The workflowr R package helps researchers organize their analyses in a way that promotes effective project management, reproducibility, collaboration, and sharing of results. Workflowr combines literate programming (knitr and rmarkdown) and version control (Git, via git2r) to generate a website containing time-stamped, versioned, and documented results. Any R user can quickly and easily adopt workflowr.

For more details, see the online documentation. For an example, see the Divvy data exploration project. To keep up-to-date with the latest workflowr developments, please join the workflowr-announce mailing list (low-volume, read-only). For bugs reports, feature requests, and questions, please open an Issue.

For those with existing workflowr projects (pre-1.0.0), see ?wflow_update if you’re interested in updating your project to use the latest features. If you like your current project the way it is, you can continue to use workflowr as you have been by getting the latest bug fixes from workflowrBeta.


  • Organized
    • Provides a project template with organized subdirectories
    • Mixes code and results with R Markdown
    • Uses Git to version both source code and results
  • Reproducible
    • Displays the code version used to create each result
    • Runs each analysis in an isolated R session
    • Records the session information of each analysis
    • Sets the same seed for random number generation for each analysis
  • Shareable
    • Creates a website to present your research results
    • Documents how to host your website for free via GitHub Pages
    • Creates links to past versions of results

To see a workflowr website in action, see this video demonstration.

For related tools, see r-project-workflows.


  1. Install R

    • (Recommended) Install RStudio

    • (Optional) Install pandoc

    • (Optional) Install Git

  2. Install workflowr from CRAN:

  3. Create an account on GitHub


Workflowr was developed, and is maintained, by John Blischak, a postdoctoral researcher in the laboratory of Matthew Stephens at The University of Chicago. He is funded by a grant from the Gordon and Betty Moore Foundation to MS. Peter Carbonetto and Matthew Stephens are co-authors.

We are very thankful to workflowr contributors for helping improve the package. We are also grateful for workflowr users for testing the package and providing feedback—thanks especially to Lei Sun, Xiang Zhu, Wei Wang, and other members (past and present) of the Stephens lab.

The workflowr package uses many great open source packages. Especially critical for this project are the R packages git2r, knitr, and rmarkdown. Please see the vignette How the workflowr package works to learn about the software that makes workflowr possible.

Workflowr is available under the MIT license. Please run citation("workflowr") for proper attribution.


We welcome community contributions, especially improvements to documentation. To get started, please read the contributing guidlines. Also, please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.