REDCap Repeating Instrument Table Splitter

Paul W. Egeler, M.S., GStat
Spectrum Health Office of Research Administration
13 July 2017

Description

So the new buzz in the REDCap world seems to be Repeating Instruments and Events. Certainly there is potential for a lot of utility in this feature and I was excited to try it out. I know I will be using this feature a lot in the future.

Unfortunately, I was not very happy with the way the data was exported either via CSV or API call. When you conceptualize the data model for a Repeating Instrument, you probably think of a multi-table model. You might expect that the non-repeating instruments may constitute one table that would be related to Repeating Instruments tables via a one-to-many relationship. In reality, the data is outputted as one table with all possible fields; this has the effect of nesting the output table in a way that is not useful in most analysis software. Therefore, I have made a solution to handle the problem in both SAS and R.

Supported Platforms

• R
• SAS

Coming Soon

• Python
• VBA

Instructions

R

Installation

First you must install the package. To do so, execute the following in your R console:

if (!require(devtools)) install.packages("devtools")
devtools::install_github("SpectrumHealthResearch/REDCapRITS/R")

Usage

After the package is installed, follow these instructions:

1. Download the record dataset and metadata. This can be accomplished either by traditional methods or using the API.
2. Call the function, pointing it to your record dataset and metadata data.frames or JSON character vectors. You may need to load the package via library() or require().

Examples

Here is an example usage in conjuction with an API call to your REDCap instance:

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,
    token = api_token,
    content = 'metadata',
    format = 'json'
)

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

And here is an example of usage when downloading a REDCap export manually from your REDCap web interface:

# 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)

SAS

1. Download the data, SAS code to load the data, and the data dictionary from REDCap.
2. Run the SAS code provided by REDCap to import the data.
3. Run the RECapRITS macro definitions in the source editor or using %include.
4. Run the macro call %REDCAP_READ_DATA_DICT() to load the data dictionary into your SAS session, pointing to the file location of your REDCap data dictionary.
5. Run the macro call %REDCAP_SPLIT(). You will have an output dataset for your main table as well as for each repeating instrument.

Issues

Suggestions and contributions are more than welcome! Please feel free to create an issue or pull request.

About REDCap

This code was written for REDCap electronic data capture tools.^1^ Code for this project was tested on the REDCap instance hosted at Spectrum Health, Grand Rapids, MI. REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources.

References

^1^Paul A. Harris, Robert Taylor, Robert Thielke, Jonathon Payne, Nathaniel Gonzalez, Jose G. Conde, Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42(2):377-81.