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Aussagekräftige Diagramme wählen
Wählt Darstellungsformen und Skalen für verständliche Vergleiche ohne Verzerrung der Datenbedeutung.
chart-selection-visual-encoding
Entdecke, wie der Skill hilftWann ist der Skill hilfreich?
Nutze dies, wenn ein Diagramm eine konkrete Frage ohne irreführende Vergleiche beantworten soll.
Wobei hilft er dir?
- Ordnet Trends, Kategorien, Verteilungen und Zusammenhängen passende Diagrammtypen zu.
- Nutzt gemeinsame Skalen und klare Grundlinien für quantitative Vergleiche.
- Vermeidet verzerrte Flächen, inkonsistente Skalen und irreführende Doppelachsen.
Das könntest du fragen
Gestalte ein Umsatz-Erstattungs-Diagramm so um, dass unterschiedliche Achsen keinen falschen Zusammenhang suggerieren. Verwende in beiden Entwürfen dieselben Monatsdaten. Erkläre zulässige Vergleiche und liefere Werte, die Lesende direkt nachprüfen können.
Ausführliche Dokumentation
Hier findest du die ursprüngliche Anleitung, Beispiele und Prüfschritte des Skills in ihrer Originalsprache.
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Chart Selection & Visual Encoding
Use this skill when choosing chart types, mapping data attributes to visual channels, and designing scales, axes, and legends.
Perceptual Accuracy & Task-Driven Encoding (Munzner & Cleveland)
- Match the Chart to the Analytical Task and Data Type:
- Trend & rate of change over ordered time: Line chart, step chart (for discrete state/price changes), or horizon/area chart.
- Magnitude comparison across categories: Horizontal or vertical bar chart, Cleveland dot plot.
- Distribution & outliers: Histogram, box/violin plot, strip/beeswarm plot, or quantile band (never hide a bimodal distribution behind a single mean bar).
- Part-to-whole composition: Stacked bar (for a few segments), 100% stacked bar, marimekko, or treemap (avoid multi-slice pie/donut charts when $>3\text{–}4$ categories or close values must be compared).
- Correlation & joint variation: Scatter plot, hexbin/contour density plot, parallel coordinates, or heatmap.
- Respect Cleveland & McGill's Perceptual Hierarchy:
- Encode primary quantitative comparisons using position along a common aligned scale first, then length, before falling back to area, angle, or color saturation.
- Never use 3D extrusions, perspective distortion, or decorative gauges that falsify perceived proportions.
Honest Scales, Baselines & Axes (Tufte)
- Mandatory Zero Baseline for Length/Area Encodings:
- Bar and filled area charts encode magnitude by length/area and must start at
0. If a narrow non-zero range is needed to inspect small variations, use a line or dot plot with clearly labeled axis bounds.
- Bar and filled area charts encode magnitude by length/area and must start at
- Avoid Deceptive Dual Y-Axes:
- Plotting two metrics on independent left/right Y-axes lets arbitrary scale stretching manufacture fake visual correlation. Prefer indexed small multiples (
% change) or vertically stacked linked panels sharing an X-axis.
- Plotting two metrics on independent left/right Y-axes lets arbitrary scale stretching manufacture fake visual correlation. Prefer indexed small multiples (
- Consistent Scales Across Small Multiples:
- Lock side-by-side comparison panels to a shared scale by default; if independent scaling is toggled, mark the axis warning unmistakably.
- Explicit Non-Linear Scales:
- Use log scales only across multiple orders of magnitude or multiplicative growth rates, and label ticks and axis titles clearly (
log10 scale).
- Use log scales only across multiple orders of magnitude or multiplicative growth rates, and label ticks and axis titles clearly (
Checklist
- Does the chosen chart type directly answer the user's comparison, trend, distribution, or composition question?
- Do all bar and area charts start at a
0baseline without arbitrary axis truncation? - Have dual-axis distortions and 3D/area misproportions been avoided in favor of aligned common scales or linked panels?
- Are units, tick formatting, scale types (linear vs. log), and legends unambiguous?
Enthaltene Dateien 1
SKILL.md
