moai-lang-r

Develop R 4.4+ applications with tidyverse, ggplot2, and Shiny.

Updated Nov 28, 2024

Search Tags:#data science #r #tidyverse #package development #ggplot2 #shiny

Ready to run

npx skills add https://github.com/desafin/Resource-Monitor-pyQT --skill moai-lang-r-desafin

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: moai-lang-r
Source: https://github.com/desafin/Resource-Monitor-pyQT/tree/main/.claude/skills/moai-lang-r
Command: npx skills add https://github.com/desafin/Resource-Monitor-pyQT --skill moai-lang-r-desafin

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides expert assistance for R development, covering modern language features, data science workflows, visualization, and application building, streamlining complex R projects.

Core Features & Use Cases

Modern R Development: Leverage R 4.4+ features like native pipes and lambda syntax.
Data Science Pipelines: Build robust data manipulation, visualization (ggplot2), and analysis pipelines using the tidyverse.
Shiny Applications: Develop interactive web applications with Shiny, including modularization and reactivity.
Package Development & Testing: Create and test R packages with best practices.
Use Case: You need to build a Shiny app that visualizes real-time data, requires data cleaning with dplyr, and needs to be deployed efficiently.

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 a Shiny app for real-time data visualization in R?▼

To build a Shiny app for real-time data visualization in R, use modularization and reactivity patterns to handle dynamic data streams and render interactive UI components efficiently.

What is the best way to structure data manipulation pipelines using the tidyverse?▼

The best way to structure tidyverse data manipulation pipelines is leveraging R 4.4+ native pipes and lambda syntax to chain dplyr operations for robust data cleaning and transformation.

How does modern R 4.4+ syntax improve package development?▼

Modern R 4.4+ syntax improves package development by utilizing native pipes and lambda expressions to create cleaner, more maintainable code with modern testing frameworks and best practices.

Can I create custom ggplot2 extensions for specialized data visualization?▼

Yes, you can create custom ggplot2 extensions for specialized data visualization by implementing advanced guides that extend the grammar of graphics for specific analytical requirements.

What are the limitations of using renv for environment management in R projects?▼

While metadata highlights renv for robust environment management, limitations may arise when integrating complex database systems or deploying Shiny applications across diverse production infrastructures.