9  Reproducible Data Analysis Workflow

I recommend the tutorial, “A Reproducible Data Analysis Workflow With R Markdown, Git, Make, and Docker” as a starting point for R-based data analyses (Peikert and Brandmaier, n.d.).

I also recommend using the integrated development environment (IDE) for R, RStudio.

Component	Description
RMarkdown	Dynamic document generation “literate programming paradigm” Eliminate copy and paste errors knitr (export to other formats such documents, presentations) papaja (APA formatting of results in text) stargazer (journal ready tables)
Git	Version control tracking Commits = “snapshots” Commit Message = Description of update Repository = Collection of commits Hash = unique identifier Note RStudio has a GUI for Git.
Make	Dependency management
Docker	Containerization Virtual software environment Reproduce results independent of host operating system

Peikert, Aaron, and Andreas Markus Brandmaier. n.d. “A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker.” https://doi.org/10.31234/osf.io/8xzqy.