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.

Instructions

SAS

1. Run the macro definition in the source editor or using %include.
2. Run the SAS code provided by REDCap to import the data BUT COMMENT THIS LINE: format redcap_repeat_instrument redcap_repeat_instrument_.;
3. Open the data dictionary in MS Excel. We will need to do some pre- processing to the data dictionary file before reading it in because some of the user entry points (such as Field Label) allows for newline characters, which can break our data ingestion. MS Excel will read in the newline characters correctly.
   • Copy the first four columns and paste into a new sheet.
   • Save the new sheet as a .csv file.
   • Close the file.
4. Call the macro, adjusting parameters as needed.

R

The function definition file contains an example to assist you.

1. Run the function definition in the source editor or using source().
2. Download the record dataset and metadata and import them. This can be accomplished either by traditional methods or using the API. The read.csv() function should be able to handle newline characters within records, so no pre-processing of metadata csv is needed.
3. Call the function, pointing it to your record dataset and metadata data.frames.