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review-r

Testing & Quality
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R code review for the sewage project. Checks script structure, reproducibility, function design, figure quality, and professional polish against project conventions (here::here, arrow/parquet, fixest, modelsummary, native pipe). This skill should be used when asked to "review the code", "check my script", or "code review".

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QUICK START

How to use this skill

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  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/41-sticerd-eee-sewage-econometrics-check/skills/review-r/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/review-r/. Do not write files or run scripts until I approve.

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Review R Code

Run a code quality review on R scripts in the sewage project. Produces a report — does NOT edit source files.

Input: $ARGUMENTS — a .R filename, directory path, or all.

Project-Specific Conventions

Script Structure

Pipeline scripts (layers 01-06):

Header block (########) → roxygen description → initialise_environment() → setup_logging() → CONFIG list → functions → main() → conditional execution (sys.nframe() == 0)

Analysis scripts (09_analysis):

Numbered sections with # === separators, inline package loading, direct execution (no main() wrapper)

Required Conventions

  • Paths: here::here() — never relative paths or setwd()
  • Data formats: arrow (parquet) for intermediate/final; DuckDB for large joins
  • Pipe: Native R pipe |> (not %>%)
  • Naming: snake_case for functions/variables, UPPER_SNAKE_CASE for constants
  • Econometrics: fixest::feols() for regressions
  • Tables: modelsummary → LaTeX with tabularray format
  • SE: vcov = "hetero" for heteroskedasticity-robust
  • Factors: forcats::as_factor() / forcats::fct_drop()

Key Files

  • Scripts: scripts/R/01_data_ingestion/ through scripts/R/09_analysis/
  • Utilities: scripts/R/utils/
  • Output: output/{figures,tables,html_plots,regs,log}/

Workflow

Step 1: Identify Scripts

  • If $ARGUMENTS is a specific .R file: review that file
  • If $ARGUMENTS is a directory: review all .R files in that directory
  • If $ARGUMENTS is all: review all scripts in scripts/R/
  • If no argument: ask which scripts to review

Step 2: Review Each Script (9 Categories)

Category 1: Script Structure

  • Pipeline scripts: header block, roxygen, initialise_environment(), CONFIG list, main(), conditional execution
  • Analysis scripts: numbered sections with # === separators
  • Clear separation of concerns

Category 2: Console Hygiene

  • No unnecessary print() / cat() pollution
  • Logging via setup_logging() in pipeline scripts
  • Clean output — only meaningful messages

Category 3: Reproducibility

  • set.seed() where randomness is involved
  • All paths via here::here() — no setwd(), no relative paths, no hardcoded absolute paths
  • No hardcoded values that should be in CONFIG

Category 4: Function Design

  • DRY — no duplicated code blocks
  • Functions at appropriate abstraction level
  • Shared utilities in scripts/R/utils/ where reuse is needed

Category 5: Figure Quality

  • Axis labels present and readable
  • Appropriate dimensions (not too wide/narrow)
  • Consistent theme across plots
  • Alpha transparency for overlapping points
  • Saved to output/figures/ via here::here()

Category 6: Data Persistence

  • Intermediate results saved as parquet (arrow::write_parquet())
  • saveRDS() for R-specific objects
  • No orphaned temporary files

Category 7: Comments

  • Explain why, not what
  • Section headers for navigation
  • No commented-out dead code

Category 8: Error Handling

  • Graceful failures with informative messages in pipeline scripts
  • File existence checks before reading
  • Data validation where appropriate

Category 9: Polish

  • Consistent style (indentation, spacing)
  • No dead code or unused imports
  • Clean namespace — library() calls at top
  • Native pipe |> (not magrittr %>%)

Step 3: Present Summary

## Code Review: [filename/directory]
**Date:** YYYY-MM-DD
**Scripts reviewed:** N

### Issues by Severity
| Script | Critical | Major | Minor |
|--------|----------|-------|-------|
| ... | ... | ... | ... |

### Top 3 Critical Issues
1. ...
2. ...
3. ...

### Conventions Compliance
- [ ] here::here() for all paths
- [ ] Native pipe |>
- [ ] arrow/parquet for data
- [ ] fixest for regressions
- [ ] modelsummary for tables

### Score: XX / 100

Save report to output/log/code_review_[target].md.

Step 4: IMPORTANT

Do NOT edit any source files. Only produce reports. Fixes are applied after user review.

Principles

  • Report only, never edit. The reviewer is a critic, not a creator.
  • Project conventions matter. Flag deviations from the conventions above, not personal preferences.
  • Proportional severity. A missing set.seed() is Major. A missing comment is Minor. Using setwd() is Critical.
  • Language-specific. Check R idioms — vectorised operations over loops, tidyverse patterns, proper use of factors.
  • Pipeline vs analysis distinction. Different structure expectations for pipeline scripts vs analysis scripts.