About

2Rmarkdown

A small set of converters that take Stata, JAMOVI, JASP, and SPSS files and produce reproducible R Markdown reports. Built for open-science workflows: replications, audits, teaching, and archiving published analyses in a readable, runnable form.

What problem this addresses

A lot of empirical work lives inside point-and-click or single-vendor software, which makes it hard to re-run, share, or extend. Translating an analysis to R by hand is doable but slow and error-prone. 2Rmarkdown does the obvious mechanical part of that translation — parsing the source file, mapping commands or analysis specs to R, and assembling an R Markdown document — so you can spend your time on the parts that actually need judgment.

What this is not

Not a numerical oracle. The generated R code mirrors the original analysis in intent. Differences can still appear at the edges (rounding, ties, default options, version drift). Always sanity-check.
Not a substitute for understanding the analysis. The output is a starting point you should read, edit, and adapt — not a black box to publish unchanged.
Not feature-complete.Each converter covers the commands or analysis types we've tested against real scripts. Niche or recent options may not be supported yet. Issues and pull requests are welcome.

How it's built

The web app is a thin shell around five open-source R packages (one per source format, plus a shared core). All five are MIT licensed and installable from GitHub. The web app uploads your file to an isolated worker container, calls the relevant package, knits the resulting Rmd to HTML, and returns both files. Uploads are deleted after the run; only minimal job metadata (format, timestamp, success/failure) is retained for rate limiting and debugging.

Author & contact

Built and maintained by Gilad Feldman (Department of Psychology, University of Hong Kong). For bug reports, feature requests, or research collaborations:

GitHub: github.com/giladfeldman
Email: giladfel@gmail.com

Citation

If 2Rmarkdown or any of its underlying R packages helped your work, please cite the relevant GitHub repository directly. A formal citation entry will be added once a companion paper is posted.