"
)

# Convert exported JSON strings into a list of data.frames
REDCapRITS::REDCap_split(records, metadata)

# Using a raw data export -------------------------------------------------

# Get the records
records <- read.csv("/path/to/data/ExampleProject_DATA_2018-06-03_1700.csv")

# Get the metadata
metadata <- read.csv(
  "/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv"
)

# Split the tables
REDCapRITS::REDCap_split(records, metadata)

# In conjunction with the R export script ---------------------------------

# You must set the working directory first since the REDCap data export
# script contains relative file references.
old <- getwd()
setwd("/path/to/data/")

# Run the data export script supplied by REDCap.
# This will create a data.frame of your records called 'data'
source("ExampleProject_R_2018-06-03_1700.r")

# Get the metadatan
metadata <- read.csv("ExampleProject_DataDictionary_2018-06-03.csv")

# Split the tables
REDCapRITS::REDCap_split(data, metadata)
setwd(old)
}