Do I need R installed locally to use this?

While the skill can generate R code files for you, it works best if R is installed locally so it can execute scripts, verify results, and check for errors in real-time.

Does this skill help with data cleaning?

Yes, Phase 1 of the workflow focuses on data familiarization, quality assessment, and cleaning to ensure the dataset supports the chosen identification strategy.

What statistical methods does r-analyst support?

It supports a wide range of econometric methods including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RD), matching, panel fixed effects, and Bayesian sensitivity analysis.

How does it handle reproducibility?

It enforces a standardized project structure with separate folders for raw data, cleaning scripts, and analysis code, ensuring all findings can be replicated from start to finish.

Can it generate tables for my research paper?

Absolutely. It uses packages like modelsummary and etable to produce publication-quality regression tables and descriptive statistics (Table 1) suitable for top-tier journals.

R Statistical Analyst

Name: R Statistical Analyst
Author: nealcaren

bynealcaren

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데이터 과학 및 ML

Guides researchers through a systematic, phased workflow to produce publication-ready sociological research and quantitative R analysis.

소개

r-analyst is a specialized Claude Code skill designed for academic researchers and data scientists conducting quantitative sociological research. It facilitates a rigorous six-phase analysis process—Research Design, Data Familiarization, Model Specification, Main Analysis, Robustness & Sensitivity, and Output—to ensure causal identification and reproducibility. By leveraging expert R programming patterns and social science methodologies, it helps users implement complex strategies like Difference-in-Differences (DiD), Instrumental Variables (IV), and panel methods while automatically generating journal-standard tables and visualizations.

주요 기능

Enforced reproducibility through standardized folder structures and master scripts
Structured 6-phase workflow ensuring design-before-estimation methodology
Built-in sensitivity analysis and robustness testing protocols
Automated generation of publication-quality tables and coefficient plots
Expert implementation of DiD, IV, RD, and synthetic control methods
2 GitHub stars

사용 사례

Automating complex sensitivity analysis and robustness checks for existing datasets
Executing rigorous causal inference designs for policy evaluation and research
Preparing quantitative manuscripts for peer-reviewed social science journals

소개

주요 기능

Enforced reproducibility through standardized folder structures and master scripts
Structured 6-phase workflow ensuring design-before-estimation methodology
Built-in sensitivity analysis and robustness testing protocols
Automated generation of publication-quality tables and coefficient plots
Expert implementation of DiD, IV, RD, and synthetic control methods
2 GitHub stars

사용 사례

Automating complex sensitivity analysis and robustness checks for existing datasets
Executing rigorous causal inference designs for policy evaluation and research
Preparing quantitative manuscripts for peer-reviewed social science journals