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Thursday, July 6 • 11:36am - 11:54am
codebookr: Codebooks in *R*

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Keywords: code book, data dictionary, data cleaning, validation, automation
Webpages: https://github.com/petebaker/codebookr, https://github.com/ropensci/auunconf/issues/46
codebookr is an R package under development to automate cleaning, checking and formatting data using metadata from Codebooks or Data Dictionaries. It is primarily aimed at epidemiological research and medical studies but can be easily used in other research areas.
Researchers collecting primary, secondary or tertiary data from RCTs or government and hospital administrative systems often have different data documentation and data cleaning needs to those scraping data off the web or collecting in-house data for business analytics. However, all studies will benefit from using codebooks which comprehensively document all study variables including derived variables. Codebooks document data formats, variable names, variable labels, factor levels, valid ranges for continuous variables, details of measuring instruments and so on.
For statistical consultants, each new data set has a new codebook. While statisticians may get a photocopied codebook or pdf, my preference is a spreadsheet so that the metadata can be used directly. Many data analysts are happy to use this metadata to code syntax to read, clean and check data. I prefer to automate this process by reading the codebook into R and then using the metadata directly for data checking, cleaning, factor level definitions.
While there is considerable interest in the data wrangling and cleaning (Jonge and Loo 2013; Wickham 2014; Fischetti 2017), there appear to be few tools available to read codebooks (see http://jason.bryer.org/posts/2013-01-10/Function_for_Reading_Codebooks_in_R.html) and even less to automatically apply the metadata to datasets.
We outline the fundamentals of codebookr and demonstrate it’s use on examples of research projects undertaken at University of Queensland’s School of Public Health.
References Fischetti, Tony. 2017. Assertr: Assertive Programming for R Analysis Pipelines. https://CRAN.R-project.org/package=assertr.

Jonge, Edwin de, and Mark van der Loo. 2013. “An Introduction to Data Cleaning with R.” Technical Report 201313. Statistics Netherlands. http://cran.vinastat.com/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf.

Wickham, Hadley. 2014. “Tidy Data.” The Journal of Statistical Software 59 (10). http://www.jstatsoft.org/v59/i10/.


Thursday July 6, 2017 11:36am - 11:54am
2.02 Wild Gallery

Attendees (186)