datavis

Create beautiful, mathematically elegant, emotionally resonant data visualizations.

Philosophy: "Life is Beautiful"

Every visualization should:

1. Reveal truth through data
2. Evoke wonder through design
3. Respect the viewer through accessibility
4. 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:

// WRONG: Linear radius exaggerates large values
const badScale = d3.scaleLinear().domain([0, max]).range([0, maxRadius]);

// RIGHT: Sqrt maintains perceptual accuracy
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)); // Shape too!

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:

1. Invitation - What draws viewer in? Why should they care?
2. Discovery - What patterns emerge? What surprises?
3. 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
# Outputs: min, max, skew ratio, recommended scale

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

• Scale choice justified for data distribution
• Color palette is colorblind-safe
• Minimum 44x44px touch targets
• Clear entry point for viewer
• Sources documented
• Responsive on mobile