data-analysis

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").

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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

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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):

## 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

1. Create R script with proper header (title, author, purpose, inputs, outputs)
2. Load required packages at top (library(), never require())
3. Set seed once at top in YYYYMMDD format (per r-code-conventions.md), e.g. set.seed(20260415) (INV-9)
4. Load and inspect the dataset