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data-analysis

End-to-end R data analysis workflow from exploration through regression to publication-ready tables and figures.

Published by brycewang-stanford ↗brycewang-stanford/Auto-Empirical-Research-Skills

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data-analysis

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Use data-analysis to Using the R code conventions in `.claude/rules/r-code-conventions.md`, perform an end-to-end R data analysis of [dataset path] to achieve [analysis goal], producing exploratory diagnostics, regression specifications, publication-ready tables and figures, and a script reviewed by the r-reviewer agent.

Required inputs

  • [dataset path]
  • [analysis goal]

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SKILL.md

name: data-analysisdescription: End-to-end R data analysis workflow from exploration through regression to publication-ready tables and figures.disable-model-invocation: trueargument-hint: "[dataset path or description of analysis goal]"allowed-tools: ["Read", "Grep", "Glob", "Write", "Edit", "Bash", "Task"]

Data Analysis

Run an end-to-end data analysis in R through 5 phases.

Phase 1: Setup and Data Loading

  • Read R code conventions from .claude/rules/r-code-conventions.md
  • Create script with proper header (title, author, purpose, inputs, outputs)
  • Load packages with library() (never require())
  • Set set.seed() if stochastic elements are involved
  • Load data using relative paths

Phase 2: Exploratory Data Analysis

  • Summary statistics (mean, sd, min, max, N)
  • Missingness patterns
  • Distributions of key variables
  • Bivariate relationships
  • Time patterns (if panel data)
  • Group comparisons
  • Save diagnostics to output/diagnostics/

Phase 3: Main Analysis

  • Use fixest for panel data with fixed effects (see /econometrics-r for detailed patterns)
  • Use lm/glm for cross-section
  • Cluster standard errors appropriately
  • Run multiple specifications (simple to complex)
  • Report both statistical and economic significance
  • For causal inference (IV, DiD, RDD), see /causal-inference-r for diagnostics and modern estimators
  • For spatial econometrics, see /econometrics-r for spdep/spatialreg patterns

Phase 4: Publication-Ready Output

  • Tables: modelsummary (preferred) or stargazer, export as .tex and .html
  • Figures: ggplot2 with project theme, explicit dimensions, white background, 300 DPI
  • Export figures as .pdf and .png

Phase 5: Save and Review

  • saveRDS() for all computed objects
  • Run r-reviewer agent on generated script
  • Address Critical/High issues

Script Structure Template

# ==============================================================================
# Title: [Analysis Name]
# Author: [Name]
# Date: [YYYY-MM-DD]
# Purpose: [What this script does]
# Inputs: [Data files]
# Outputs: [Tables, figures, RDS files]
# ==============================================================================

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

# 1. Data Loading ----

# 2. Exploratory Analysis ----

# 3. Main Analysis ----

# 4. Tables and Figures ----

# 5. Export ----

All scripts saved to scripts/R/, all outputs to output/.