REDCap Repeating Instrument Table Splitter
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
R Adding change directory directive to CI because R pkg is a subdir Jun 28, 2018
SAS Closes #3. SAS now handles instrument status fields Jun 5, 2018
test-data/test_splitr Working on #7. Appears to be a functioning patch Jun 23, 2018
.gitignore Introducing framework for testthat tests Jun 25, 2018
.travis.yml
LICENSE Initial commit May 22, 2018
README.md Updating Appveyor CI link in README.md Oct 19, 2018
appveyor.yml Changing .Rbuildignore to remove NOTE form Appveyor build Oct 15, 2018

README.md

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.

The normalized data can be retrieved by downloading repeating instruments individually then doing a little data munging or by writing a few custom parameters in a series of API calls (then doing more data munging), but this is a lot of extra steps that can make reproducible research more difficult.

REDCapRITS is a programmatic solution to handle the problem in both SAS and R.

Illustration

For example, consider this mocked-up data exported from a REDCap project with repeating instruments. The data contains information on a subset of cars in R's built-in mtcars dataset [1]. Within the table there is also a repeating instrument, sales, which has sales transaction data for some of those cars.

car_id redcap_repeat_instrument redcap_repeat_instance make model mpg cyl motor_trend_cars_complete price color customer sale_complete
1 AMC Javelin 15.2 8 1
1 sale 1 12000.50 1 Bob 0
1 sale 2 13750.77 3 Sue 2
1 sale 3 15004.57 2 Kim 0
2 Cadillac Fleetwood 10.4 8 0
3 Camaro Z28 13.3 8 0
3 sale 1 7800.00 2 Janice 2
3 sale 2 8000.00 3 Tim 0
4 Chrysler Imperial 14.7 8 0
4 sale 1 7500.00 1 Jim 2
5 Datsun 710 22.8 4 0
6 Dodge Challenger 15.5 8 0
7 Duster 360 14.3 8 0
7 sale 1 8756.40 4 Sarah 1
7 sale 2 6800.88 2 Pablo 0
7 sale 3 8888.88 1 Erica 0
7 sale 4 970.00 4 Juan 0
8 Ferrari Dino 19.7 6 0
9 Mazda RX4 Wag 21 6 0
10 Merc 230 22.8 4 0
10 sale 1 7800.98 2 Ted 0
10 sale 2 7954.00 1 Quentin 0
10 sale 3 6800.55 3 Sharon 2

You can see that the data from the non-repeating form (primary table) is interlaced with the data in the repeating form, creating a checkerboard pattern. In order to do analysis, the data must be normalized and then the tables rejoined. Normalization would result in two tables: 1) a primary table and 2) a sale table. The normalized tables would look like this:

Primary table

car_id make model mpg cyl motor_trend_cars_complete
1 AMC Javelin 15.2 8 1
2 Cadillac Fleetwood 10.4 8 0
3 Camaro Z28 13.3 8 0
4 Chrysler Imperial 14.7 8 0
5 Datsun 710 22.8 4 0
6 Dodge Challenger 15.5 8 0
7 Duster 360 14.3 8 0
8 Ferrari Dino 19.7 6 0
9 Mazda RX4 Wag 21 6 0
10 Merc 230 22.8 4 0

Sale table

car_id redcap_repeat_instrument redcap_repeat_instance price color customer sale_complete
1 sale 1 12000.50 1 Bob 0
1 sale 2 13750.77 3 Sue 2
1 sale 3 15004.57 2 Kim 0
3 sale 1 7800.00 2 Janice 2
3 sale 2 8000.00 3 Tim 0
4 sale 1 7500.00 1 Jim 2
7 sale 1 8756.40 4 Sarah 1
7 sale 2 6800.88 2 Pablo 0
7 sale 3 8888.88 1 Erica 0
7 sale 4 970.00 4 Juan 0
10 sale 1 7800.98 2 Ted 0
10 sale 2 7954.00 1 Quentin 0
10 sale 3 6800.55 3 Sharon 2

Suppose you would like to do some analysis such as sale price by make of car or find the most popular color for each model. To do so, you can join the tables together with relational algebra. After inner joining the primary table to the sale table on car_id and selecting only the fields you are interested in, your resulting analytic dataset might look something like this:

car_id make model price color customer
1 AMC Javelin 12000.50 1 Bob
1 AMC Javelin 13750.77 3 Sue
1 AMC Javelin 15004.57 2 Kim
3 Camaro Z28 7800.00 2 Janice
3 Camaro Z28 8000.00 3 Tim
4 Chrysler Imperial 7500.00 1 Jim
7 Duster 360 8756.40 4 Sarah
7 Duster 360 6800.88 2 Pablo
7 Duster 360 8888.88 1 Erica
7 Duster 360 970.00 4 Juan
10 Merc 230 7800.98 2 Ted
10 Merc 230 7954.00 1 Quentin
10 Merc 230 6800.55 3 Sharon

Such a join can be accomplished numerous ways. Just to name a few:

Supported Platforms

Currently, the R and SAS code is well-tested with mocked-up data.

  • R
  • SAS

I have made some effort to replicate the messiness of real-world data and have tried to include as many special cases and data types as possible. However, this code may not account for all contingencies or changes in the native REDCap export format. If you find a bug, please feel free to open an issue or pull request.

Coming Soon

Currently, we have given some consideration to expand the capabilities into the following languages.

  • Python
  • VBA

If you have some talents in these or other languages, please feel free to open a pull request! We welcome your contributions!

Instructions

R

Travis-CI Build Status AppVeyor Build Status

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 (data dictionary). This can be accomplished by several methods:
    • Using the API. Check with your REDCap administrator for details.
    • Exporting the data from the web interface by selecting CSV / Microsoft Excel (raw data).
    • Exporting the data from the web interface by selecting R Statistical Software. If you use this method, you may run the R script supplied by REDCap prior to splitting the data.
    • Do NOT export from the web interface with the CSV / Microsoft Excel (labels) option. This will not work with REDCapRITS.
  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,     # 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)

And here is an example of usage when downloading a REDCap export of the raw data (not labelled!) 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)

REDCapRITS also works with the data export script (a.k.a., syntax file) supplied by REDCap. Here is an example of its usage:

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

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

# Split the tables
REDCapRITS::REDCap_split(data, 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.

Examples

Please follow the instructions from REDCap on importing the data into SAS. REDCap provides the data in a csv format as well as bat and sas files. The instructions are available when exporting the data from the REDCap web interface. If you do not use the pathway mapper (bat file) provided, you will need to go into the sas file provided by REDCap and alter the file path in the infile statment (Line 2).

* Run the program to import the data file into a SAS dataset;
%INCLUDE "c:\path\to\data\ExampleProject_SAS_2018-06-04_0950.sas";

* Run the MACRO definitions from this repo;
%INCLUDE "c:\path\to\macro\REDCap_split.sas";

* Read in the data dictionary;
%REDCAP_READ_DATA_DICT(c:\path\to\data\ExampleProject_DataDictionary_2018-06-04.csv);

* Split the tables;
%REDCAP_SPLIT();

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 [2]. 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] Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391--411. Modified with fake data for the purpose of illustration

[2] 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.