R Markdown. (27th February 2015)

• Record Type: Journal Article
• Authors: Baumer, Benjamin; Udwin, Dana
• Is Part Of: Wiley interdisciplinary reviews. Volume 7:Number 3(2015)
• Page Start: 167
• Page End: 177
• Publication Date: 2015-02-27
• Subjects: computing -- programming -- R Markdown -- reproducible research; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1348
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract : Reproducibility is increasingly important to statistical research, but many details are often omitted from the published version of complex statistical analyses. A reader's comprehension is limited to what the author concludes, without exposure to the computational process. Often, the industrious reader cannot expand upon or validate the author's results. Even the author may struggle to reproduce their own results upon revisiting them. R Markdown is an authoring syntax that combines the ease of Markdown with the statistical programming language R. An R Markdown document or presentation interweaves computation, output and written analysis to the effect of transparency, clarity and an inherent invitation to reproduce (especially as sharing data is now as easy as the click of a button). It is an open‐source tool that can be used either on its own or through the RStudio integrated development environment (IDE). In addition to facilitating reproducible research, R Markdown is a boon to collaboratively minded data analysts, whose workflow can be streamlined by sharing only one master document that contains both code and content. Statistics educators may also find that R Markdown is helpful as a homework template, for both ease‐of‐use and in discouraging students from copy‐and‐pasting results from classmates. Training students in R Markdown will introduce to the workforce a new class of data analysts with an ingrained, foundational inclination toward reproducible research. WIREs Comput Stat 2015, 7:167–177. doi: 10.1002/wics.1348 This article is categorized under: Software for Computational Statistics > Software/Statistical Software