Data viz storytelling healy

Data Visualization & Storytelling (Healy + AntV)

"The tools you use can help you live up to the right standards. But they cannot make you do the right thing." — Kieran Healy, Data Visualization, Ch. 1

When to Use This Skill

• Deciding which chart type fits the analytical question
• Writing a report or slide deck where numbers need a narrative
• Reviewing a figure for honesty / misleading patterns
• Drafting an insight summary with claim → evidence → caveat structure
• Choosing between infographic (visual design) and statistical figure (accuracy)

For actual plotting code → use matplotlib, seaborn, or scientific-visualization.

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Section 1 — Honesty & Judgment (Healy Ch. 1)

Before choosing colors or chart types, verify the figure does not mislead.

1.1 Pre-plot Honesty Checklist

Check	Why it matters
Baseline / zero start	Bar charts starting above zero exaggerate differences. Line charts may omit zero legitimately if the focus is trend, not level.
Dual axes	Two Y-axes on one plot invite false correlation. Prefer faceted panels or indexed series.
Cherry-picked window	Short time windows can hide long-term patterns. Always show context.
Aggregation level	Averages can hide distribution shape. Consider showing raw data, box plots, or density.
Proportional vs absolute	Normalize when comparing groups of different size; keep raw counts available.
Color encoding	Do not use rainbow/palette that implies order for categorical data. Use colorblind-safe palettes.
Uncertainty	Show CI, prediction bands, or error bars when presenting estimates.
Axis labels & units	Every axis must state what it measures and in what unit.

Rule of thumb: If removing context (time range, N, CI) changes the takeaway, the figure is misleading as-is.

1.2 Perception Principles (Healy Ch. 1 + Cleveland)

Humans perceive some encodings more accurately than others:

1. Position on common scale — most accurate (scatter, line)
2. Position on different scale — good (grouped bar, faceted)
3. Length / direction — moderate
4. Angle / area — poor (avoid pie charts for comparison)
5. Color intensity / shape — worst for magnitude; OK for categories

Implication: Prefer dot plots or bar charts over pie/bubble for quantitative comparison.

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Section 2 — Chart Selection Rubric (AntV taxonomy → Python)

Ask: "What is the analytical question?" then pick the chart family.

Question family	Chart types	Python (seaborn/matplotlib)
Trend over time	Line, area, step	sns.lineplot, ax.plot, ax.fill_between
Comparison (categories)	Bar (grouped/stacked), lollipop	sns.barplot, ax.barh
Part-to-whole	Stacked bar, waffle, pie (sparingly)	ax.bar(stacked), avoid pie for >3 slices
Distribution	Histogram, KDE, box, violin, ridgeline	sns.histplot, sns.kdeplot, sns.boxplot, sns.violinplot
Relationship (2+ vars)	Scatter, bubble, hexbin, 2D density	sns.scatterplot, ax.hexbin, sns.jointplot
Ranking	Lollipop, horizontal bar (sorted)	ax.barh (sorted)
Deviation / contrast	Diverging bar, slope chart, dumbbell	Custom ax.barh with center baseline
Geography	Choropleth, bubble map	geopandas + matplotlib; or Folium
Hierarchy / flow	Treemap, sankey, dendrogram	squarify, matplotlib-sankey
Multi-variate summary	Parallel coordinates, radar, heatmap	pd.plotting.parallel_coordinates, sns.heatmap
Model diagnostics	Residual plot, calibration curve, lift/gains	sns.residplot, custom calibration, KS plot

Decision Flow

1. How many variables?  → 1 (distribution) | 2 (relationship) | 3+ (multivariate)
2. Is there a time component?  → Yes: line/area trend
3. Am I comparing groups?  → Yes: bar/lollipop
4. Am I showing composition?  → Yes: stacked bar > pie
5. Am I showing uncertainty?  → Always add CI/bands
6. Audience?  → Expert: detail plot | Executive: annotated summary + callout

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Section 3 — Building Plots Layer by Layer (Healy Ch. 3)

Healy teaches the grammar of graphics: data → mapping → geom → stat → coord → facet → theme.

Mapped to matplotlib/seaborn:

Grammar layer	ggplot concept	matplotlib / seaborn equivalent
Data	ggplot(df)	Pass DataFrame to seaborn or plot columns
Aesthetic mapping	aes(x=, y=, color=)	x=, y=, hue= params in seaborn; manual in matplotlib
Geometry	geom_point, geom_line	ax.scatter, ax.plot, sns.scatterplot
Statistical transform	stat_summary, geom_smooth	sns.regplot, sns.aggplot-like via groupby + plot
Coordinate system	coord_flip, coord_polar	ax.invert_yaxis(), projection='polar'
Facet	facet_wrap(~var)	sns.FacetGrid / sns.relplot(col=)
Theme / labels	labs(), theme_minimal()	ax.set_title/labels, spine removal, style sheets

Workflow in Python:

import seaborn as sns
import matplotlib.pyplot as plt

# Layer 1: data + mapping + geom
g = sns.relplot(
    data=df,
    x="feature_date", y="default_rate",
    hue="segment", col="product",
    kind="line", facet_kws={"sharey": False}
)

# Layer 2: annotation
g.fig.suptitle("Default Rate Trend by Segment & Product", y=1.02)
g.set_axis_labels("Month", "Default Rate (%)")

# Layer 3: refine
g.tick_params(axis="x", rotation=45)
sns.despine()
plt.tight_layout()

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Section 4 — Narrative with Numbers (AntV T8 + Report Pattern)

When writing a data-driven section (report, slide, notebook conclusion), follow:

4.1 Claim-Evidence-Caveat Pattern

**Claim:** "Approval rate for Segment A dropped 15 pp in Q3."

**Evidence:**
  - Figure: Line chart of monthly approval rate, Segments A/B/C, Jan–Sep
  - Table: N, mean, CI per segment per quarter

**Caveat:**
  - "Data window excludes Jan policy change; Segment C sample < 500."

4.2 Entity Annotation (T8-inspired)

In markdown, annotate key entities so readers (and agents) can parse them:

The [metric:approval_rate] for [dimension:Segment_A] was [value:72%] ([trend:down_15pp])
in [dimension:Q3_2026], compared to [value:87%] in [dimension:Q2_2026].

This mirrors AntV T8's entity-based narrative but works in plain markdown.

4.3 Figure Caption Template (Healy Ch. 5)

A good caption has:

1. What is being shown (chart type + variables)
2. Who/when (cohort, time window, N)
3. Key takeaway (one sentence)
4. Caveat (if any)

Example:

Figure 3. Monthly default rate (%) by risk tier, Jan 2025 – Sep 2026 (N = 142,000). Tier D shows the steepest increase after the Q2 policy change. 95% CI shown in shading.

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Section 5 — Model → Visualization (Healy Ch. 6, Credit Scoring context)

Common model diagnostic plots for credit scoring:

Diagnostic	Chart	Purpose
Residual analysis	Residual vs predicted, Q-Q plot	Check model assumptions
Feature effect	Partial dependence / ICE	Direction & magnitude of top features
Discrimination	KS plot, ROC curve, PR curve	Rank-ordering ability
Calibration	Calibration curve (predicted vs actual)	Score reliability
Stability	PSI over time, population distribution shift	Feature / score drift
Segmentation	Slice metrics (Gini by segment, approval rate by tier)	Fairness & performance parity
SHAP	Beeswarm, waterfall, scatter	Explainability (see shap skill)

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Section 6 — Refinement Quick Reference

For detailed publication styling → scientific-visualization.

Aspect	Guideline
Color palette	Use colorblind-safe (viridis, Okabe-Ito). Avoid rainbow for sequential data.
Grayscale test	Figure should still be readable in black & white.
Font size	Minimum 6 pt at final print size; larger for presentations.
Spines	Remove top & right spines for cleaner look.
Grid lines	Use light, sparse gridlines; avoid heavy grid.
Legends	Place near data; consider direct labels instead.
Aspect ratio	Choose so perception is not distorted (banking to 45° for trends).
File format	PDF/SVG for vector; PNG at 300+ DPI for raster.

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Section 7 — Infographic vs Statistical Figure

Dimension	Infographic (infographics skill)	Statistical Figure (this + scientific-visualization)
Primary goal	Communicate key message visually	Show data accurately & completely
Audience	General / executive / marketing	Analysts, reviewers, risk committee
Data density	Low-moderate (curated highlights)	High (full distribution, uncertainty)
Aesthetics	Template-driven, branded	Clean, minimal chart junk
Tool	Nano Banana Pro / design tools	matplotlib / seaborn / plotly

Choose infographic when: one key message, non-technical audience, visual impact matters. Choose statistical figure when: accuracy, reproducibility, peer review required.

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References

• Healy, K. (2019). Data Visualization: A Practical Introduction. Princeton University Press.
• AntV chart-visualization-skills: https://github.com/antvis/chart-visualization-skills (MIT)
• Cleveland, W. S. & McGill, R. (1984). "Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods." JASA.
• Tufte, E. R. (2001). The Visual Display of Quantitative Information. Graphics Press.