R Best Practices

1 Welcome

Welcome to R Best Practices! This course covers essential best practices for writing clear, maintainable, and reproducible R code.

2 Course Materials

Materials will be added here as we progress through the course.

https://r-best-practices.njtierney.com

Prerequisites

• Basic R programming experience
• Familiarity with writing R scripts
• Experience working on data analysis projects

Learning outcomes

• How to name things effectively
• Using a style guide
• How to refactor your code
• How to review your code and others’
• How to lay out a project so others know how to run your code
• How to make a reproducible example (reprex)

3 Schedule

3.1 Project Organisation

• Common project structures
• Pitfalls of organisation
• Understanding file paths
• RStudio and positron set up
• “Good Enough”/Common Sense principles of project organisation
• Always have a README

3.2 Code Style and Readability

• Style is like grammar
• why we care about consistent names, spaces, indentation
• Using the {air} formatter
• Using linters

3.3 Writing readable code

• Good vs bad variable names
• De-chunking code
• How to review code
• Review your own code

3.4 Writing Functions

• The problem functions solve
• Anatomy of a function
• Good (simple) function design
• Using {fnmate} to speed up creating functions
• How to use a debugger

3.5 Writing a reprex / getting unstuck

• How to share problems
• Using {reprex}
• Practicing reprex
• Practical tips on debugging
  • Keep solving vs cleaning up

3.6 Putting It All Together

• Take an existing project and provide code review
• Apply code linting, code styling, functions
• Discussion and Q & A