Reproducible Research in R (and friends)

Cheatsheet

This cheatsheet provides essential guidelines and best practices for conducting reproducible research using R and related tools. It covers project organization, version control, data management, documentation, environment management, workflow automation, and more. For any suggestions or feedback, please feel free to email me.

1 Project Organization

• Use a consistent folder structure:
  • data/ - Analysis data files
  • scripts/ - Analysis scripts
  • outputs/ - Results (figures, tables)
  • docs/ - Documentation and reports
• Use RStudio Projects to facilitate project management and environment isolation
• Maintain a project log to document progress, changes, and key decisions throughout the analysis
• Reference:
  • The concept of research compendium
  • Using RStudio projects

2 Version Control

• Use Git to track changes in scripts and documents
• Commit regularly with meaningful messages
• One repository per analysis
• Make sure your data/ folder is in the .gitignore file
• Make sure there is no sensitive information in your code
• Reference:
  • Happy Git with R

3 Data Management

• Store raw data in data/raw/ and never modify it directly
• Produce a README describing the source data
• Use scripts to clean and process data, save the cleaned data in data/processed/
• Document each step of data cleaning
• Keep data cleaning separate from analysis
• Organize your data in a tidy format: each variable is a column, each observation is a row
• Reference:
  • Principles of tidy data

4 Documentation

• Comment code extensively to explain steps and logic
• Create README files to explain project structure and instructions for running the analysis
• Document all functions clearly, including input parameters, output, and purpose
• Reference:
  • Example README file
  • {roxygen2} for function documentation

5 Environment Management

• Use sessionInfo() or devtools::session_info() to capture the R session information
• Use {renv} to manage package versions
• Reference:
  • Introduction to renv
  • Example on how to show the session info (scroll to bottom)

6 Workflow Automation

• Organize your analysis into a series of numbered and ordered scripts to create a clear and reproducible workflow (e.g., 01-data-cleaning.R, 02-data-analysis.R, 03-visualization.R)
• Create a master script (e.g., run_all.R) that sequentially runs each numbered script

OR

• Use Makefile or {targets} package to automate and document the workflow

• Reference:

  • {targets} Package
    
  • Example Project using {targets}

7 Analysis Scripts

• Break analysis into small, reusable functions
• Use meaningful and consistent naming conventions such as provided by the Tidyverse Naming Conventions for variables and functions and by data carpentry for folders and files
• Style your code according to standardized recommendations from the Tidyverse Style Guide
• Reference:
  • Embrace functional programming
  • Tidyverse Style Guide

8 Computational reproducibility

• Set seeds to ensure reproducibility when using randomness in your analysis
• Document all warnings
• Reference:
  • Random number seed in R

9 Reporting

• Use RMarkdown (.Rmd) or Quarto (.Qmd) files to combine code, results, and narrative for creating dynamic reports
• Reference:
  • Quarto Documentation

10 Validation

• Get your code reviewed prior to publication
• Reference:
  • Code Review Practices

11 Sharing Code And Data

• Use repositories like GitHub or GitLab for sharing code
• Use repositories like Zenodo or data.gouv.fr if you have data sets to share

References

Marwick, Ben, Carl Boettiger, and Lincoln Mullen. 2018. “Packaging Data Analytical Work Reproducibly Using R (and Friends).” Preprint. PeerJ Preprints. https://doi.org/10.7287/peerj.preprints.3192v2.