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data-analysis

Run end-to-end R analysis from data loading to regression tables and figures.

203|27|Updated Apr 19, 2026
Search Tags:#regression#r#exploratory data analysis#reproducibility#publication-ready#modelsummary#panel data
One-click install
npx skills add https://github.com/franklee16/academic-research-skills --skill data-analysis-franklee16
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/franklee16/academic-research-skills/tree/main/data-analysis/data-analysis
Command: npx skills add https://github.com/franklee16/academic-research-skills --skill data-analysis-franklee16

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns an academic research question into an end-to-end R analysis by coordinating data loading, exploratory diagnostics, regression modeling, and publication-ready tables and figures.

Core Features & Use Cases

  • End-to-end R workflow: guides setup, exploratory data analysis, main estimation, and exportable outputs in a consistent structure.
  • Publication-ready outputs: produces regression tables (via modelsummary/stargazer) and figures (ggplot2 with project theme) in export-friendly formats.
  • Reproducible artifacts: saves computed objects with saveRDS(), organizes outputs under output/, and enforces R code conventions for repeatability.
  • Use Case: You have a county panel CSV and need to regress wages on education with fixed effects, generate diagnostics, and export tables/figures for a paper or report.

Quick Start

Ask the AI to run the data-analysis skill to analyze a dataset you provide and produce regression tables and diagnostic figures following the repository workflow.

Frequently Asked Questions about data-analysis

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

FAQPage Schema
How do I run an end-to-end R regression analysis and generate publication-ready tables?▼

Generate publication-ready regression tables in R by moving from dataset loading and exploratory diagnostics to model estimation, then exporting results via modelsummary or stargazer into LaTeX and HTML formats.

How do I create reproducible panel data regressions with fixed effects in R?▼

Create reproducible panel data regressions in R by estimating your fixed effects model, saving all computed objects with saveRDS(), and organizing diagnostic plots and scripts under a dedicated output directory.

Can I export ggplot2 diagnostic figures and modelsummary tables for an academic paper?▼

Yes, you can export ggplot2 diagnostic figures and modelsummary tables for academic papers by applying a project theme to your visuals and saving regression outputs in export-friendly formats like LaTeX and HTML.

What is the best way to structure exploratory data analysis before running a cross-sectional regression in R?▼

The best way to structure exploratory data analysis before a cross-sectional regression in R is to follow a consistent workflow that guides setup, generates diagnostic plots, and computes intermediate objects prior to main estimation.

Does this R data analysis workflow enforce specific code conventions for repeatability?▼

Yes, this R data analysis workflow enforces code conventions for repeatability by requiring adherence to repository R conventions, saving all intermediate and final objects via saveRDS(), and generating reviewer-reviewed scripts.

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