A Reproducible Data Analysis Workflow With R Markdown, Git, Make, and Docker

Abstract: In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible. While the resea.... https://qcmb.psychopen.eu/index.php/qcmb/article/view/3763

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: A Reproducible Data Analysis Workflow With R Markdown, Git, Make, and Docker ; volume:1 ; number:1 ; day:11 ; month:05 ; year:2021 ; Quantitative and computational methods in behavioral sciences ; 1, Heft 1 (11.05.2021)

Contributors, Places and Time

• Creator: Peikert, Aaron ; Brandmaier, Andreas M.

Further information

• DOI: 10.5964/qcmb.3763
• URN: urn:nbn:de:101:1-2021072405134263717595
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:51 AM CEST