2018-01-19 17:50:08 +01:00
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REDCap Repeating Instrument Table Splitter
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2018-01-19 17:58:47 +01:00
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Paul W. Egeler, M.S., GStat
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Spectrum Health Office of Research Administration
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2018-01-19 17:50:08 +01:00
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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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## Instructions
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### SAS
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1. Run the macro definition in the source editor or using `%include`.
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2. Run the SAS code provided by REDCap to import the data BUT COMMENT
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THIS LINE:
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2018-01-19 17:52:09 +01:00
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```format redcap_repeat_instrument redcap_repeat_instrument_.;```
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2018-01-19 17:50:08 +01:00
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3. Open the data dictionary in MS Excel. We will need to do some pre-
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processing to the data dictionary file before reading it in because
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some of the user entry points (such as **Field Label**) allows for newline
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characters, which can break our data ingestion. MS Excel will read in
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the newline characters correctly.
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- Copy the first four columns and paste into a new sheet.
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- Save the new sheet as a .csv file.
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- Close the file.
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4. Call the macro, adjusting parameters as needed.
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### R
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The function definition file contains an example to assist you.
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1. Run the function definition in the source editor or using `source()`.
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2. Download the record dataset and metadata and import them. This can
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be accomplished either by traditional methods or using the API. The
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`read.csv()` function should be able to handle newline characters within
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records, so no pre-processing of metadata csv is needed.
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3. Call the function, pointing it to your record dataset and metadata
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`data.frame`s.
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