workflowr: organized + reproducible + shareable data science in R
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 explore reproducible research projects facilitated by workflowr, browse the projects registered at workflowr.io. 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.
- Provides a project template with organized subdirectories
- Mixes code and results with R Markdown
- Uses Git to version both source code and results
- 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
To see a workflowr website in action, see this video demonstration.
For related tools, see r-project-workflows.
Install workflowr from CRAN:
library("workflowr") # Configure Git (only need to do once per computer) wflow_git_config(user.name = "Full Name", user.email = "email@domain") # Start a new workflowr project wflow_start("myproject") # Build the site wflow_build() # Customize your site! # 1. Edit the R Markdown files in analysis/ # 2. Edit the theme and layout in analysis/_site.yml # 3. Add new or copy existing R Markdown files to analysis/ # Preview your changes wflow_build() # Publish the site, i.e. version the source code and HTML results wflow_publish("analysis/*", "Start my new project")
Read the full Getting started vignette to learn how to share your results online. Alternatively, you could read the Reproducible research workshop vignette. It covers the same steps, but includes example code and data to demonstrate some of workflowr's reproducibility features
Read the customization vignette for ideas on how to customize your research website
Read the migrating vignette for how to integrate workflowr into your existing project
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.
Blischak JD, Carbonetto P, and Stephens M. Creating and sharing reproducible research code the workflowr way [version 1; peer review: 3 approved]. F1000Research 2019, 8:1749 (https://doi.org/10.12688/f1000research.20843.1)
To obtain a BibTeX entry, please run
citation("workflowr"). Note that
F1000Research publishes not only the original version but also any revisions. To
check for the latest version, please go to the paper's URL.
We welcome community contributions, especially improvements to documentation. To get started, please read the contributing guidelines. 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.