Figure Designer
Applies to any figure type in a social-science manuscript. For figures whose interpretation depends on method-specific standards, also consult the relevant sibling skill (conjoint-design, conjoint-diagnostics, list-experiment, topic-modeling, text-classification, vlm-ocr). For end-stage QA on a finished figure set, hand off to figure-table-audit.
A figure is ready when it makes one comparison legible, can be read without the surrounding text, and can be regenerated from a script.
Instructions
1. State the comparison before drawing
Write down, in one sentence, what the figure should let the reader see (for example, "The treatment effect is larger among low-information respondents"). If the comparison does not fit in one sentence, the figure has too many goals; split it into panels or separate figures.
2. Choose the chart type from the comparison
Match the geometry to the comparison, not to the data type. Two defaults that are often missed:
- Effect estimates compared across models or subgroups go in a coefficient plot (point and interval), not a regression table.
- Many small comparisons go in small multiples on a shared scale, not in separate figures.
Pie, donut, 3D, and dual-axis charts do not belong in academic figures.
3. Pick scales and axes deliberately
- Label both axes with the quantity and its units: "Household income (USD, 2020)", not "Income".
- For percentages, write
0.00–1.00 or 0%–100% consistently — do not mix within a figure.
- Use a log scale when the data span more than two orders of magnitude or when a multiplicative effect is the substantive story, and label the scale as log.
- Anchor axes at zero only when zero is meaningful for the comparison (counts, proportions). Deviations, differences, and z-scores need no zero baseline.
- Use the same y-axis range across panels of small multiples unless the claim is explicitly about within-panel scale.
- Reverse the y-axis only when the substantive direction demands it (e.g., rank where 1 is "best") and label the reversal in the caption.
4. Choose color with intent
- Sequential (light → dark): for ordered/quantitative variables (income brackets, time, density).
- Diverging (red ↔ blue, etc.): for variables with a meaningful midpoint (effect sign, deviation from baseline).
- Categorical: for unordered groups; use a colorblind-safe palette (Okabe-Ito, viridis discrete, ColorBrewer Set2/Dark2).
- Test grayscale: convert the figure and check that the comparison still reads. If it does not, add shape, line type, or direct labels.
- Encoding one variable in both color and shape is fine as an accessibility aid; keep the legend consistent with it.
- Replace default
rainbow / jet ramps, which are not perceptually uniform.
5. Match legend order to the visual order in the chart
Legend order mirrors the data's visual order, so the reader can match series without scanning back and forth:
- For lines, areas, or stacked elements arranged top-to-bottom in the plot at the rightmost x-value, list legend entries in the same top-to-bottom order.
- For bars or categories arranged left-to-right, use a horizontal legend listed left-to-right in the same order.
- For panels (small multiples), follow the panel grid: rows top-to-bottom, columns left-to-right.
- Do not alphabetize a legend when the data have a natural order (time, magnitude, treatment intensity, ordinal scale). ggplot2 and similar libraries sort character variables alphabetically by default, and the result is a frequent cause of misreading.
- With five or fewer series, label lines or bars directly at their ends instead of using a legend.
- When a legend is needed, place it where it does not cover data.
6. Put the title in the caption, not inside the figure
Keep titles and subtitles out of the plotting area. The figure's title, and any explanatory note, belong in the document as the caption beneath the figure, set in the manuscript's font and editable alongside the prose. A title rendered inside the image duplicates the caption, sets in a mismatched typeface, and cannot be changed without regenerating the plot. Strip ggtitle(), labs(title=, subtitle=), plt.title(), and plt.suptitle() from the figure script and let the caption carry the title. (Panel tags such as "A" and "B" inside a multi-panel figure are fine; a descriptive title is not.)
The caption must be self-contained: a reader who skims should understand the figure from the caption alone. Include:
- What is plotted (the dependent quantity, the unit of analysis).
- The sample (N, source, time window).
- The uncertainty interval (e.g., "95% confidence intervals from cluster-robust standard errors").
- Panel meanings, if multi-panel.
- Any transformations or exclusions.
- The estimand or model family if the figure is showing model output (not just "Results").
Keep abbreviations defined and units explicit.
7. Make it reproducible
- Generate the figure from a script (R, Python, Stata, Julia) in the replication archive. Hand edits in Illustrator or Inkscape are acceptable only when the manual step is documented.
- Save with the script: input data path, package versions, random seed (for jittered or sampled plots), output dimensions.
- Export vector (PDF, SVG) for plots with text and lines; export raster (PNG at ≥300 dpi) only for raster-native content (heatmaps with thousands of cells, photographs, maps with imagery).
- Use a single ggplot/matplotlib theme across the manuscript so figures are visually consistent.
- For multi-panel figures, build with
patchwork/cowplot/gridExtra or matplotlib's subplots/gridspec — not by stitching exported PNGs in Word.
8. Accessibility and production sanity
- Axis label and tick text ≥ 8pt at print size.
- Alt text for journals that require it (a one-sentence description of what the figure shows).
- Consistent figure dimensions and fonts across the manuscript.
Output
When asked to design or revise a figure, produce:
# Figure Plan
Comparison: <one sentence>
Chart type: <type and why>
Geometry: <axes, scales, faceting>
Color encoding: <palette, what it encodes, accessibility check>
Legend / labeling: <direct label or legend; order matches visual order>
Caption draft: <self-contained>
Reproducibility: <script path, packages, output format and dimensions>
Open issues: <anything that needs author input — denominator choice, sample restriction, etc.>
When asked to produce code, default to a single ggplot2 (R) or matplotlib + seaborn (Python) script with the theme, palette, and figure dimensions explicit at the top.