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moai-lang-r

Develops R 4.4+ data analysis pipelines with tidyverse, ggplot2, and Shiny applications.

Updated Nov 20, 2025
Search Tags:#data science#r#tidyverse#ggplot2#shiny#r development
One-click install
npx skills add https://github.com/soo-kate-yeon/shadowoo --skill moai-lang-r-soo-kate-yeon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-lang-r
Source: https://github.com/soo-kate-yeon/shadowoo/tree/main/.agents/skills/moai-lang-r
Command: npx skills add https://github.com/soo-kate-yeon/shadowoo --skill moai-lang-r-soo-kate-yeon

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines R development, from data analysis and visualization to building interactive web applications, by providing expert guidance and best practices.

Core Features & Use Cases

  • Data Analysis & Visualization: Leverage tidyverse, ggplot2, and dplyr for efficient data manipulation and compelling visualizations.
  • Shiny App Development: Build dynamic and responsive web applications with Shiny.
  • Reproducible Research: Utilize R Markdown and Quarto for creating reports and documents.
  • Use Case: You need to analyze a dataset, create several plots using ggplot2, and then build a simple interactive dashboard using Shiny to present your findings.

Quick Start

Use the moai-lang-r skill to create a basic Shiny app that displays a scatter plot of 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 a Shiny app to visualize a dataset using ggplot2?▼

You can build a Shiny app by defining UI and server logic, using ggplot2 to render interactive scatter plots from datasets like mtcars and displaying them dynamically within the application.

What is the best way to manage R dependencies for reproducible data science?▼

Use renv to manage R dependencies, creating isolated project environments that ensure your data analysis pipelines and visualizations remain completely reproducible across different systems.

How does tidyverse improve data manipulation in R?▼

Tidyverse improves data manipulation by providing packages like dplyr, allowing you to efficiently filter, mutate, and summarize datasets using a consistent, intuitive syntax for analysis pipelines.

Can I use Quarto and R Markdown for reproducible research reports?▼

Yes, you can use Quarto and R Markdown to generate dynamic documents, embedding R analysis pipelines and ggplot2 visualizations directly into formatted reports for reproducible research.

Does this R development skill support testthat for unit testing?▼

Yes, this skill supports testthat to implement robust testing frameworks, enabling you to validate data transformation logic and ensure R functions perform correctly before deploying analysis pipelines.

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