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 1: Setup and Data Loading

1. Read

   .claude/rules/r-code-conventions.md

   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:

   set.seed(42)

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/diagnostics/

.

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 Beamer 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 (regression results, summary tables, processed data)
2. Create

   output/

   subdirectories as needed with

   dir.create(..., recursive = TRUE)

3. Run the r-reviewer agent on the generated script:

Delegate to the r-reviewer agent:
"Review the script at scripts/R/[script_name].R"

4. Address any Critical or High issues from the review.

---

Script Structure

Follow this template:

# ============================================================
# [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(42)

dir.create("output/analysis", recursive = TRUE, showWarnings = FALSE)

# 1. Data Loading ----
# [Load and clean data]

# 2. Exploratory Analysis ----
# [Summary stats, diagnostic plots]

# 3. Main Analysis ----
# [Regressions, estimation]

# 4. Tables and Figures ----
# [Publication-ready output]

# 5. Export ----
# [saveRDS for all objects, ggsave for all figures]

---

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.