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kickstartR

Create Project Boilerplate Structures for R Analysis Projects

Overview

kickstartR is an R package that helps you quickly initialize new R analysis projects with a standardized, organized directory structure and boilerplate files. It creates a consistent project layout that follows best practices for data science and statistical analysis projects.

Features

  • Standardized Directory Structure: Creates organized folders for data, scripts, outputs, models, and notebooks
  • Boilerplate Files: Generates template files including README.md, .gitignore, and starter R scripts
  • RStudio Integration: Creates .Rproj files for seamless RStudio integration
  • Flexible Configuration: Customizable options for including/excluding specific folders
  • Best Practices: Follows R community best practices for project organization

Installation

You can install the development version of kickstartR from GitHub:

# Install devtools if you haven't already
install.packages("devtools")

# Install kickstartR from GitHub
devtools::install_github("sidhuk/kickstartR")

Quick Start 

library(kickstartR)

# Create a new project in the current directory
initialize_project("MyAnalysisProject")

# Create a project in a specific location
initialize_project("MyProject", path = "~/Documents/R_Projects")

# Customize the project structure
initialize_project("MyProject",
                   include_models = FALSE,
                   include_notebooks = FALSE,
                   create_rproj = TRUE)

Project Structure

When you run initialize_project(), it creates the following directory structure:

MyProject/
├── 01_data/
│   ├── 01_raw/           # Original, immutable data
│   ├── 02_processed/     # Cleaned, transformed data
│   └── 03_external/      # External data sources
├── 02_scripts/           # R scripts and analysis code
│   └── 00_main_script.R  # Main analysis script template
├── 03_output/            # All analysis outputs
│   ├── 01_figures/       # Generated plots and figures
│   ├── 02_tables/        # Generated tables and summaries
│   └── 03_reports_rendered/ # Rendered reports (HTML, PDF)
├── 04_models/            # Saved model objects (optional)
├── 05_notebooks/         # R Markdown, Jupyter notebooks (optional)
├── README.md             # Project documentation
├── .gitignore            # Git ignore rules
├── .here                 # For use with the 'here' package
└── MyProject.Rproj       # RStudio project file

Function Reference

initialize_project()

Creates a new project with standardized directory structure and boilerplate files.

Parameters:

  • project_name: Name of the project (required)
  • path: Directory where project should be created (default: current directory)
  • overwrite: Whether to overwrite existing directories (default: FALSE)
  • create_rproj: Whether to create RStudio project file (default: TRUE)
  • include_models: Whether to include models folder (default: TRUE)
  • include_notebooks: Whether to include notebooks folder (default: TRUE)

Examples:

# Basic usage
initialize_project("MyProject")

# Advanced usage with custom options
initialize_project(
  project_name = "DataAnalysis2024",
  path = "~/Projects",
  include_models = TRUE,
  include_notebooks = FALSE,
  create_rproj = TRUE
)

# Overwrite existing project (use with caution!)
initialize_project("ExistingProject", overwrite = TRUE)

Best Practices

The project structure created by kickstartR follows these best practices:

  1. Separate raw and processed data: Keep original data immutable in 01_data/01_raw/
  2. Organize outputs: Separate figures, tables, and reports for easy navigation
  3. Use relative paths: The .here file enables robust path management with the here package
  4. Version control ready: Includes sensible .gitignore patterns for R projects
  5. Reproducible workflows: Template scripts include best practice patterns
  1. Create your project: initialize_project("MyProject")
  2. Open the .Rproj file in RStudio
  3. Place raw data in 01_data/01_raw/
  4. Start coding in 02_scripts/00_main_script.R
  5. Save processed data to 01_data/02_processed/
  6. Export outputs to appropriate folders in 03_output/

Dependencies

kickstartR has minimal dependencies and uses only base R functions. For enhanced functionality, consider installing:

  • here: For robust file path management
  • usethis: For additional project setup utilities
  • renv: For package dependency management

Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests at https://github.com/sidhuk/kickstartR.

License

This project is licensed under the MIT License - see the LICENSE file for details.