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data-analysis

Execute end-to-end R data analysis workflows with reproducible outputs.

1.5k|2.9k|Updated Feb 6, 2026
Search Tags:#workflow#regression#data-analysis#r#reproducible#ggplot2
One-click install
npx skills add https://github.com/pedrohcgs/claude-code-my-workflow --skill data-analysis-pedrohcgs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/pedrohcgs/claude-code-my-workflow/tree/main/.claude/skills/data-analysis
Command: npx skills add https://github.com/pedrohcgs/claude-code-my-workflow --skill data-analysis-pedrohcgs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data analysis in R often involves a long sequence of setup, exploration, modelling, and reporting steps that are hard to reproduce and maintain. This skill provides a structured end-to-end workflow to streamline these tasks and deliver reproducible results.

Core Features & Use Cases

  • End-to-end R workflow: from data loading and exploration to regression analyses and publication-ready tables and figures.
  • Reproducibility: all steps are documented, with outputs saved under a consistent directory structure and objects saved with saveRDS for later review.
  • Publication-ready outputs: generation of latex/table outputs and ggplot figures suitable for Beamer/Quarto reports.
  • Use Case: you have a dataset of surveys; this workflow will perform EDA, fit models, and produce a manuscript-friendly set of tables and plots.

Quick Start

Run the data-analysis workflow by providing a dataset path or a description of your analysis goal, and the system will perform the end-to-end steps and generate publication-ready outputs.

Frequently Asked Questions about data-analysis

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

FAQPage Schema
How do I create a reproducible R data analysis workflow from exploration to modelling?▼

To create a reproducible R workflow, you structure scripts in a dedicated directory, save intermediate objects with saveRDS, and store outputs consistently to ensure results can be reviewed later.

What is the best way to generate publication-ready tables and figures in R?▼

The best way to generate publication-ready outputs in R is by using ggplot2 for figures and exporting latex tables suitable for Beamer or Quarto reports directly from your analysis scripts.

How do I structure an end-to-end data analysis project for cross-sectional and panel data?▼

Structuring an end-to-end data analysis project involves enforcing a consistent directory layout, saving R scripts to scripts/R/, and storing generated tables and plots in an output directory for cross-sectional and panel data scenarios.

Can I use ggplot2 to create manuscript-friendly plots for Quarto reports?▼

Yes, you can use ggplot2 to create manuscript-friendly figures that are directly suitable for integration into Quarto and Beamer reports, ensuring your data visualization meets publication standards.

Does this R data analysis workflow support regression analysis and EDA?▼

Yes, the R data analysis workflow supports both exploratory data analysis and regression modelling, executing the full sequence from data loading to producing publication-ready tables and figures.

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