caixinyu2017-star/xixi/econ-visualization

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

• ggplot2 documentation
• Tufte (2001) The Visual Display of Quantitative Information

Changelog

v1.0.0

• Initial release