data-analysis

Execute R data analysis workflows with regression modeling and LaTeX-compatible outputs.

6|14|Updated May 7, 2019

Search Tags:#data analysis #regression #exploratory data analysis #tables #R #figures

Ready to run

npx skills add https://github.com/irudik/repo-template --skill data-analysis-irudik

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/irudik/repo-template/tree/main/.agents/skills/data-analysis
Command: npx skills add https://github.com/irudik/repo-template --skill data-analysis-irudik

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the entire process of data analysis in R, from initial data loading and exploration to generating publication-ready tables and figures.

Core Features & Use Cases

Comprehensive Analysis: Handles data loading, exploratory analysis, regression modeling, and output generation.
Publication Quality: Produces tables using modelsummary and figures using ggplot2 with a project theme.
Reproducibility: Enforces R code conventions, uses saveRDS for outputs, and relies on Makefiles for directory management.
Use Case: Analyze a county-level panel dataset to estimate the impact of a policy, generating regression tables and trend plots for a research paper.

Quick Start

Run the data-analysis skill to perform an end-to-end R analysis on the dataset located at data/county_panel.csv.

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 data analysis workflow from data loading to regression?▼

An end-to-end R data analysis workflow automates data loading, exploratory analysis, regression modeling, and output generation. It uses R to handle cross-sectional analysis with lm/glm and outputs publication-ready tables and figures.

What's the best way to generate publication-ready regression tables in R?▼

To generate publication-ready regression tables in R, use the modelsummary package to format model outputs. This workflow creates LaTeX-compatible formats and saves outputs using saveRDS, ensuring reproducibility for research papers.

Can I use R to estimate policy impact on a county-level panel dataset?▼

Yes, you can estimate policy impact on a county-level panel dataset in R using the fixest package for panel data analysis. This approach supports generating regression tables and trend plots for research paper publication.

Does this R data analysis workflow support cross-sectional and panel data?▼

Yes, this R data analysis workflow supports both cross-sectional and panel data. It handles cross-sectional analysis using lm and glm functions, while panel data analysis is executed using the fixest package.

How do I create custom figures for an exploratory data analysis in R?▼

To create custom figures for exploratory data analysis in R, use ggplot2 with a project theme. This workflow generates publication-ready visualizations and manages directory structures using Makefiles for reproducible outputs.

Why use Makefiles and saveRDS for R data analysis reproducibility?▼

Using Makefiles and saveRDS for R data analysis enforces code conventions and ensures reproducibility. Makefiles manage directory structures while saveRDS preserves output objects, creating reliable workflows for publication-ready results.