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moai-lang-r

Apply modern R 4.4+ development patterns for data science projects.

Updated Jan 12, 2026
Search Tags:#workflow#data-analysis#tidyverse#R#ggplot2#Shiny#R4.4
One-click install
npx skills add https://github.com/windysky/claude_notification_wsl2 --skill moai-lang-r-windysky
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-lang-r
Source: https://github.com/windysky/claude_notification_wsl2/tree/main/.claude/skills/moai-lang-r
Command: npx skills add https://github.com/windysky/claude_notification_wsl2 --skill moai-lang-r-windysky

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a structured approach for R developers to adopt modern, reproducible development patterns across data analysis, visualization, and Shiny applications.

Core Features & Use Cases

  • R 4.4+ feature coverage: native pipes, lambda syntax, and modern patterns.
  • Tidyverse-centric data workflows: dplyr, tidyr, purrr, ggplot2.
  • Shiny app design patterns: modularization, reactive patterns, and deployment-ready templates.
  • Reproducible reporting and project scaffolding across pipelines and dashboards.

Quick Start

To begin, install R 4.4+ and the standard tidyverse suite, then follow the Moai Lang-R guidance to initialize a data science project, create analysis modules, and build a simple Shiny app.

Frequently Asked Questions about moai-lang-r

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

FAQPage Schema
How do I structure a reproducible data science project in R using tidyverse pipelines?▼

To structure reproducible R data science projects, apply standardized tidyverse pipelines using dplyr, tidyr, and purrr to create modular analysis components and consistent project scaffolding.

What is the best way to modularize a Shiny app for deployment?▼

The best way to modularize Shiny apps is applying standardized reactive patterns and modularization templates to build scalable, deployment-ready dashboard components.

Do I need R 4.4+ to use native pipe and lambda syntax in my workflows?▼

Yes, you need R 4.4+ to utilize native pipe operators and lambda syntax, as these modern R features are required for the standardized development patterns provided here.

How does ggplot2 fit into modern R data visualization workflows?▼

ggplot2 integrates into R workflows by providing standardized visualization layers within tidyverse pipelines, allowing you to generate reproducible plots directly from processed data.

Can I use this approach to scaffold both analysis modules and dashboards?▼

Yes, you can scaffold reproducible reporting components for both tidyverse analysis modules and Shiny dashboards using the provided modern R development patterns.

Are additional software packages required beyond the standard R ecosystem?▼

No additional software beyond the standard R ecosystem is required, though you must install core tidyverse packages to utilize the data analysis and visualization patterns.

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