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name: Data Visualization description: "Comprehensive data visualization skill covering visual execution and technical implementation. Includes perceptual foundations, chart selection, layout algorithms, and library guidance. Load on-demand when building charts, graphs, dashboards, or any visual data representation." version: 1.0.0

Data Visualization

Visualization is communication. Every visual element must serve understanding.

Critical Rules

🚨 Use established algorithms. Graph layout, tree layout, spatial indexing—these problems are solved. Check dagre, d3-force, ELK.js before implementing anything custom.

🚨 Choose encodings by perceptual accuracy. Position beats length beats angle beats area beats color. Prefer bar charts over pie charts over bubble charts.

🚨 Never rely on color alone. 8% of men are colorblind. Use shape, pattern, or labels as backup encoding.

🚨 Match rendering to scale. SVG for <1000 elements, Canvas for 1000-10000, WebGL for >10000.

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1. Visual Encoding

Marks & Channels

Marks are geometric primitives representing data:

• Points (scatter plots, dot plots)
• Lines (line charts, network edges)
• Areas (bar charts, area charts, maps)

Channels are visual properties applied to marks:

• Position (x, y coordinates)
• Size (length, area, volume)
• Color (hue, saturation, lightness)
• Shape (circle, square, triangle)
• Orientation (angle, slope)

Cleveland & McGill Hierarchy (1984)

Visual encodings ranked by perceptual accuracy:

1. Position along common scale (most accurate)
2. Position on non-aligned scales
3. Length
4. Angle/slope
5. Area
6. Volume
7. Color saturation/hue (least accurate)

Implication: Bar charts (position) > pie charts (angle) > bubble charts (area)

Preattentive Attributes

Properties processed in <250ms without conscious effort:

• Color (hue, saturation)
• Form (orientation, length, width, size, shape)
• Spatial position
• Motion

Use preattentive attributes for the most important data—they "pop out" automatically.

Channel Effectiveness by Data Type

Data Type	Best Channels
Quantitative	Position, length, angle, area
Ordinal	Position, density, saturation
Categorical	Shape, hue, spatial region

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2. Interaction Design

Shneiderman's Mantra (1996)

"Overview first, zoom and filter, then details on demand"

1. Overview — Show entire dataset, establish context
2. Zoom & Filter — Reduce complexity, focus on subset
3. Details on Demand — Tooltips, click-to-expand, drill-down

Interaction Patterns

Pattern	Use Case
Brushing & linking	Cross-highlighting across coordinated views
Focus + context	Fisheye lens, detail-on-demand panels
Direct manipulation	Drag nodes, resize elements, reorder
Animated transitions	Help users track changes between states
Pan & zoom	Navigate large visualizations
Filtering	Reduce data to relevant subset
Selection	Highlight specific data points

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3. Chart Selection

By Question Type

Question	Chart Type	Why
How do values compare?	Bar chart	Position encoding is most accurate
How has this changed over time?	Line chart	Shows trends, handles many points
What's the distribution?	Histogram, box plot	Shows spread, outliers, shape
What's the relationship?	Scatter plot	Reveals correlation, clusters
What's the part-to-whole?	Stacked bar, treemap	Shows composition
What are the connections?	Network graph, Sankey	Shows relationships, flows
What's the hierarchy?	Tree, sunburst, treemap	Shows parent-child structure
Where is it?	Choropleth, symbol map	Geographic context

By Data Volume

Volume	Approach
<20 points	Simple charts, direct labeling
20-500	Standard visualization
500-5000	Consider aggregation, filtering
5000+	Aggregation mandatory, or Canvas/WebGL

Common Anti-Patterns

• ❌ Pie charts with >5 slices (use bar chart)
• ❌ 3D charts without strong justification
• ❌ Dual-axis with unrelated scales (misleading)
• ❌ Non-zero baselines for bar charts (distorts perception)
• ❌ Truncated axes without clear indication

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4. Color

Palette Types

Type	Use Case	Examples
Sequential	Low to high values	Blues, Greens, Viridis
Diverging	Values diverge from midpoint	RdBu, BrBG, Spectral
Categorical	Distinct categories	Set2, Tableau10, Category10

Colorblind Safety

• 8% of men, 0.5% of women have color vision deficiency
• Never rely on color alone—use shape, pattern, labels
• Safe sequential: viridis, cividis, plasma
• Safe categorical: ColorBrewer's colorblind-safe options
• Test with: Coblis, Sim Daltonism, Chrome DevTools

Perceptual Uniformity

• Avoid rainbow colormaps (jet)—perceptual steps are uneven
• Use viridis, parula, cividis for sequential data
• These ensure equal perceptual distance between values

Color Guidelines

• 4.5:1 contrast ratio for text (WCAG AA)
• 3:1 contrast for UI components
• Max 7-10 distinct categorical colors
• Use saturation/lightness variation for emphasis

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5. Layout Algorithms

🚨 Before implementing ANY layout algorithm, check if a library exists.

Algorithm → Library Mapping

Problem	Algorithm	Libraries
Layered/DAG graphs	Sugiyama (1981)	dagre, ELK.js
Force-directed networks	Fruchterman-Reingold (1991)	d3-force, Cytoscape.js
Tree layouts	Reingold-Tilford (1981)	d3-hierarchy
Treemaps	Squarified (2000)	d3-hierarchy, ECharts
Circle packing	Wang (2006)	d3-hierarchy
Sankey diagrams	—	d3-sankey
Chord diagrams	—	d3-chord
Large graphs (10k+)	WebGL + spatial indexing	Sigma.js, G6, deck.gl
Spatial queries	Quadtree, R-tree	d3-quadtree, rbush
Edge crossing minimization	Barth (2002)	Built into dagre/ELK

When to Use Each Layout

Layout	Best For
Sugiyama (dagre)	Flowcharts, dependency graphs, DAGs with direction
Force-directed	Social networks, organic relationships, exploration
Tree	Hierarchies with single parent per node
Treemap	Hierarchies with quantitative values
Circular	Emphasizing central nodes, ring structures
Matrix	Dense graphs where edges would overlap

These problems are solved. Never implement from scratch.

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6. Rendering & Performance

Rendering Technology Thresholds

<1000 elements    → SVG
                    - DOM events work naturally
                    - Accessibility (ARIA) supported
                    - Crisp at any zoom level
                    - CSS styling

1000-10000        → Canvas
                    - Batch rendering
                    - Manual hit testing required
                    - Lower memory footprint
                    - requestAnimationFrame for animation

>10000            → WebGL
                    - GPU acceleration
                    - Sigma.js, deck.gl, regl
                    - Complex setup
                    - Limited text rendering

Performance Patterns

Pattern	When to Use
Web Workers	Layout computation (never block main thread)
Spatial indexing	Hit detection with quadtree/R-tree
Level-of-detail	Simplify distant/small elements
Viewport culling	Only render visible elements
Debouncing	Expensive interactions (zoom, filter)
Virtualization	Long lists of chart components
Aggregation	Too many data points to render individually

Anti-Patterns

• ❌ 5000 SVG nodes (use Canvas)
• ❌ Layout computation on main thread
• ❌ Hit testing without spatial indexing
• ❌ Rendering off-screen elements
• ❌ Animating thousands of elements individually

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7. Libraries

Graph Layouts

Library	Best For	Notes
dagre	Layered DAGs, flowcharts	Sugiyama algorithm, good defaults
dagre-d3	dagre + D3 rendering	SVG output
ELK.js	Complex layouts, compound graphs	Eclipse Layout Kernel, highly configurable
d3-force	Organic networks	Fruchterman-Reingold, customizable forces
Cytoscape.js	Graph analysis + visualization	Rich algorithm library
Sigma.js	Large graphs (10k+)	WebGL rendering
G6/AntV	Enterprise graphs	Full-featured, Chinese ecosystem
vis-network	Quick prototypes	Easy API, limited customization

Charting

Library	Best For	Notes
D3.js	Custom, highly interactive	Low-level, maximum control
Observable Plot	Quick exploration	D3 team, excellent defaults
Recharts	React integration	Declarative, composable
Victory	React integration	Animation support
ECharts	Feature-rich dashboards	Great mobile, large dataset support
Vega-Lite	Grammar of graphics	Declarative JSON spec
Chart.js	Simple charts	Easy setup, limited customization
Plotly	Scientific visualization	3D support, interactivity

When to Use D3 vs Higher-Level Libraries

Use D3 when:

• Need complete control over rendering
• Building novel/custom visualizations
• Integrating with existing SVG/Canvas code
• Performance-critical with custom optimizations

Use higher-level libraries when:

• Standard chart types suffice
• Faster development time matters
• Team less experienced with D3
• Need built-in responsiveness/animation

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8. Composition & Layout

Project Composition (Dashboard Level)

• Visual hierarchy — Guide eye to most important first
• Grid systems — Align elements for coherence
• Grouping — Related visualizations together
• White space — Breathing room, not wasted space
• Reading flow — Z-pattern or F-pattern for Western audiences

Chart Composition (Single Chart)

Element	Guidelines
Title	Clear, descriptive; top-left or centered above
Subtitle	Additional context; smaller, below title
Axes	Labeled with units; tick marks at meaningful intervals
Legend	Embedded when possible; external if complex
Aspect ratio	Affects slope perception; 45° banking for trends
Margins	Enough for labels; consistent across charts

Aspect Ratio Guidelines

• Line charts: ~16:9 for trends (banking to 45°)
• Bar charts: Depends on number of bars
• Scatter plots: Often square (1:1) for correlation
• Maps: Preserve geographic proportions

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9. Annotation

Annotation Types

Type	Purpose
Title	The "what" — identifies the visualization
Subtitle	Additional context, data source
Caption	The "so what" — key insight or takeaway
Axis labels	Variable names and units
Legend	Decode color/shape/size mappings
Callouts	Highlight specific data points
Reference lines	Benchmarks, targets, averages
Source citation	Data provenance

Best Practices

• Annotate the insight, not just the data — "Sales peaked in Q3" not just "Sales over time"
• Use callouts sparingly — Highlight 1-3 key points maximum
• Direct labeling — Embed labels in chart when possible (vs separate legend)
• Provide context — Benchmarks, historical reference, targets
• Layer information — Overview visible, details on interaction

Text Hierarchy

1. Title (largest, boldest)
2. Subtitle/caption
3. Axis titles
4. Tick labels
5. Annotations
6. Source (smallest)

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10. Accessibility

WCAG Requirements

• AA minimum (AAA preferred)
• 4.5:1 contrast ratio for normal text
• 3:1 contrast for large text and UI components
• No information conveyed by color alone

Keyboard Navigation

• Tab through interactive elements
• Arrow keys for traversing data points
• Enter/Space for selection
• Escape to cancel/close

Screen Reader Support

<svg role="img" aria-labelledby="chart-title chart-desc">
  <title id="chart-title">Monthly Sales 2024</title>
  <desc id="chart-desc">Bar chart showing sales increasing from $10M in January to $15M in December</desc>
</svg>

• Use ARIA labels and roles
• Provide text alternatives
• Announce dynamic updates with live regions
• Structure for logical reading order

Alternative Representations

• Data tables — Provide as fallback for all charts
• Text summaries — Describe key insights
• Sonification — Audio representation for time-series
• Tactile graphics — For physical accessibility

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11. Anti-Patterns Summary

Design Anti-Patterns

Anti-Pattern	Why It's Wrong	What to Do
3D charts	Distorts perception	Use 2D
Pie >5 slices	Hard to compare	Use bar chart
Dual unrelated axes	Misleading correlation	Separate charts
Non-zero baseline	Exaggerates differences	Start at zero
Rainbow colormap	Perceptually uneven	Use viridis
Color-only encoding	Excludes colorblind	Add shape/pattern
Chart junk	Distracts from data	Remove decoration
Overplotting	Hides data density	Aggregate or jitter

Implementation Anti-Patterns

Anti-Pattern	Why It's Wrong	What to Do
Custom graph layout	Reinventing solved problem	Use dagre/ELK
5000 SVG nodes	Poor performance	Use Canvas
Main thread layout	Blocks UI	Use Web Worker
No spatial indexing	Slow hit detection	Use quadtree
Rendering off-screen	Wasted computation	Viewport culling

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12. Academic Foundations

Seminal Papers

Paper	Year	Contribution
Cleveland & McGill "Graphical Perception"	1984	Visual encoding hierarchy
Shneiderman "The Eyes Have It"	1996	Overview-zoom-filter-details mantra
Gansner et al. "Drawing Directed Graphs"	1993	Foundation for dagre
Fruchterman & Reingold "Force-directed Placement"	1991	Foundation for d3-force
Sugiyama et al. "Hierarchical Systems"	1981	Layered graph layout
Barth et al. "Bilayer Cross Counting"	2002	Edge crossing minimization
Brewer "Color Use Guidelines"	1994	ColorBrewer palettes

Essential Resources

Resource	Type	Focus
ColorBrewer (colorbrewer2.org)	Tool	Accessible color palettes
From Data to Viz (data-to-viz.com)	Guide	Chart selection decision tree
Visualization Analysis & Design (Munzner)	Textbook	Comprehensive theory
Data Visualisation (Kirk)	Textbook	Practitioner guide
Visual Display of Quantitative Information (Tufte)	Textbook	Data-ink ratio, chart junk
D3 Gallery (observablehq.com/@d3/gallery)	Examples	Implementation patterns

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Summary

🚨 Before implementing visualization:

1. What question are you answering? → Select chart type
2. What's your data volume? → Select rendering technology
3. Is there an established algorithm? → Use the library
4. Is it accessible? → Color, keyboard, screen reader
5. Does it follow perceptual best practices? → Encoding hierarchy