| name | datavis |
| description | Comprehensive data visualization toolkit for creating beautiful, mathematically elegant visualizations with D3.js, Chart.js, and custom SVG. Use when (1) building interactive data visualizations, (2) designing color palettes for charts, (3) choosing scales and visual encodings, (4) creating data pipelines from Census/SEC/Wikipedia APIs, (5) crafting narrative-driven data stories, (6) making perceptually accurate charts, or (7) implementing force-directed networks, timelines, or geographic maps. |
Data Visualization Skill
Create beautiful, mathematically elegant, emotionally resonant data visualizations.
Philosophy: "Life is Beautiful"
Every visualization should:
- Reveal truth through data
- Evoke wonder through design
- Respect the viewer through accessibility
- Honor complexity through elegant simplification
Core Capabilities
1. Visual Encoding
Scale Selection:
| Scale | Use When | Example |
|---|
| Linear | Evenly distributed data | Temperature |
| Log | Multiple orders of magnitude | Population (100 to 1B) |
| Sqrt | Encoding area (circles) | Bubble chart radius |
| Time | Temporal data | Dates |
Perceptual Honesty - Area scales with square of radius, so use sqrt:
const badScale = d3.scaleLinear().domain([0, max]).range([0, maxRadius]);
const goodScale = d3.scaleSqrt().domain([0, max]).range([0, maxRadius]);
2. Color Design
Palette Types:
- Categorical - Distinct hues for nominal data (max 8)
- Sequential - Single hue gradient for ordered data
- Diverging - Two hues meeting at meaningful midpoint
Colorblind-Safe Palette (8 colors):
const colorblindSafe = [
'#332288', '#117733', '#44AA99', '#88CCEE',
'#DDCC77', '#CC6677', '#AA4499', '#882255'
];
Always use redundant encoding - don't rely on color alone:
node.attr('fill', d => colorScale(d.category))
.attr('d', d => symbolScale(d.category));
3. D3.js Patterns
Force Simulation:
const simulation = d3.forceSimulation(nodes)
.force('charge', d3.forceManyBody().strength(-300))
.force('link', d3.forceLink(links).id(d => d.id))
.force('center', d3.forceCenter(width/2, height/2))
.force('collision', d3.forceCollide().radius(d => d.r + 2));
Responsive SVG:
const svg = d3.select('#chart')
.append('svg')
.attr('viewBox', `0 0 ${width} ${height}`)
.attr('preserveAspectRatio', 'xMidYMid meet');
Touch-Friendly (44x44px minimum):
node.append('circle')
.attr('class', 'hit-area')
.attr('r', Math.max(actualRadius, 22))
.attr('fill', 'transparent');
4. Narrative Structure
Three Acts:
- Invitation - What draws viewer in? Why should they care?
- Discovery - What patterns emerge? What surprises?
- Reflection - What should they feel/understand/do?
Progressive Disclosure:
Level 1: Overview → Level 2: Exploration → Level 3: Detail → Level 4: Context
5. Data Pipeline
Structure:
scripts/
├── 01_fetch_raw.py # API calls with caching
├── 02_clean_data.py # Transformation
├── 03_validate.py # Quality checks
└── 04_export.py # Final format
Source Documentation (every dataset needs):
- URL, access date, update frequency
- License and confidence level
- Field descriptions and limitations
Scripts
Generate Color Palette
scripts/color-palette.py --type sequential --hue blue --steps 9
scripts/color-palette.py --type categorical --count 6 --colorblind-safe
scripts/color-palette.py --type diverging --low red --high blue
Analyze Data Distribution
scripts/analyze-distribution.py data.csv --column value
Scaffold D3 Project
scripts/d3-scaffold.py my-viz --type force-network
scripts/d3-scaffold.py my-viz --type timeline
scripts/d3-scaffold.py my-viz --type choropleth
Anti-Patterns to Avoid
- 3D charts (distorts perception)
- Pie charts with >6 categories
- Dual y-axes
- Rainbow color scales (perceptually uneven)
- Truncated y-axes without disclosure
- Animation without purpose
Quality Checklist
