Data Analysis Workflow

Run an end-to-end data analysis in R or Python: 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").

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Phase 0: Choose Language

Determine language from $ARGUMENTS or ask the user:

• User mentions tidyverse, fixest, lm, .R context → R track
• User mentions pandas, statsmodels, sklearn, .py or .ipynb context → Python track
• Dataset is .csv/.parquet with no language cue → use AskUserQuestion with a single-select menu:
  • header: "Language"
  • question: "Which language should I use for this analysis?"
  • options:
    • label: "R (Recommended)", description: "tidyverse, fixest, ggplot2 — full plugin support with coding conventions and R reviewer"
    • label: "Python", description: "pandas, statsmodels — supported for analysis scripts and figures"
    • label: "Both", description: "R for figures and tables, Python for data processing"

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R Track

Constraints

• Follow rules/r-code-conventions.md for all standards
• Save scripts to scripts/R/ with descriptive names
• Save all outputs (figures, tables, RDS) to output/
• Use saveRDS() for every computed object
• Run r-reviewer on the generated script before presenting results

Phase 1: Setup and Data Loading

1. Create R script with proper header (title, author, purpose, inputs, outputs)
2. Load required packages at top (library(), never require())
3. Set seed once at top: set.seed(42)
4. Create output directories: dir.create("output/analysis", recursive = TRUE, showWarnings = FALSE)
5. Load and inspect the dataset

Phase 2: Exploratory Data Analysis

• summary(), missingness rates, variable types
• Histograms for key continuous variables
• Scatter plots, correlation matrices
• Panel trends, pre-treatment comparisons if applicable
• Save all diagnostic figures to output/diagnostics/

Phase 3: Main Analysis

• Panel data: use fixest; cross-section: use lm/glm
• Cluster SEs at the appropriate level (document why)
• Multiple specifications: start simple, progressively add controls
• Report standardized effects alongside raw coefficients

Phase 4: Publication-Ready Output

Tables: modelsummary (preferred) or stargazer — export .tex and .html Figures: ggplot2 with project theme; explicit ggsave(width = X, height = Y); save as .pdf and .png; add bg = "transparent" only if output is for Beamer slides

Phase 5: Save and Review

1. saveRDS() for all key objects
2. Run the r-reviewer agent: "Review the script at scripts/R/[script_name].R"
3. Address Critical and High issues before presenting results

R Script 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 ----
# 2. Exploratory Analysis ----
# 3. Main Analysis ----
# 4. Tables and Figures ----
# 5. Export ----

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Python Track

Constraints

• Save scripts to scripts/python/ with descriptive names
• Save all outputs (figures, tables, pickles) to output/
• Use joblib.dump() for model objects; .to_parquet() for DataFrames
• Use pathlib.Path for all file paths — never hardcode absolute paths
• Set random seeds at the top of the script

Phase 1: Setup and Data Loading

1. Create Python script with header (title, author, purpose, inputs, outputs)
2. Import all packages at the top of the file
3. Set seeds: np.random.seed(42) and random.seed(42)
4. Create output directories: Path("output/analysis").mkdir(parents=True, exist_ok=True)
5. Load and inspect the dataset with pandas

Phase 2: Exploratory Data Analysis

• df.describe(), df.isnull().sum(), df.dtypes
• Histograms and distributions with matplotlib/seaborn
• Scatter plots and correlation matrices
• Save diagnostic figures to output/diagnostics/
• Save summary stats: df.describe().to_csv("output/diagnostics/summary_stats.csv")

Phase 3: Main Analysis

• Cross-section OLS: smf.ols("y ~ x", data=df).fit(cov_type="HC3")
• Panel data: PanelOLS from linearmodels with cluster-robust SEs
• Multiple specifications: build incrementally
• Document SE choice with a comment

Phase 4: Publication-Ready Output

Tables: Format with pandas and export via .to_latex() or stargazer (Python port) Figures: matplotlib/seaborn; explicit fig.savefig(path, dpi=300, bbox_inches="tight"); save as .pdf and .png

Phase 5: Save and Review

1. joblib.dump(model, "output/model.pkl") for fitted models
2. df_results.to_parquet("output/results.parquet") for DataFrames
3. Review the script manually against the Python checklist below before presenting

Python Script Template

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

import random
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import seaborn as sns
import joblib
from pathlib import Path

# Seeds
np.random.seed(42)
random.seed(42)

# Output directories
Path("output/analysis").mkdir(parents=True, exist_ok=True)
Path("output/figures").mkdir(parents=True, exist_ok=True)

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

Python Quality Checklist

[ ] All imports at top
[ ] Random seeds set (numpy + stdlib)
[ ] All paths use pathlib.Path — no hardcoded strings
[ ] Output directories created with mkdir(exist_ok=True)
[ ] Figures saved with explicit dpi=300, bbox_inches="tight"
[ ] Model objects saved with joblib.dump()
[ ] DataFrames saved as parquet
[ ] Comments explain WHY, not WHAT

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Shared Principles

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