# Data Analysis

End-to-end R data analysis pipeline, exploration → cleaning → regression →...

- Repository: https://github.com/pedrohcgs/claude-code-my-workflow (1676 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-09-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add pedrohcgs/claude-code-my-workflow --skill data-analysis -g -a claude-code -y`
Codex: `npx skills add pedrohcgs/claude-code-my-workflow --skill data-analysis -g -a codex -y`
Cursor: `npx skills add pedrohcgs/claude-code-my-workflow --skill data-analysis -g -a cursor -y`
Gemini CLI: `npx skills add pedrohcgs/claude-code-my-workflow --skill data-analysis -g -a gemini-cli -y`

## Description

End-to-end R data analysis pipeline, exploration → cleaning → regression → publication-ready tables and figures. Use when user says "analyze this dataset", "run a regression on X", "explore this CSV", "full analysis workflow", "get me summary stats and a regression", or points at a `.csv`/`.rds`/`.dta` and asks for empirical results. Produces numbered R scripts in `scripts/R/` and outputs to `output/`.

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

# Data Analysis Workflow

Run an end-to-end data analysis in R: load, explore, analyze, and produce publication-ready output.

**Input:** `$ARGUMENTS`, a dataset path (e.g., `data/county_panel.csv`) or a description of the analysis goal (e.g., "regress wages on education with state fixed effects using CPS data").

---

## Constraints

- **Follow R code conventions** in `.claude/rules/r-code-conventions.md`
- **Save all scripts** to `scripts/R/` with descriptive names
- **Save all outputs** (figures, tables, RDS) to `output/`
- **Use `saveRDS()`** for every computed object, Quarto slides may need them
- **Use project theme** for all figures (check for custom theme in `.claude/rules/`)
- **Run r-reviewer** on the generated script before presenting results

---

## Workflow Phases

### Phase 0: Pre-Flight Report

**Before writing any analysis code, produce a Pre-Flight Report** showing you read the inputs. This prevents the common failure mode where the agent hallucinates variable names or skips project conventions.

Output block (in your response to the user, before Phase 1):

```markdown
## Pre-Flight Report

**Dataset:** [path]
- Variables found: [list from head()/names()]
- Rows: [count]
- Key types: [e.g., "outcome=numeric, treatment=binary, state=factor"]
- Missing-data summary: [% missing per key var]

**Project conventions read:**
- `.claude/rules/r-code-conventions.md`, [one-line summary of most relevant rule]
- `.claude/rules/content-invariants.md`, [INV-9, INV-10, INV-11, INV-12 applicable]

**Task interpretation:** [one sentence restating what the user asked for]

**Plan:** [3-5 bullet outline of the R script structure]
```

If any input cannot be read (missing file, unreadable format), stop and ask the user before proceeding.

### Phase 1: Setup and Data Loading

The rest: https://github.com/pedrohcgs/claude-code-my-workflow/blob/HEAD/.claude/skills/data-analysis/SKILL.md