Reproducible Computing

Abstract

Success in statistics and data science is dependent on the development of both analytical and computational skills.

This workshop will cover:

• Recognizing the problems that reproducible research helps address.
• Identifying pain points in getting your analysis to be reproducible.
• The role of documentation, sharing, version control, automation, and organization in making your research more reproducible.
• Introducing tools to solve these problems, specifically R, RStudio, RMarkdown, git, GitHub, and make. - Strategies for scaling these tools and methods for larger more complex projects.

Workshop attendees will work through several exercises and get first-hand experience with using relevant tool-chains and techniques, including R/RStudio, literate programming with R Markdown, automation with make, and collaboration and version control with git/GitHub.

Schedule

Time	Activity	Slides
08:30 - 09:00	Welcome	html, pdf
09:00 - 09:40	Literate programming	html, pdf
09:40 - 10:15	Naming & Organization	html, pdf
10:15 - 10:30	Coffee break
10:30 - 12:30	Version control with Git and GitHub	html, pdf
12:30 - 14:00	Lunch break
14:00 - 14:30	Scaling reproducible projects	html, pdf
14:30 - 15:15	Introduction to make	html, pdf
15:15 - 15:30	Coffee break
15:30 - 16:30	make in action
16:30 - 17:00	Parting remarks	html, pdf

Topic Details

Welcome, literate programming, and naming

• Recognize the problems that reproducible research helps address and identify pain points in getting your analysis to be reproducible.
• The role of documentation, sharing, automation, and organization in making your research more reproducible.
• Literate programming with R Markdown
  • Introduce the data: World Cup!
  • Hands on activity: Updating an analysis when the source data changes
• Naming best practices

Organization and version control with Git and GitHub

• Project organization
  • File and folder organization for projects
  • Naming conventions
• What is Git and version control?
• Git in GitHub
  • Define git vocabulary (commit, fork, pull request, repository, commit message).
  • Demonstrate ability to navigate through a Github repository main page.
  • Define the difference between a directory and a repository.
  • Create a repository on GitHub.
  • Demonstrate ability to commit changes to text files with a commit message.
  • Evaluate repository History.
  • Create a pull request to someone else’s repository.
• Git in RStudio
  • Define git vocabulary (push, fork, local repository, remote repository)
  • Fork remote repository from Github into RStudio.
  • Navigate through the basics of using git in RStudio.
  • Push local repository from RStudio to Github.
  • Demonstrate the ability to host code from RStudio to Github.
• usethis Package

Scaling reproducible projects + Make

• Practical example - Scottish lip cancer
  • Reproducible R Markdown document with “full Bayesian analysis” including data munging, EDA, model fitting and analysis.
• Caching as a solution to scaling
  • Build your own cache: Saving your own results with save() vs. saveRDS()
  • R Markdown caching: cache = TRUE
• Using make to automate and scale
  • Introduce make
  • Review make syntax
  • Introduce hands on exerciseß

Computing requirements

An R + RStudio computing environment will be provided for all students via RStudio Cloud. All that will be needed the day of the event is a laptop and a Google Account that can be used for authentication.

Instructor

Colin Rundel - University of Edinburgh, Duke University

Colin recently started as a Lecturer in the School of Mathematics at the University of Edinburgh. Prior to this position he was an a Assistant Professor of the Practice in the Department of Statistical Science at Duke University. He has developed and taught a number of Statistical Computing courses for undergraduate, master’s and Ph.D. levels students. His pedagogical and research interests are in the area of statistical computing, data science, and spatial statistics.

Acknowledgements

• Data Carpentry’s modules on naming, organization, and version control.
• Happy git with R by Jenny Bryan.

License

Materials in this repository are licensed under CC Attribution 4.0 International.