econ-visualization

name	econ-visualization
description	Creates publication-quality economics figures with consistent academic styling. Use when creating or styling charts.
argument-hint	<chart type> <data source> [description]
allowed-tools	Bash, Read, Write, Edit, Glob, Grep
version	1.0.0
workflow_stage	communication
tags	["visualization","figures","ggplot2","matplotlib","publication-quality"]

Economics Visualization

Create publication-quality figures with consistent academic styling, optimized for economics journals.

Arguments

• $ARGUMENTS — chart type and data description (e.g., "coefficient plot from notebook-02 regressions", "event study DiD results", "time series GDP per capita by country", "scatter income vs growth with fitted line", "RD plot running variable")

Steps

1. Parse the chart type from the arguments. Supported chart types:

   • Scatter plot — bivariate relationships, with optional fitted line and confidence band
   • Time series — one or more series over time, with optional recession shading or event markers
   • Coefficient plot — forest-plot style with point estimates and confidence intervals
   • Event study plot — pre/post treatment coefficients with confidence bands
   • RD plot — regression discontinuity with binned scatter and fitted polynomials both sides of cutoff
   • Distribution — kernel density, histogram, or box plot
   • Bar chart — grouped or stacked, for categorical comparisons
   • Heatmap — correlation matrix or spatial data
   • If the chart type is not recognized, ask the user to describe the desired visualization.

2. Ask the user for the language preference: Python, R, or Stata.

3. Apply project-wide figure standards (from CLAUDE.md):

   • Dimensions: 6 inches wide × 4 inches tall
   • Resolution: 300 DPI
   • Export path: ../images/<label>.png
   • Quarto cell options: #| label: fig-<name> and #| fig-cap: "<caption>"

4. Generate code with academic styling conventions:

   Python (matplotlib/seaborn):

   fig, ax = plt.subplots(figsize=(6, 4))
   # Use clean, minimal style
   ax.spines['top'].set_visible(False)
   ax.spines['right'].set_visible(False)
   ax.set_xlabel("X Label", fontsize=12)
   ax.set_ylabel("Y Label", fontsize=12)
   ax.tick_params(labelsize=10)
   fig.savefig("../images/<label>.png", dpi=300, bbox_inches="tight")
   

   R (ggplot2):

   p <- ggplot(data, aes(x, y)) +
     geom_point() +
     theme_minimal(base_size = 12) +
     theme(
       panel.grid.minor = element_blank(),
       plot.title = element_text(size = 14, face = "bold")
     ) +
     labs(x = "X Label", y = "Y Label")
   ggsave("../images/<label>.png", plot = p, width = 6, height = 4, dpi = 300)
   

   Stata:

   twoway (scatter y x), ///
     scheme(s2color) ///
     xtitle("X Label") ytitle("Y Label")
   quietly graph export "../images/<label>.png", replace width(1800)
   

5. Apply chart-type-specific templates:

   • Coefficient plot: Horizontal forest plot with point estimates as dots, 95% CI as horizontal lines, vertical zero reference line (dashed). Order coefficients by magnitude or logical grouping. Use geom_pointrange() (R) or ax.errorbar() (Python).

   • Event study plot: X-axis = periods relative to treatment (t-3, t-2, ..., t+3). Point estimates with 95% CI error bars. Vertical dashed line at t=0 (treatment). Horizontal dashed line at y=0 (null effect). Normalize t-1 to zero (omitted period). Shade post-treatment region lightly.

   • RD plot: Binned scatter plot (evenly spaced bins or quantile bins). Fitted polynomial curves on each side of the cutoff. Vertical line at the cutoff. Different colors for below/above cutoff. Show the discontinuity visually.

   • Time series: Multiple series with distinct colors from a colorblind-safe palette. Legend placed inside the plot area (top-right or bottom-left to avoid clutter). Optional vertical lines for key events. Date formatting on x-axis.

   • Distribution: Kernel density with bw_method='scott' or histogram with Freedman-Diaconis bins. Vertical dashed lines for mean and median, labeled. If comparing groups, use semi-transparent overlapping densities.

   • Scatter plot: Point opacity at 0.5–0.7 for dense data. Optional OLS fit line with geom_smooth(method="lm") or np.polyfit. Label outliers if relevant. Consider log scales for skewed variables.

   • Heatmap: Diverging color palette (blue-white-red for correlations). Annotate cells with values. Mask the upper triangle for correlation matrices. Use sns.heatmap() (Python) or corrplot (R).

6. Apply universal styling rules:

   • Colors: Use colorblind-safe palettes: Set2, Dark2 (categorical) or viridis, RdBu (sequential/diverging). Never use rainbow.
   • Typography: Axis labels 12pt, tick labels 10pt, title 14pt. Sans-serif font for screen; serif acceptable for print.
   • Grid: Minimal or no gridlines. Never major+minor grids simultaneously.
   • Legend: Inside the plot when possible. No box border around legend.
   • Aspect ratio: Wider than tall (3:2) for most plots. Square for scatter/heatmap.

7. Show the embed shortcode for manuscript integration:

   {{< embed notebooks/<notebook>.qmd#fig-<name> >}}
   

Error handling

• If the data source is not specified, ask the user which notebook or dataset contains the data.
• If the chart type doesn't match the data structure (e.g., event study with no time dimension), suggest an alternative.

Common Pitfalls

• Cluttered legends: Move the legend inside the plot; remove the border; reduce to essential entries only
• Axis labels too small for print: Journal figures are often printed at column width (~3.5 inches). Labels that look fine at 6 inches become unreadable at 3.5. Use 12pt minimum.
• Rainbow color palettes: ~8% of men have color vision deficiency. Use viridis, Set2, or RdBu instead of jet or rainbow.
• 3D charts: Never use 3D bar charts or 3D scatter plots in academic work. They distort perception and add no information.
• Missing units on axes: Every axis must have a unit (%, USD, log points, standard deviations). Bare numbers are ambiguous.
• Inconsistent styling: All figures in the same paper should use the same font sizes, color palette, and grid style. Establish a style early and reuse it.
• Overplotting: For large datasets, use transparency (alpha=0.3), jitter, hex bins, or contour plots instead of opaque points.

References

• Schwabish (2014) "An Economist's Guide to Visualizing Data" — Journal of Economic Perspectives
• Tufte (2001) "The Visual Display of Quantitative Information"
• Wilke (2019) "Fundamentals of Data Visualization"