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Felpix-Studios
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data-analysis

Automates end-to-end data analysis in R or Python with reproducible outputs.

8|2|Updated Apr 2, 2026
Search Tags:#python#data#visualization#regression#r#analyze#eda
One-click install
npx skills add https://github.com/Felpix-Studios/social-science-research --skill data-analysis-felpix-studios
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/Felpix-Studios/social-science-research/tree/main/skills/data-analysis
Command: npx skills add https://github.com/Felpix-Studios/social-science-research --skill data-analysis-felpix-studios

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates and guides the complete data analysis workflow in R or Python, enabling users to move from data loading to regression, visualization, and publication-ready outputs with structured steps and reproducible practices.

Core Features & Use Cases

  • Exploratory data analysis: summary statistics, missingness assessment, data types, and initial visualizations.
  • Modeling and regression workflows in R or Python: from simple OLS to advanced panel/data models.
  • Publication-ready outputs: tables and figures exported in LaTeX, HTML, and image formats, with reproducible project structure.
  • Use Case: Apply the workflow to a dataset to reproduce a research question, estimate effects, and prepare a manuscript-ready figure.

Quick Start

Load your dataset and run the end-to-end analysis pipeline using your preferred language.

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 data analysis workflow from exploration to publication-ready outputs?▼

Exploratory data analysis includes summary statistics, missingness assessment, data type checks, and initial visualizations to understand dataset structure. It guides users through data loading and initial exploration before modeling in R or Python.

Can I use Python and R for regression modeling and data visualization in the same workflow?▼

Yes, both Python and R are supported for regression modeling ranging from simple OLS to advanced panel data models. The workflow also handles data visualization and produces reproducible project structures for manuscript preparation.

What is the best way to prepare publication-ready figures and tables from statistical models?▼

To prepare publication-ready outputs, use a workflow that exports tables and figures in LaTeX, HTML, and image formats. It enforces reproducible project structures and modular scripting conventions for manuscript-ready results.

Does this data analysis workflow enforce reproducibility for research datasets?▼

Yes, the workflow enforces reproducibility by requiring modular scripting conventions and output packaging for tables and figures. This ensures research datasets can be reloaded and analyzed with consistent, publication-ready results.

How do I assess missingness and data types during exploratory data analysis?▼

Exploratory data analysis assesses missingness and data types by generating summary statistics and initial visualizations. This structured step in R or Python helps validate dataset quality before running regression workflows.

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