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econ-visualization

Create publication-quality charts and graphs for economics papers.

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

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econ-visualization

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Use econ-visualization to create publication-quality, reproducible figure code for [dataset and variables] as a [chart type], exported in [output format and size], with documented transformations and clear academic labels.

Required inputs

  • [dataset and variables]
  • [chart type]
  • [output format and size]

User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗

SKILL.md

name: econ-visualizationdescription: Create publication-quality charts and graphs for economics papers.workflow_stage: communicationcompatibility:   - claude-code  - cursor  - codex  - gemini-cliauthor: Awesome Econ AI Communityversion: 1.0.0tags:   - visualization  - ggplot2  - charts  - publication

Econ Visualization

Purpose

This skill creates publication-quality figures for economics papers, using clean styling, consistent scales, and export-ready formats.

When to Use

  • Building figures for empirical results and descriptive analysis
  • Standardizing chart style across a paper or presentation
  • Exporting figures to PDF or PNG at journal quality

Instructions

Follow these steps to complete the task:

Step 1: Understand the Context

Before generating any code, ask the user:

  • What is the dataset and key variables?
  • What chart type is needed (line, bar, scatter, event study)?
  • What output format and size are required?

Step 2: Generate the Output

Based on the context, generate code that:

  1. Uses a consistent theme for academic styling
  2. Labels axes and legends clearly
  3. Exports figures at high resolution
  4. Includes reproducible steps for data preparation

Step 3: Verify and Explain

After generating output:

  • Explain how to regenerate or update the plot
  • Suggest alternatives (log scales, faceting, smoothing)
  • Note any data transformations used

Example Prompts

  • "Create an event study plot with confidence intervals"
  • "Plot GDP per capita over time for three countries"
  • "Build a scatter plot with fitted regression line"

Example Output

# ============================================
# Publication-Quality Figure in R
# ============================================
library(tidyverse)

df <- read_csv("data.csv")

ggplot(df, aes(x = year, y = gdp_per_capita, color = country)) +
  geom_line(size = 1) +
  scale_y_continuous(labels = scales::comma) +
  labs(
    title = "GDP per Capita Over Time",
    x = "Year",
    y = "GDP per Capita (USD)",
    color = "Country"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    legend.position = "bottom",
    panel.grid.minor = element_blank()
  )

ggsave("figures/gdp_per_capita.pdf", width = 7, height = 4, dpi = 300)

Requirements

Software

  • R 4.0+ or Python 3.10+

Packages

  • For R: ggplot2, scales, dplyr
  • For Python: matplotlib, seaborn (optional alternative)

Best Practices

  1. Use vector formats (PDF, SVG) for publication
  2. Keep labels concise and readable
  3. Document data filters used in the figure

Common Pitfalls

  • Overcrowded plots without clear labeling
  • Inconsistent scales across figures
  • Exporting low-resolution images

References

Changelog

v1.0.0

  • Initial release