packageSkeleton

Overview

The packageSkeleton repository provides a comprehensive, production-ready template for creating professional R packages with integrated Quarto documentation websites. This skeleton eliminates the complexity of package setup by providing a fully configured development environment that includes automated testing, continuous integration, and professional documentation hosting—all ready to deploy on GitHub.

Transform your R scripts and workflows into a professional, shareable package in minutes rather than hours. Whether you're consolidating personal utilities, creating tools for your team, or contributing to the open-source community, this skeleton provides everything you need to get started immediately.

🚀 Quick Start

1. Use this template: Click "Use this template" on GitHub or clone this repository
2. Rename: Replace "packageSkeleton" with your package name throughout the project
3. Customize: Add your functions to R/, update DESCRIPTION, and modify documentation
4. Deploy: Push to GitHub - your package website will automatically deploy via GitHub Pages

📖 Detailed Setup Guide | 🌐 Live Example

📦 What's Included

This skeleton provides a complete R package infrastructure:

Core Package Structure

packageSkeleton/
├── R/                          # Your R functions
├── man/                        # Auto-generated documentation
├── vignettes/                  # Package tutorials and examples
├── tests/                      # Automated testing framework
├── data/                       # Package datasets
├── DESCRIPTION                 # Package metadata
├── NAMESPACE                   # Package namespace (auto-managed)
└── LICENSE.md                  # License information

Documentation & Website

├── _quarto.yml                 # Website configuration
├── index.qmd                   # Homepage content
├── articles/                   # Vignettes and tutorials
├── reference/                  # Function documentation
└── .github/workflows/          # Automated deployment

Development Tools

• Automated Testing: Pre-configured testthat framework
• CI/CD Pipeline: GitHub Actions for testing and deployment
• Code Coverage: Integrated coverage reporting
• Documentation: Automatic function documentation with roxygen2
• Website Hosting: Automated Quarto website deployment to GitHub Pages
• Package Checks: Comprehensive R CMD CHECK validation

✨ Key Features

• Zero-Configuration Setup: Clone and start developing immediately
• Automated Documentation: Functions documented with roxygen2 are automatically included in your website
• Professional Styling: Clean, responsive Quarto website with modern design
• Continuous Integration: Automated testing on multiple R versions and operating systems
• Easy Customization: Modular structure allows easy modification of any component
• Version Control: Git-ready with appropriate .gitignore and configuration files
• Cross-Platform: Works seamlessly on Windows, macOS, and Linux

🎯 Perfect For

Data Scientists & Analysts

• Personal Utility Libraries: Consolidate your frequently-used data cleaning, visualization, and analysis functions
• Workflow Automation: Package repetitive tasks into reusable functions with professional documentation
• Team Collaboration: Share standardized tools and methodologies across your organization

R Developers

• Package Prototyping: Quickly test and iterate on package ideas with full infrastructure
• Best Practices: Learn modern R package development through a well-structured example
• Portfolio Projects: Create professional showcases of your R capabilities

Educators & Researchers

• Course Materials: Package datasets, functions, and tutorials for students
• Research Tools: Share reproducible analysis tools with the scientific community
• Workshop Resources: Provide participants with ready-to-use packages and documentation

🛠️ Installation & Setup

Prerequisites

• R (≥ 4.0.0) and RStudio (recommended)
• Git for version control
• GitHub account for hosting and collaboration
• Basic familiarity with R package structure

Essential R Packages

Install these packages to fully utilize the skeleton:

# Core development tools
install.packages(c("devtools", "usethis", "roxygen2", "testthat"))

# Documentation and website
install.packages(c("quarto", "pkgdown"))

Setup Steps

1. Create your repository:

   • Click "Use this template" on the GitHub repository page
   • Name your new repository (e.g., "myAwesomePackage")
   • Clone to your local machine

2. Customize the package:

   # In RStudio, open the project and run:
   usethis::use_description(fields = list(
     Title = "Your Package Title",
     Description = "What your package does",
     `Authors@R` = "Your Name <[email protected]> [aut, cre]"
   ))

3. Add your content:

   • Place R functions in R/ directory
   • Add datasets to data/ directory
   • Create vignettes in vignettes/ directory
   • Write tests in tests/testthat/ directory

4. Build and check:

   # Document functions and build package
   devtools::document()
   devtools::check()
   
   # Build website locally
   quarto::quarto_render()

5. Deploy:

   • Push changes to GitHub
   • Enable GitHub Pages in repository settings
   • Your website will be available at https://yourusername.github.io/yourpackage

💡 Content Ideas

Transform your R work into a professional package:

Data Manipulation Tools

# Example: Custom data cleaning functions
clean_survey_data <- function(df, remove_incomplete = TRUE) {
  # Your data cleaning logic here
}

Analysis Wrappers

# Example: Simplified statistical analysis
quick_regression <- function(data, outcome, predictors) {
  # Wrapper for common regression tasks
}

Visualization Functions

# Example: Themed plotting functions
theme_publication <- function() {
  # Custom ggplot2 theme for publications
}

Domain-Specific Tools

• Finance: Portfolio analysis, risk calculations
• Education: Assessment scoring, growth modeling
• Healthcare: Clinical data processing, outcome analysis
• Marketing: Customer segmentation, campaign analysis

🔧 Advanced Features

Automated Testing

The skeleton includes a comprehensive testing framework:

# Example test structure in tests/testthat/
test_that("my_function works correctly", {
  expect_equal(my_function(2, 3), 5)
  expect_error(my_function("a", "b"))
})

Custom Documentation

Leverage roxygen2 for professional documentation:

#' Calculate Summary Statistics
#'
#' This function calculates descriptive statistics for a numeric vector.
#'
#' @param x A numeric vector
#' @param na.rm Logical, should NA values be removed?
#' @return A list of summary statistics
#' @examples
#' calc_summary(c(1, 2, 3, 4, 5))
#' @export
calc_summary <- function(x, na.rm = TRUE) {
  # Function implementation
}

Website Customization

Modify _quarto.yml to customize your site:

website:
  title: "Your Package Name"
  navbar:
    left:
      - text: "Home"
        file: index.qmd
      - text: "Reference"
        file: reference/index.qmd

🤝 Why Create an R Package?

Creating an R package isn't just for CRAN submissions—it's about building better tools for yourself and your team:

• Efficiency: Stop copying functions between projects
• Documentation: Force yourself to document your work properly
• Testing: Ensure your functions work correctly across different scenarios
• Sharing: Easily distribute your tools to colleagues and collaborators
• Portfolio: Demonstrate your R skills professionally
• Learning: Master R package development best practices

📚 Learning Resources

• 📖 Complete Setup Guide: Step-by-step instructions
• 🔗 R Packages Book: Comprehensive guide by Hadley Wickham
• 🔗 Quarto Documentation: Learn to customize your website
• 🔗 GitHub Actions: Understand the CI/CD pipeline

🆘 Support & Contributing

• Issues: Report bugs or request features via GitHub Issues
• Discussions: Ask questions and share ideas in GitHub Discussions
• Contributing: Pull requests welcome! See CONTRIBUTING.md for guidelines
• Community: Join the broader R package development community

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Ready to start building? Use this template and create your first R package today! 🎉

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Produced with love ❤️❤️❤️️ by:

Damian Betebenner

Erik Whitfield

Brian Harrold

Nathan Dadey