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moai-lang-r

Develop R 4.4+ data science workflows with tidyverse, ggplot2, and Shiny.

Updated Feb 26, 2026
Search Tags:#data science#r#tidyverse#ggplot2#shiny#r development
One-click install
npx skills add https://github.com/skeeper75/widget.creator --skill moai-lang-r-skeeper75
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-lang-r
Source: https://github.com/skeeper75/widget.creator/tree/main/.claude/skills/moai-lang-r
Command: npx skills add https://github.com/skeeper75/widget.creator --skill moai-lang-r-skeeper75

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines R development by providing expert guidance on modern R features, data manipulation, visualization, and application building, significantly reducing development time and improving code quality.

Core Features & Use Cases

  • Modern R Development: Leverages R 4.4+ features like native pipes and lambda syntax.
  • Data Science Toolkit: Expertly handles data manipulation (dplyr, tidyr), visualization (ggplot2), and functional programming (purrr).
  • Shiny App Development: Guides the creation of interactive web applications with reactivity and modularity.
  • Reproducible Workflows: Supports R Markdown, Quarto, and renv for robust and reproducible analysis.
  • Use Case: Develop a complex data analysis pipeline, create interactive visualizations with ggplot2, build a responsive Shiny dashboard, and ensure reproducibility with renv.

Quick Start

Use the moai-lang-r skill to create a basic Shiny app that displays a histogram of the 'mpg' column from the 'mtcars' dataset.

Frequently Asked Questions about moai-lang-r

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an interactive Shiny dashboard with R?▼

Build interactive Shiny applications in R by structuring code around reactivity and modularity patterns. This approach streamlines web application development while ensuring responsive data visualizations and robust user interfaces.

What's the best way to manage data manipulation and visualization in R?▼

The best way to manage data manipulation and visualization in R is using the tidyverse toolkit. Data manipulation is handled via dplyr and tidyr, functional programming via purrr, and complex visualizations are created using ggplot2.

How do I ensure reproducible research workflows in R?▼

Ensure reproducible research workflows in R by utilizing R Markdown, Quarto, and renv. These tools manage project dependencies and document dynamic analysis pipelines, guaranteeing robust and reproducible project environments.

Does R 4.4+ support modern syntax for data science pipelines?▼

R 4.4+ supports modern syntax for data science pipelines through native pipes and lambda syntax. Leveraging these features significantly reduces development time and improves code quality within your data manipulation workflows.

How do I optimize R code for database integration and package development?▼

Optimize R code for database integration and package development by applying advanced patterns for performance optimization. This technique supports complex data analysis pipelines and ensures robust, scalable package architecture.

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