Econ Visualization
- 187 installs
- 590 repo stars
- Updated June 23, 2026
- meleantonio/awesome-econ-ai-stuff
Plot time series, distributions, regression fits, and policy counterfactuals with publication-quality charts for papers, dashboards, and stakeholder memos.
About
Builds economics-oriented visualizations—time series, histograms, regression diagnostics, and treatment-effect plots—with matplotlib-style workflows tuned for papers, Beamer decks, and briefings so empirical results are clear, labeled, and reproducible.
- Publication-quality charts
- Time series and distribution plots
- Regression diagnostic figures
- Export for papers and slides
- Consistent econ styling
Econ Visualization by the numbers
- 187 all-time installs (skills.sh)
- +6 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #673 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 187 |
|---|---|
| repo stars | ★ 590 |
| Last updated | June 23, 2026 |
| Repository | meleantonio/awesome-econ-ai-stuff ↗ |
What it does
Plot time series, distributions, regression fits, and policy counterfactuals with publication-quality charts for papers, dashboards, and stakeholder memos.
Files
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
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
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