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87 lines
2.5 KiB
R
87 lines
2.5 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/REDCap_split.r
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\name{REDCap_split}
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\alias{REDCap_split}
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\title{Split REDCap repeating instruments table into multiple tables}
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\usage{
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REDCap_split(records, metadata, primary_table_label = "")
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}
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\arguments{
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\item{records}{Exported project records. May be a \code{data.frame},
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\code{response}, or \code{character} vector containing JSON from an API
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call.}
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\item{metadata}{Project metadata (the data dictionary). May be a
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\code{data.frame}, \code{response}, or \code{character} vector containing
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JSON from an API call.}
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\item{primary_table_label}{Name of the label given to the list element for
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the primary output table (as described in *README.md*).}
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}
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\value{
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A list of \code{"data.frame"}s: one base table and zero or more
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tables for each repeating instrument.
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}
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\description{
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This will take output from a REDCap export and split it into a base table
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and child tables for each repeating instrument. Metadata
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is used to determine which fields should be included in each resultant table.
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}
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\examples{
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\dontrun{
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# Using an API call -------------------------------------------------------
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library(RCurl)
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# Get the records
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records <- postForm(
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uri = api_url, # Supply your site-specific URI
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token = api_token, # Supply your own API token
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content = 'record',
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format = 'json',
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returnFormat = 'json'
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)
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# Get the metadata
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metadata <- postForm(
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uri = api_url, # Supply your site-specific URI
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token = api_token, # Supply your own API token
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content = 'metadata',
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format = 'json'
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)
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# Convert exported JSON strings into a list of data.frames
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REDCapRITS::REDCap_split(records, metadata)
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# Using a raw data export -------------------------------------------------
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# Get the records
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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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# 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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setwd("/path/to/data/")
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# Run the data export script supplied by REDCap.
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# This will create a data.frame of your records called 'data'
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source("ExampleProject_R_2018-06-03_1700.r")
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# Get the metadata
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metadata <- read.csv("ExampleProject_DataDictionary_2018-06-03.csv")
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# Split the tables
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REDCapRITS::REDCap_split(data, metadata)
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}
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}
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\author{
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Paul W. Egeler, M.S., GStat
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}
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