`/econ-visualization`¶

Code-produced publication figures in ggplot2, matplotlib/seaborn, or Stata twoway/coefplot, exported as vector PDF straight into the paper's figs/ folder, with a chart-type decision tree (event-study coefficient plots, balance plots, recession-shaded time series, binscatters, density plots, choropleths) and colourblind-safe Okabe-Ito or viridis palettes — never red-green for treatment versus control. Follows DIME's Reviewing Graphs checklist and separates caption-dependent paper figures from standalone visuals that need an in-figure title and a source line.

Pack: Econ Agent Skills (Jonas Weinert)

Category: figures

Field: economics

License:

CC0-1.0 per the repo LICENSE file and README ("CC0 1.0 — public domain"); GitHub's API reports the licence as NOASSERTION / "Other" — record both

Updated: 2026-05-05

Stages: data-analysis · paper-drafting

↗ view SKILL.md on source · GitHub stars

Economics Visualization¶

Generate publication-quality figures that come out of code and \includegraphics{} cleanly into the paper or \frame{} into the slides. The default style follows DIME's Data Visualization and Reviewing Graphs checklist guidance.

Operating Principles¶

Figures are produced, never screenshotted. Same DIME four-tier replicability as tables: full replicability means the script writes a vector file (.pdf/.eps/.svg) directly to paper/figs/.
Vector first. Save as PDF (or EPS for some journals; SVG for web). PNG only for raster content (maps, photos, screenshots).
Color discipline. Use a colorblind-safe palette by default (Okabe-Ito, viridis); reserve color for information; ensure the figure still reads in grayscale; never red-green for treatment-vs-control.
Audience-aware annotation. A figure embedded in a paper relies on the caption; a standalone figure (slide, dashboard, blog) needs an in-figure title, takeaway annotation, and source citation.
Same dimensions everywhere. Define one figure-size convention per project (e.g. width = 6.5 in, height = 4 in for paper figures; width = 10 in, height = 5 in for 16:9 slides) so every figure aligns visually.

Decision Policy¶

This skill follows the repo-wide Agent Policy.

ASK before proceeding (blocking):

The single point the figure makes. (One figure, one claim.)
Audience: paper figure (rely on caption) vs standalone (needs in-figure title + source).
Chart type when more than one would work (e.g. event-study coefficient plot vs forest plot).
Color encoding — does color carry information, or is it decorative.

DEFAULT + flag (use this default; tell the user how to override):

Okabe-Ito colorblind-safe palette; never red-green for treatment/control.
Vector PDF (device = cairo_pdf in R; default in matplotlib) for paper figures.
Paper figure dimensions width = 6.5 in, height = 4 in; slide figures width = 10 in, height = 5.5 in.
theme_paper() in R / paper-wide rcParams in matplotlib / scheme(white_tableau) in Stata.
Output to paper/figs/<name>.pdf; the paper \includegraphics{} directly.

DOCUMENT and proceed (write into the figure script header):

Sample / filter applied for this specific figure (when different from the main estimation sample).
Source of any external macro variable (e.g. NBER recession dates).
Color and shape encoding choices for any non-default series.

PROCEED items: figures produced by code (never screenshotted); save vector format; here::here() / pathlib for paths; one script per figure; rebuild path runs figure scripts before LaTeX.

Pre-flight Checklist¶

Before generating code, confirm with the user — and write the answers in the script header:

Question the figure answers. A figure should make exactly one point.
Chart type. Line / bar / scatter / coefficient plot / event study / binscatter / map / density / heatmap?
Data source and sample. Where does the data come from; what filters apply?
Audience. Paper figure (rely on caption) or standalone (needs title + source)?
Output target. PDF for paper; PDF or PNG for slides; SVG for web.
Dimensions. Width/height in inches; affects font sizing.
Color encoding. Is color carrying information (yes → palette choice matters) or is it decorative (no → use a single accent color)?

Decision Tree (chart type by question)¶

Text Only

Comparing two groups over time
└── Two-line plot with vertical reference line at the event.
    Add a shaded confidence band if you have one.

Treatment effect dynamics (event study)
└── Coefficient plot: x = relative period, y = coefficient,
    error bars = 95% CI, vertical line at -1 (omitted period).

Magnitude of one effect across many specifications
└── Coefficient plot: y axis lists specifications, x axis shows
    point estimate + 95% CI; vertical line at 0.

Distribution of a variable in two groups
└── Overlapping density plots, or a violin plot if N is large.

Continuous-X relationship (with many obs)
└── Binscatter (binsreg in Stata; binsreg / binscatter in R).

Heterogeneity across categories
└── Forest plot (coefplot per category, sorted by point estimate).

Geographic variation
└── Choropleth (sf + ggplot2 in R; geopandas in Python; spmap in Stata).

Cross-section relationship
└── Scatter + line of best fit; consider log scales for skewed vars.

Time series with macro events
└── Line plot with shaded recession bars (NBER dates) and
    annotated key events.

Project Layout¶

Text Only

paper/
├── paper.tex
├── figs/                       # all .pdf figures; never hand-edited
│   ├── fig_event_study.pdf
│   ├── fig_balance.pdf
│   └── fig_time_series.pdf
└── code/
    ├── r/
    │   └── make_figures.R
    └── stata/
        └── make_figures.do

In the paper:

TeX

\begin{figure}[htbp]\centering
  \includegraphics[width=0.85\textwidth]{figs/fig_event_study.pdf}
  \caption{Event study of treatment effect on outcome Y.}
  \label{fig:event}
\end{figure}

Color Palettes (colorblind-safe defaults)¶

Text Only

R, ggplot2:
  scale_color_manual(values = c("#0072B2", "#D55E00", "#009E73",
                                "#F0E442", "#CC79A7", "#56B4E9"))
  # Okabe-Ito, used by default in scientific publishing.

Python, matplotlib:
  plt.rcParams["axes.prop_cycle"] = cycler(
      color = ["#0072B2", "#D55E00", "#009E73", "#F0E442",
               "#CC79A7", "#56B4E9"])

Sequential numeric (viridis):
  ggplot2: scale_*_viridis_c()
  matplotlib: cmap = "viridis"
  Stata: graph twoway, ... scheme(white_tableau)

Never use a red-green encoding for treatment/control — fails for ~8% of male readers.

Output Skeleton (R, ggplot2)¶

S

## make_figures.R header
## Project: ProjectABC
## Inputs : data/processed/analysis.parquet
## Outputs: paper/figs/fig_event_study.pdf
## Author : First Last
## Notes  : One figure per script section. Run as a whole.

library(ggplot2)
library(arrow)
library(here)

FIGS <- here("paper", "figs")
dir.create(FIGS, recursive = TRUE, showWarnings = FALSE)

theme_paper <- function(base_size = 11) {
  theme_minimal(base_size = base_size) +
    theme(panel.grid.minor = element_blank(),
          plot.title       = element_text(face = "plain"),
          plot.title.position = "plot",
          legend.position  = "bottom")
}

## (figure code)

ggsave(file.path(FIGS, "fig_event_study.pdf"),
       width = 6.5, height = 4, units = "in", device = cairo_pdf)

Common Pitfalls¶

Default ggplot/matplotlib themes for journal figures (gray panels, busy gridlines). Strip them.
3D bar charts and pie charts. Almost never the right choice.
Two y-axes encoding different units. Hard to interpret; consider faceting instead.
Tiny axis text on a paper figure that becomes unreadable when the page is printed.
Inconsistent axis ranges between related figures. Force shared limits.
Using "rainbow" colormaps (jet) — perceptually misleading. Use viridis/cividis.
Forgetting to label units (Percent, USD, Log GDP).
Including a legend with one entry. Use a direct title instead.
Saving as PNG for a paper figure. Use PDF/EPS so the figure scales without pixelation.
Pasting a figure that requires the audience to read tiny coefficient labels — replace with a coefficient plot.

Additional Resources¶

reference.md — extended patterns: event-study coefplot recipes, binscatter, recession-shaded time series, balance plot, choropleth maps, faceting.
examples/ — runnable scripts:
examples/event_study_ggplot.R — coefficient plot from fixest::iplot data
examples/balance_plot_ggplot.R — standardized differences plot
examples/time_series_recessions.R — line plot with shaded NBER recessions
examples/binscatter_ggplot.R — binsreg-style scatter
examples/event_study_matplotlib.py — same in Python
examples/coefplot_stata.do — Stata coefplot from eststo output
examples/twoway_stata.do — Stata twoway chart with shaded periods
examples/theme_paper.R — paper-wide ggplot2 theme

Requirements¶

R: ggplot2, arrow, dplyr, here, viridis, sf, binsreg (optional).
Python: matplotlib, seaborn, pandas, pyarrow, geopandas (for maps).
Stata: coefplot, binsreg, spmap, colorpalette (in palettes package).
LaTeX: graphicx, caption, subcaption.

References¶

DIME¶

DIME Analytics, Data Visualization.
DIME Analytics, Checklist: Reviewing Graphs.
DIME Analytics, Exporting Analysis.
DIME Analytics, Stata Visual Library for Impact Evaluation.

Style and Theory¶

Tufte (2001), The Visual Display of Quantitative Information.
Cleveland (1993), Visualizing Data.
Wilke (2019), Fundamentals of Data Visualization — https://clauswilke.com/dataviz/.
Healy (2018), Data Visualization: A Practical Introduction — https://socviz.co/.
Datawrapper, What to Consider When Choosing Colors for Data Visualization.
Okabe & Ito, Color Universal Design palette.

Tools¶

ggplot2 — https://ggplot2.tidyverse.org/.
matplotlib gallery — https://matplotlib.org/stable/gallery/.
Stata coefplot (Jann) — https://repec.sowi.unibe.ch/stata/coefplot/.

/econ-visualization¶

Economics Visualization¶

Operating Principles¶

Decision Policy¶

Pre-flight Checklist¶

Decision Tree (chart type by question)¶

Project Layout¶

Color Palettes (colorblind-safe defaults)¶

Output Skeleton (R, ggplot2)¶

Common Pitfalls¶

Additional Resources¶

Requirements¶

References¶

DIME¶

Style and Theory¶

Tools¶

`/econ-visualization`¶