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:

• Uses a consistent theme for academic styling

• Labels axes and legends clearly

• Exports figures at high resolution

• 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

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Publication-Quality Figure in R

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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

• Use vector formats (PDF, SVG) for publication

• Keep labels concise and readable

• Document data filters used in the figure

Common Pitfalls

• Overcrowded plots without clear labeling

• Inconsistent scales across figures

• Exporting low-resolution images

References

• ggplot2 documentation

• Tufte (2001) The Visual Display of Quantitative Information

Changelog

v1.0.0

• Initial release