rworkflows: automating reproducible practices for the R community.

Area of Science:

• Computational biology
• Software engineering
• Data science

Background:

• Reproducibility in computational research remains a significant challenge.
• Over 50% of R packages are distributed via GitHub, which lacks built-in quality checks.
• Ensuring consistent R package usability across time and platforms is difficult.

Purpose of the Study:

• To develop an automated solution for enhancing R package reproducibility and usability on GitHub.
• To provide a user-friendly tool for R package developers of all skill levels.
• To promote widespread adoption of best practices in the R community.

Main Methods:

• Development of rworkflows, a suite of tools for continuous integration and deployment.
• Implementation via a single R function call with customizable options.
• Automated triggering of workflows upon GitHub repository updates.

Main Results:

• Workflows automatically install dependencies, run code checks, and generate documentation.
• Publicly accessible containerized environments are deployed.
• The rworkflows suite is free, automated, and simple to implement.

Conclusions:

• rworkflows significantly improves the quality and reproducibility of R packages.
• The tool lowers the barrier for implementing robust software development practices.
• Widespread adoption of rworkflows can enhance the reliability of the R ecosystem.