A Reproducible Data Analysis Workflow

In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The leverages established tools and practices from software engineering. It combines the benefits various open-source including R Markdown, Git, Make, Docker, whose interplay ensures seamless integration version management, dynamic report generation conforming journal styles, full cross-platform computational reproducibility. meeting primary goals that 1) reporting statistical results is consistent with actual (dynamic generation), 2) analysis exactly reproduces at later point in time even if computing platform or changed (computational reproducibility), 3) changes any (during development post-publication) are tracked, tagged, documented while earlier versions both code remain accessible. While research community increasingly recognizes document management as reproducibility, demonstrate practical examples these alone not sufficient Combining containerization, dependence generation, proposed increases scientific productivity by facilitating reuse data.