# Full Empirical Analysis Skill R

Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics...

- Repository: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills (4869 stars)
- Category: Testing
- License: see repository
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill-R -g -a claude-code -y`
Codex: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill-R -g -a codex -y`
Cursor: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill-R -g -a cursor -y`
Gemini CLI: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill-R -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills`

## Description

Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem, dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **Defaults to economics empirical-paper style** (AER / QJE / AEJ), every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step R pipeline an applied economist runs on every paper, (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, Hon

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Full Empirical Analysis, Classical R Workflow

This skill is the *canonical* 8-step pipeline an applied economist runs on every empirical paper, written in the **modern tidyverse + econometrics R ecosystem**, `dplyr`/`tidyr`/`haven` for data, `fixest` as the panel/IV/DID workhorse, `did`/`bacondecomp`/`HonestDiD` for modern DID, `rdrobust`/`rddensity` for RD, `Synth`/`gsynth`/`synthdid` for synthetic control, `MatchIt`/`WeightIt`/`cobalt`/`ebal` for matching, `grf`/`DoubleML` for ML causal, `mediation` for causal mediation, `marginaleffects` for post-estimation, `modelsummary`/`kableExtra`/`gt` for publication tables, `ggplot2`/`iplot`/`binsreg` for figures.

**Companion skills**: this is the R sibling of `00-StatsPAI_skill` (Python DSL), `00.1-Full-empirical-analysis-skill` (explicit Python), and `00.2-Full-empirical-analysis-skill_Stata` (Stata `.do`). All four implement the same 8 steps, in their respective ecosystems.

## Philosophy

1. **Tidyverse + fixest, the modern R idioms.** `feols(... | unit + year, cluster = ~unit)`, not Frankenstein-y `lm(y ~ x + factor(unit) + factor(year))`.
2. **Reproducible scripts / Quarto.** Every example below is paste-runnable. `renv` for package locking; `Quarto` (`.qmd`) for combined narrative + code + tables/figures.
3. **8 steps, first-class.** R users historically over-invest in Step 5; this skill treats Steps 1, 4 and 6, 8 as core.
4. **Rich outputs.** Every step yields at least one table or figure, tex/docx/png/pdf.
5. **Progressive disclosure.** `SKILL.md` gives the canonical call per step; [`references/`](references/) holds variant-specific depth.

## SkillOpt-style execution gate

The rest: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/plugins/empirical-analysis-r/skills/pipeline/SKILL.md