Data Analysis Workflow

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

Input: A dataset path (e.g., data/county_panel.csv) or a description of the analysis goal.

Constraints

• •Follow R code conventions in code/AGENTS.md (Codex) or .claude/rules/r-code-conventions.md (Claude)
• •Save all scripts to the appropriate code/[task_group]/ directory
• •Save all outputs to output/ (figures, tables, numbers subdirs)
• •Use saveRDS() for every computed object
• •Use project theme for all figures
• •Run /review-r on the generated script before presenting results

Workflow Phases

Phase 1: Setup and Data Loading

1. •Read code conventions for project standards
2. •Create R script with proper header (title, author, purpose, inputs, outputs)
3. •Load required packages at top (library(), never require())
4. •Set seed once at top in YYYYMMDD format: set.seed(20260211)
5. •Load and inspect the dataset

Phase 2: Exploratory Data Analysis

Generate diagnostic outputs:

• •Summary statistics: summary(), missingness rates, variable types
• •Distributions: Histograms for key continuous variables
• •Relationships: Scatter plots, correlation matrices
• •Time patterns: If panel data, plot trends over time
• •Group comparisons: If treatment/control, compare pre-treatment means

Save all diagnostic figures to output/figures/.

Phase 3: Main Analysis

Based on the research question:

• •Regression analysis: Use fixest for panel data, lm/glm for cross-section
• •Standard errors: Cluster at the appropriate level (document why)
• •Multiple specifications: Start simple, progressively add controls
• •Effect sizes: Report standardized effects alongside raw coefficients

Phase 4: Publication-Ready Output

Tables:

• •Use modelsummary for regression tables (preferred) or stargazer
• •Include all standard elements: coefficients, SEs, significance stars, N, R-squared
• •Export as .tex for LaTeX inclusion and .html for quick viewing

Figures:

• •Use ggplot2 with project theme
• •Set bg = "transparent" for LaTeX compatibility
• •Include proper axis labels (sentence case, units)
• •Export with explicit dimensions: ggsave(width = X, height = Y)
• •Save as both .pdf and .png

Phase 5: Save and Review

1. •saveRDS() for all key objects
2. •Rely on the Makefile for directory creation (do not call dir.create() in scripts)
3. •Run /review-r on the generated script
4. •Address any Critical or High issues from the review

Script Structure

# ============================================================
# [Descriptive Title]
# Author: [from project context]
# Purpose: [What this script does]
# Inputs: [Data files]
# Outputs: [Figures, tables, RDS files]
# ============================================================

# 0. Setup ----
library(tidyverse)
library(fixest)
library(modelsummary)

set.seed(20260211)  # YYYYMMDD format

# Note: output directories are created by the Makefile, not the script

# 1. Data Loading ----
# 2. Exploratory Analysis ----
# 3. Main Analysis ----
# 4. Tables and Figures ----
# 5. Export ----

Important

• •Reproduce, don't guess. If the user specifies a regression, run exactly that.
• •Show your work. Print summary statistics before jumping to regression.
• •Check for issues. Look for multicollinearity, outliers, perfect prediction.
• •Use relative paths. All paths relative to repository root.
• •No hardcoded values. Use variables for sample restrictions, date ranges, etc.