REDCapCAST/R/man/REDCap_split.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/REDCap_split.r
\name{REDCap_split}
\alias{REDCap_split}
\title{Split REDCap repeating instruments table into multiple tables}
\usage{
REDCap_split(records, metadata)
}
\arguments{
\item{records}{Exported project records. May be a \code{data.frame},
\code{response}, or \code{character} vector containing JSON from an API
call.}

\item{metadata}{Project metadata (the data dictionary). May be a
\code{data.frame}, \code{response}, or \code{character} vector containing
JSON from an API call.}
}
\value{
A list of \code{"data.frame"}s: one base table and zero or more
  tables for each repeating instrument.
}
\description{
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.
}
\examples{
\dontrun{
# 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.
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 metadata
metadata <- read.csv("ExampleProject_DataDictionary_2018-06-03.csv")

# Split the tables
REDCapRITS::REDCap_split(data, metadata)
}
}
\author{
Paul W. Egeler, M.S., GStat
}