Automated Reproducibility Testing in R Markdown

Abstract

Computational results are considered _reproducible_ if the same computation on the same data yields the same results if performed on a different computer or on the same computer later in time. Reproducibility is a prerequisite for replicable, robust and transparent research in digital environments. Various approaches have been suggested to increase chances of reproducibility. Many of them rely on R Markdown as a language to dynamically generate reproducible research assets (e.g., reports, posters, or presentations). However, a simple way to detect non-reproducibility, that is, unwanted changes in these assets over time is still missing. We introduce the R package `reproducibleRchunks`, which provides a new type of code chunk in R Markdown documents, which automatically stores meta data about original computational results and verifies later reproduction attempts. With a minimal change to users' workflows, we hope that this approach increases transparency and trustworthiness of digital research assets