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/ holds variant-specific depth.

SkillOpt-style execution gate

Use this long playbook as a seed skill, not as a script to exhaustively apply. SkillOpt discipline: treat each local R/Quarto change as a candidate patch that must beat a selection check and survive a held-out check before it becomes reusable boilerplate. Before writing or revising an R script/Quarto workflow, compress the user's request into a task-local best_skill card: