Organize your project into a research website
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README.md

workflowr: organized + reproducible + shareable data science in R

Travis-CI Build Status codecov AppVeyor Build Status CRAN status CRAN downloads

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.

Features

  • 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.

Installation

  1. Install R

    • (Recommended) Install RStudio

    • (Optional) Install pandoc

    • (Optional) Install Git

  2. Install workflowr from CRAN:

    install.packages("workflowr")
  3. Create an account on GitHub

Quick start

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")

Next steps:

  1. Read the full Getting started vignette to learn how to share your results online via GitHub

  2. Read the customization vignette for ideas on how to customize your research website

  3. Read the migrating vignette for how to integrate workflowr into your existing project

Attribution

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.