Journal of Statistical Software (Jul 2022)

On the Programmatic Generation of Reproducible Documents

  • Michael Kane,
  • Xun (Tony) Jiang,
  • Simon Urbanek

DOI
https://doi.org/10.18637/jss.v103.i08
Journal volume & issue
Vol. 103
pp. 1 – 15

Abstract

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Reproducible document standards, like R Markdown, facilitate the programmatic creation of documents whose content is itself programmatically generated. While programmatic content alone may not be sufficient for a rendered document since it does not include prose (content generated by an author to provide context, a narrative, etc.) programmatic generation can provide substantial efficiencies for structuring and constructing documents. This paper explores the programmatic generation of reproducible documents by distinguishing components that can be created by computational means from those requiring human-generation, providing guidelines for the generation of these documents, and identifying a use case in clinical trial reporting. These concepts and use case are illustrated through the listdown package for the R programming environment, which is is currently available on the Comprehensive R Archive Network.

Keywords