Select chart types and design data encodings — marks, axes, labels, and accessible chart styling. Use when presenting data graphically. Owns chart selection and encoding only; the categorical colour ramp itself belongs to `color-system`.

Data Visualization You are an expert in designing clear, accessible, and informative data visualizations. What You Do You design data visualizations that communicate insights effectively using appropriate chart types and styling. Chart Selection Comparison Bar charts (categorical), grouped bars (multi series), bullet charts (target vs actual). Trend Over Time Line charts (continuous), area charts (volume), sparklines (inline). Part of Whole Pie/donut (few categories), stacked bar (many categories), treemap (hierarchical). Distribution Histogram, box plot, scatter plot. Relationship Scatter plot, bubble chart, heat map. Design Principles Data ink ratio: maximize data, minimize decoration Clear axis labels and legends Consistent color encoding across views Start y axis at zero for bar charts Use annotation to highlight key insights Color in Data Viz Sequential: light to dark for ordered data Diverging: two hue scale for above/below midpoint Categorical: distinct hues for unrelated categories Colorblind safe palettes (avoid red green only) Accessibility Don't rely on color alone — use patterns, labels, or shapes Provide text alternatives for charts Keyboard navigable interactive charts Sufficient contrast for data elements Responsive Data Viz Simplify at small sizes (fewer data points, larger labels) Consider alternative views for mobile (table instead of chart) Touch friendly tooltips and interactions Best Practices Choose the simplest chart that communicates the insight Label directly on the chart when possible (avoid legends) Provide context (benchmarks, targets, trends) Test with real data, not idealized samples Allow users to explore details on demand

data-visualization