VSCode R Snippets Collection

Overview

A professional collection of VSCode snippets for R and R Markdown, designed to accelerate your data science workflow. Supports the latest R 4.5.0 features and works with VS Code, Positron, and other modern R IDEs.

Pro Tip: These snippets also work in the new RStudio Codium-based IDE, Positron.

Installation

1. In VS Code, go to Code -> Preferences -> User Snippets
2. Select r.json (for R snippets) or markdown.json (for R Markdown)
3. Copy and paste the snippets from snippets.json in this repository

Featured Snippets

This collection includes 100+ powerful snippets covering the entire data science workflow:

Data Import & Manipulation

• Modern tidyverse operations (|> pipe operator)
• High-performance data.table operations
• Database connections (MySQL, PostgreSQL, SQLite)
• Spatial data handling with sf

Visualization

• Complete ggplot2 templates with themes and facets
• Publication-quality plot themes
• Interactive visualization with plotly
• Customizable chart parameters

Statistical Analysis

• Comprehensive modeling (linear, GLM, mixed effects)
• ANOVA, t-tests, and non-parametric tests
• Modern machine learning workflows with tidymodels
• Cross-validation and model evaluation

Machine Learning & Advanced Analytics

• Random forest, gradient boosting, and neural networks
• Cross-validation templates with tidymodels
• Time series forecasting with Prophet
• Text mining with tidytext

Reporting & Reproducibility

• R Markdown report templates
• Shiny application scaffolding
• Parallel processing setup
• Automated EDA templates

Usage Examples

Quick Data Pipeline

# Type "tidyverse: pipe chain" for:
data |>
  filter(condition) |>
  mutate(new_var = transformation) |>
  group_by(group_var) |>
  summarize(summary_stats) |>
  arrange(arrangement)

Professional Visualization

# Type "ggplot: scatter with regression" for:
ggplot(data, aes(x = x, y = y)) +
  geom_point(aes(color = group), alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE) +
  theme_minimal() +
  labs(title = "Scatterplot with Linear Regression", x = "X Label", y = "Y Label")

Machine Learning Workflow

# Type "cross validation" for a complete modeling workflow

Detailed Snippet List

The collection includes snippets for:

• Data Wrangling: filter, select, mutate, arrange, join, pivot, etc.
• Visualization: All ggplot geoms, themes, facets, and customizations
• Statistical Tests: t-tests, ANOVA, chi-square, normality tests
• Modeling: Linear models, GLM, mixed effects, machine learning
• Advanced Analytics: Time series, text mining, spatial data
• Functional Programming: map, walk, reduce, purrr operations
• Reporting: R Markdown templates, Shiny app structures
• Performance: Parallel processing, data.table operations

See snippets.json for the complete list.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-snippet)
3. Commit your changes (git commit -am 'Add some amazing snippet')
4. Push to the branch (git push origin feature/amazing-snippet)
5. Open a Pull Request

Requirements

• R 4.0+ (optimized for R 4.3+)
• VS Code with R extension or similar IDE
• Recommended packages: tidyverse, data.table, ggplot2, tidymodels

License

This project is licensed under the Beerware License - see the LICENSE file for details. If you find these snippets useful, consider buying me a beer if we ever meet!

Acknowledgments

• The R Core Team for their amazing language
• The tidyverse team for revolutionizing data science in R
• The VS Code team for an excellent IDE
• All contributors to this project

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Last updated: April 29, 2025