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 hostingall 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: ```r

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: ```r

   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@example.com [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: ```r

   Document functions and build package

   devtools::document() devtools::check()

# Build website locally quarto::quarto_render() ```

1. 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

```r

Example: Custom data cleaning functions

cleansurveydata <- function(df, remove_incomplete = TRUE) { # Your data cleaning logic here } ```

Analysis Wrappers

```r

Example: Simplified statistical analysis

quick_regression <- function(data, outcome, predictors) { # Wrapper for common regression tasks } ```

Visualization Functions

```r

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: ```r

Example test structure in tests/testthat/

testthat("myfunction works correctly", { expectequal(myfunction(2, 3), 5) expecterror(myfunction("a", "b")) }) ```

Custom Documentation

Leverage roxygen2 for professional documentation: ```r

' 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: yaml 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 submissionsit'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