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.
|08:30 - 09:00
|09:00 - 09:40
|09:40 - 10:15
||Naming & Organization
|10:15 - 10:30
|10:30 - 12:30
||Version control with Git and GitHub
|12:30 - 14:00
|14:00 - 14:30
||Scaling reproducible projects
|14:30 - 15:15
||Introduction to make
|15:15 - 15:30
|15:30 - 16:30
||make in action
|16:30 - 17:00
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
- 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
- R Markdown caching:
cache = TRUE
- Using make to automate and scale
- Introduce make
- Review make syntax
- Introduce hands on exerciseß
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.
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.
Materials in this repository are licensed under CC Attribution 4.0 International.