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r-data-science

Generate reproducible R project scaffolds and code following tidyverse conventions.

Updated Aug 27, 2026
Search Tags:#data science#data analysis#visualization#statistics#quarto#epidemiology#r programming
One-click install
npx skills add https://github.com/crypticpy/Rdata --skill r-data-science
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: r-data-science
Source: https://github.com/crypticpy/Rdata/tree/main/.claude/skills/r-data-science
Command: npx skills add https://github.com/crypticpy/Rdata --skill r-data-science

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the manual, repetitive coding often involved in R-based data analysis, visualization, and statistical workflows. It standardizes practices, accelerates report generation, and ensures adherence to modern R best practices, saving significant time and reducing complexity for data professionals.

Core Features & Use Cases

  • Tidyverse-First Data Wrangling: Efficiently clean, transform, and prepare data using dplyr, tidyr, and janitor for analysis.
  • Publication-Quality Visualizations: Generate compelling ggplot2 charts and interactive plotly graphics that effectively communicate insights.
  • Robust Statistical Analysis: Perform a wide range of statistical tests, regression modeling (linear, logistic, Poisson), and epidemiological calculations (RR, OR, age-adjusted rates).
  • Reproducible Research & Reporting: Structure projects for reproducibility using renv for package management and Quarto for dynamic, professional reports and dashboards.
  • Use Case: Imagine you need to analyze a new public health dataset, generate an epidemic curve, calculate age-adjusted rates, and produce a professional report. This Skill guides you through the entire process, ensuring best practices and saving you from writing boilerplate code.

Quick Start

Use the r-data-science skill to analyze the attached 'surveillance_data.csv' file, generate an epidemic curve, and summarize key findings in a Quarto report.

Frequently Asked Questions about r-data-science

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

FAQPage Schema
How do I automate R data analysis and reporting with reproducible code?▼

R data analysis automation uses tidyverse conventions, renv for package management, and Quarto for dynamic reports. This Skill generates scaffolded R projects that standardize data wrangling, visualization, and statistical workflows while ensuring reproducibility across public health and epidemiology analyses.

Can I use ggplot2 and Quarto together for publication-quality epidemiological reports?▼

Yes. This Skill structures R projects to combine ggplot2 visualizations with Quarto for dynamic, reproducible reports. It handles epidemic curves, age-adjusted rates, and statistical analysis within a single reproducible workflow compatible with renv package management.

What's the best way to organize R code for data wrangling and statistical analysis following tidyverse conventions?▼

Tidyverse-first organization uses dplyr, tidyr, and janitor for data transformation with explicit missing-data handling and snake_case naming. This Skill generates project scaffolds with pipe-forward compatibility, proper path handling via here, and input validation for consistent, maintainable code.

How do I generate an epidemic curve and calculate age-adjusted rates in R?▼

This Skill automates epidemiological calculations including relative risk, odds ratios, and age-adjusted rates within reproducible R workflows. It generates code for epidemic curves, statistical tests, and regression modeling while adhering to public health data-science best practices.

Do I need to know renv and Quarto to use R for data analysis projects?▼

Understanding renv and Quarto enhances reproducibility, but this Skill abstracts setup complexity. It generates pre-configured project scaffolds with renv for dependency management and Quarto templates for reports, letting you focus on analysis rather than infrastructure.

What file formats does R data analysis support for importing and exporting results?▼

This Skill handles R scripts (.R), R Markdown (.Rmd), and Quarto (.qmd) files for analysis and reporting. It generates code compatible with standard data imports (CSV) and exports through Quarto for HTML, PDF, and dashboard outputs.

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