Review R

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.