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122 lines
4.3 KiB
Markdown
122 lines
4.3 KiB
Markdown
REDCap Repeating Instrument Table Splitter
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===========================================
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Paul W. Egeler, M.S., GStat
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Spectrum Health Office of Research Administration
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13 July 2017
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## Description
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So the new buzz in the REDCap world seems to be Repeating Instruments
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and Events. Certainly there is potential for a lot of utility in this
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feature and I was excited to try it out. I know I will be using this
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feature a lot in the future.
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Unfortunately, I was not very happy with the way the data was exported
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either via CSV or API call. When you conceptualize the data model for
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a Repeating Instrument, you probably think of a multi-table model. You
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might expect that the non-repeating instruments may constitute one table
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that would be related to Repeating Instruments tables via a one-to-many
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relationship. In reality, the data is outputted as one table with all
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possible fields; this has the effect of nesting the output table in a
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way that is not useful in most analysis software. Therefore, I have made
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a solution to handle the problem in both SAS and R.
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## Supported Platforms
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- R
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- SAS
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### Coming Soon
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- Python
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- VBA
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## Instructions
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### R
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#### Installation
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First you must install the package. To do so, execute the following in your R console:
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```r
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if (!require(devtools)) install.packages("devtools")
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devtools::install_github("SpectrumHealthResearch/REDCapRITS/R")
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```
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#### Usage
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After the package is installed, follow these instructions:
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1. Download the record dataset and metadata. This can
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be accomplished either by traditional methods or using the API.
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1. Call the function, pointing it to your record dataset and metadata
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`data.frame`s or JSON character vectors. You may need to load the package via
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`library()` or `require()`.
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#### Examples
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Here is an example usage in conjuction with an API call to your REDCap instance:
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```r
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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,
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token = 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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```
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And here is an example of usage when downloading a REDCap export manually from your REDCap web interface:
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```r
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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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```
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### SAS
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1. Download the data, SAS code to load the data, and the data dictionary from REDCap
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1. Run the SAS code provided by REDCap to import the data BUT COMMENT
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THIS LINE:
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```format redcap_repeat_instrument redcap_repeat_instrument_.;```
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1. Run the RECapRITS macro definitions in the source editor or using `%include`.
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1. 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.
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1. Run the macro call `%REDCAP_SPLIT()`. You will have an output dataset for
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your main table as well as for each repeating instrument.
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## Issues
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Suggestions and contributions are more than welcome! Please feel free to create an issue or pull request.
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## About REDCap
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This code was written for [REDCap electronic data capture tools](https://projectredcap.org/).^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.
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## References
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^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.
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