Chart Tufte

A chart is good when it shows the data, helps the viewer reason about it, and does not lie. Tufte spent a career formalising this. This skill collapses his principles into a self-grade pass that runs on any chart the assistant is about to emit.

Read references/vdqi-catalogue.md first when grading. It holds the named-failures library (NYT MPG 14.8, TIME barrel 59.4, etc.) and the named-exemplars library (Minard, Marey, Playfair, Snow). The catalogue is what makes assessment diagnostic instead of generic.

When to invoke

• After building: as the last step of /visual-explainer whenever the page holds a quantitative chart (bar, line, scatter, area, dot, range-frame); that skill's steps call for it. Skip for diagrams (architecture, sequence, flow); those are different beasts.
• Explicitly: /chart-tufte <chart-spec> to score a chart produced elsewhere.

The rubric (run before emitting)

Score each on 0-10. Stop and revise if any score drops below 5.

1. Data-ink ratio: does every pixel of ink represent data? Borders, gridlines, redundant legends are non-data ink. Target: tend toward 1.0; typical for default-styled charts is 0.1-0.2 (VDQI p.136).
2. Lie factor: (visual change %) / (data change %). Acceptable range 0.95-1.05. Cite a VDQI named failure if it is worse: TIME's barrel chart hit 59.4, NYT's MPG hit 14.8.
3. Data density: numbers per square inch. Below 0.15 is overwrought (a single bar showing one number is the canonical sin). High-density alternatives: small multiples, range-frame scatters, tables.
4. Chartjunk count: does the chart contain any of the four named species?
   • Moiré: vibrating cross-hatch patterns.
   • The dreaded grid: gridlines drawn darker than the data.
   • The duck: visual gimmick that overwhelms the data (3D pies, gradient-shaded bars).
   • Decoration: clip art, icons, mascots in the chart frame. Each chartjunk species costs points.
5. Genre fit: is this the right shape for this data?
   • ≤20 numbers → table, not chart
   • Many series → small multiples (one panel per series)
   • 2 quantitative variables → range-frame scatter (axis only spans data min-max)
   • Distribution → quartile plot, not bar of mean
   • Time series → thin line + direct endpoint labels, no legend
6. Dimensionality discipline: does the chart use more dimensions than the data has? A 3D pie chart on 1D data is dishonest by construction. Penalise.
7. Direct labelling: are series labelled at their endpoint or inline, not via a separate legend? Legends force the reader's eye to bounce.
8. Range-frame discipline: does the axis span only the data range, not 0 to some arbitrary max?
9. Comparability: if multiple panels: same y-scale where comparison matters; different scales only when local shape is the question.

Remedies (when the rubric flags problems)

• B1: fix lie factor: redraw with proportional scaling. State the new lie factor in the chart caption.
• B2: erase non-data ink: remove borders, drop gridlines, kill the legend if direct labelling fits.
• B3: increase data density: switch to small multiples if you have ≥3 panels of comparable data.
• B4: pick the right genre (see C1-C10 below).
• B5: name the failure: if the chart resembles a VDQI named failure (NYT MPG 14.8, TIME barrel 59.4, LA Times shrinking-doctor 2.8, USA Today 3D bar chart, Playfair's wheat decline), say so before showing.
• B6: fix dimensionality: 1D data → 1D chart; never 3D unless the third dimension is in the data.
• B7: deflate currency over time: if plotting dollars across years, deflate to a common year (state which) before plotting. Failing to do this is a silent lie factor.

Genre playbook (C1-C10)

• C1: quartile plot for distributions (5-number summary, no whiskers gimmick).
• C2: range-frame scatter for two quantitative variables (axes span data range only).
• C3: dot-dash marginals on a range frame to show density on each axis.
• C4: paired bars for two-group comparison on one categorical axis.
• C5: small multiples for many series (1 panel per series, same x-axis, often same y-axis).
• C6: sparkline for inline time series at word-scale.
• C7: slopegraph for before/after comparison across many categories.
• C8: stem-and-leaf table when the goal is the actual numbers, not the shape.
• C9: table when ≤20 numbers and structure matters.
• C10: thin line chart for time series, direct endpoint label, no legend, no gridlines.

Exemplars to emulate

• Minard's Napoleon's March (1869): six variables, one image, time + space + temperature + casualties.
• Playfair's wheat-vs-wages (1822): currency deflated, dual y-axis used honestly.
• Modern NYT inflation charts: direct labels, no gridlines, deflated currency.
• Tufte's own sparkline-in-prose pattern: chart as part of the sentence.

Output

A six-line block:

Tufte self-grade
  Data-ink ratio: 7/10
  Lie factor: 1.02 (acceptable; not in catalogue)
  Chartjunk: 0 species
  Genre fit: C10 (thin line), correct for the data
  Verdict: ship
  Notes: (any remedies applied during pass)

If the verdict is not ship, revise the chart and re-grade before emitting.

Integration with visual-explainer

/visual-explainer calls this skill after it builds the page (its "Grade any chart" step). Two things to do there:

1. Check the genre: name the Tufte genre (C1-C10) the chart should be, and whether the built chart matches it. The visual palette is a separate choice; never let styling pick the genre.
2. Grade the rendered chart: run this rubric over the rendered HTML/SVG before the page is saved. If any score is below 5, revise. Do not save on verdict != ship.

VDQI page references (for citations)

• Data-ink ratio definition + targets: p.93, 136
• Lie factor formula + named failures: pp.55-80
• Chartjunk taxonomy (moiré / grid / duck / decoration): pp.107-121
• Small multiples: pp.170-175
• Range frame: pp.130-133
• Sparklines (later book Beautiful Evidence, but cited in the canon)

Edward Tufte, The Visual Display of Quantitative Information, 2nd ed., Graphics Press, 2001.