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

• Read .claude/rules/r-code-conventions.md for project standards

• Create R script with proper header (title, author, purpose, inputs, outputs)

• Load required packages at top (library() , never require() )

• Set seed once at top: set.seed(42)

• 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

• saveRDS() for all key objects (regression results, summary tables, processed data)

• Create output/ subdirectories as needed with dir.create(..., recursive = TRUE)

• Run the r-reviewer agent on the generated script:

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

• 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.