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How To Make Your Data Analysis Notebooks More Reproducible


Slide deck | Slide deck as PDF

๐ŸŽฅ Video of talk at rstudio::conf(2019)


I have included a handful of links to papers, software packages and tutorials/manuals about some tools I mention in my talk. Pull requests or issues on additional ones to include are welcome.

Research Compendia

Examples of Research Compendia on GitHub Below are a few links to real world examples of research compendia in R. To have a minimal compendium, all you really need is a valid DESCRIPTION file containing a handful of fields such as type, name, version and dependencies. See Marwick et al 2017 for a detailed description of the different types of compendia.




Software packages related to research compendia

  • ๐Ÿ“ฆ rrtools by Ben Marwick (also the author of the packaging data analysis paper mentioned above) extends functions in devtools and provides instructions, templates, and functions to make a basic compendium suitable for doing reproducible research with R.
    • Also see ๐Ÿ“ฆ workflowr by John Blischak and the task view on R-based data analysis projects maintained by John Blischak, Anna Krystalli, Ben Marwick, Daniel Nรผst.
  • ๐Ÿ“ฆ usethis Many of the major function in rrtools are imported from usethis. A savvy user can get by setting up and maintaining a compendium purely with usethis functions.
  • ๐Ÿ“ฆ goodpractice - Designed to help you build more robust packages, the package does a deep dive on your package contents and provide advice on syntax pitfalls to avoid, code formatting suggestions, and helps you improve overall package structure.
  • The ๐Ÿ“ฆ rticles package by JJ has numerous journal templates and together with Rstudio addins like word countaddin and citr + knitcitations.

๐Ÿ“ˆ Data management

  • ๐Ÿ“ฆ piggyback, [docs]: This clever R package allows you to attach arbitrary data (or other) files (upto 2gb each) to a GitHub release. Given GitHub's fast CDN, this would be an easy way to quickly attach large files to a compendium and read them back in a local/collaborator/remote environment very easily. As always be sure to archive a long-term copy on Zenodo.
  • ๐Ÿ“ฆ arkdb [docs]: This package allows you to archive and unarchive databases as flat text files.
  • ๐ŸŽฅ For more on setting up data packages, see this excellent talk by Noam Ross at New York R.

Computational environments: Binder and friends

Other hosted Binder hubs

Setting up Binder for your analysis

I have captured all the various ways to set up mybinder with a R project in a separate document.

Are you interested in setting up or hosting a binderhub for the R community? Get in touch via the issues.

Also see

Software packages related to setting up computational environments

  • ๐Ÿ“ฆ Containerit. Detailed blog post This sweet package will generate a Dockerfile for you by examining the code inside a folder or just from your session info. This is analogous to repo2docker but is very R centric
  • stevedore Although there are a few docker clients (docker, harbor), this is my recommendation for managing docker containers from inside R.

๐Ÿ”จ Workflows: drake and friends

  • ๐Ÿ“ฆ drake - An R-focused pipeline toolkit for reproducibility and high-performance computing. Install the package from here or CRAN.
  • The prequel to the drake R package A blog post by the creator of drake describing his motivation for the package.
  • drake manual A detailed bookdown guide on how to setup and use drake for projects of varying levels of complexity.
  • Presentation on drake Slides from a talk by Will Landau (who is here at the conference so go pick his brain if you want to learn more!)

Real world drake examples



Many thanks to Chris Holdgraf, Carl Boettiger, Will Landau, and Ben Marwick for various discussions on these topics. Also thanks to Ciera Martinez, Kara Woo, and Nick Tierney for comments on the presentation.


Resources from my Rstudio::conf 2019 talk







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