| name | data-viz-2025 |
| description | State-of-the-art data visualization for React/Next.js/TypeScript with Tailwind CSS. Creates compelling, tested, and accessible visualizations following Tufte principles and NYT Graphics standards. Activate on "data viz", "chart", "graph", "visualization", "dashboard", "plot", "Recharts", "Nivo", "D3". NOT for static images, print graphics, or basic HTML tables. |
| allowed-tools | Read,Write,Edit,Bash |
| metadata | {"category":"Data & Analytics","tags":["data","viz","2025","data-viz","chart"],"pairs-with":[{"skill":"react-performance-optimizer","reason":"Large dataset visualizations require React performance optimization for smooth rendering"},{"skill":"large-scale-map-visualization","reason":"Map visualizations are a specialized subset of the broader data viz discipline"},{"skill":"color-contrast-auditor","reason":"Data visualization accessibility depends on proper color contrast in charts and legends"},{"skill":"typescript-advanced-patterns","reason":"Type-safe chart data structures and generic visualization components benefit from advanced TS"}]} |
Data Visualization 2025: The Art & Science of Visual Communication
Create visualizations that Seaborn users, Tufte readers, and everyone else will love. Marry NYT Graphics rigor with MoMA aesthetics, Nike energy, and On Kawara precision.
When to Use This Skill
✅ Use for:
- Building interactive charts, dashboards, and data stories
- Complex visualizations (chord diagrams, Sankey flows, network graphs)
- Real-time data displays with animations
- Mobile-responsive data components
- Accessible, tested visualizations for production
❌ NOT for:
- Static PNG/SVG exports without interaction (use design tools)
- Basic HTML tables (use semantic markup)
- Print-only graphics (different constraints)
- Simple icon displays (use icon libraries)
Core Philosophy: The Three Pillars
1. Clarity (Tufte's Data-Ink Ratio)
Every visual element must earn its place. Remove chart junk, maximize signal-to-noise.
2. Beauty (Aesthetic Standards)
Visualizations are art. Use spring physics, thoughtful color, and premium design systems.
3. Truth (Graphical Integrity)
Data representation must be honest. Test rigorously, document assumptions, preserve context.
Quick Decision Tree
What are you building?
├─ Exploratory analysis / many iterations
│ └─ → Observable Plot (grammar-of-graphics)
│
├─ Standard business charts (bars, lines, pies)
│ ├─ Simple React integration needed
│ │ └─ → Recharts (easiest, most popular)
│ └─ Premium aesthetics + theming
│ └─ → Nivo (beautiful out of the box)
│
├─ Custom, one-of-a-kind visualizations
│ ├─ Need low-level control
│ │ └─ → Visx (React + D3 primitives)
│ └─ Full D3 power
│ └─ → D3.js directly (steeper learning curve)
│
└─ Dashboard with Tailwind design system
├─ → Tremor (purpose-built for dashboards)
└─ → shadcn-ui Charts (Recharts + shadcn styling)
The Data Viz Stack (2025)
Recommended Packages
{
"dependencies": {
"@observablehq/plot": "^0.6.0",
"recharts": "^2.12.0",
"@nivo/core": "^0.87.0",
"@visx/visx": "^3.10.0",
"d3": "^7.9.0",
"@tremor/react": "^3.15.0",
"framer-motion": "^11.0.0"
},
"devDependencies": {
"@percy/cli": "^1.29.0",
"@testing-library/react": "^14.2.0",
When to Use Each Library
Observable Plot - You want ggplot2/Vega-Lite in JavaScript
- Grammar-of-graphics approach (marks, scales, transforms)
- Perfect for rapid prototyping
- Great for notebooks and exploratory analysis
Recharts - You want it to "just work" in React
- Component-based (everything is a
<Component />)
- Excellent documentation and community
- TypeScript support built-in
- Smallest learning curve
Nivo - You want visually stunning results
- 20+ chart types with beautiful defaults
- Canvas, SVG, and HTML rendering
- Server-side rendering support (unique feature)
- Extensive customization via props
Visx - You want maximum control with React patterns
- Low-level primitives (scales, axes, shapes)
- Compose your own chart types
- Airbnb's D3 + React toolkit
- Best for novel visualizations
D3.js - You want unlimited power (and responsibility)
- Full control over every pixel
- Steepest learning curve
- Best for advanced, custom work
- Use with
useEffect and useRef in React
The Tufte Checklist
Before shipping any visualization, verify:
Read references/tufte-principles.md for deep dive.
The NYT Graphics Workflow
The New York Times graphics team process:
- Make 500 charts → Pick the one that displays information best
- Simplify within reason → Remove noise and clutter
- Annotate with insight → Words should highlight patterns, not just describe data
- Test with real users → Watch people interact, identify confusion
- Responsive by default → Mobile-first, progressive enhancement
Read references/nyt-workflow.md for case studies.
Animation & Micro-interactions
Data viz isn't static. Movement communicates:
When to Animate
- State transitions - Data updates, filter changes
- Draw attention - Highlight insights, guide the eye
- Show relationships - Morphing between views reveals structure
- Delight - Thoughtful motion = premium feel
Animation Principles
const springConfig = {
type: "spring",
stiffness: 300,
damping: 30
};
const staggerChildren = {
delayChildren: 0.1,
staggerChildren: 0.05
};
const shouldAnimate = !window.matchMedia('(prefers-reduced-motion: reduce)').matches;
Read references/animation-patterns.md for complete patterns library.
Color: Beyond the Rainbow
Semantic Color Systems
const categorical = [
"#d97706", "#7c3aed", "#059669", "#dc2626", "#2563eb"
];
const sequential = [
"#fef3c7", "#fcd34d", "#f59e0b", "#d97706", "#92400e"
];
const diverging = [
"#dc2626", "#f87171", "#fef2f2", "#c7d2fe", "#6366f1"
];
Accessibility Requirements
- Contrast ratio ≥4.5:1 for text on backgrounds
- Don't rely on color alone - Use shapes, patterns, labels
- Colorblind-safe palettes - Test with simulators
- Consider dark mode - Colors must work in both themes
Testing Data Visualizations
Visual Regression Testing
npx percy snapshot ./storybook-static
npx chromatic --project-token=<token>
Data Accuracy Testing
test('bar chart renders correct number of bars', () => {
const data = [{ x: 'A', y: 10 }, { x: 'B', y: 20 }];
render(<BarChart data={data} />);
const bars = screen.getAllByTestId('bar');
expect(bars).toHaveLength(2);
});
test('bar heights proportional to values', () => {
const data = [{ x: 'A', y: 10 }, { x: 'B', y: 20 }];
render(<BarChart data={data} />);
const bars = screen.getAllByTestId('bar');
const heights = bars.map(b => parseInt(b.style.height));
expect(heights[]).(heights[] * );
});
Read references/testing-strategies.md for comprehensive test suites.
Responsive Design Patterns
Mobile-First Approach
const ChartResponsive = ({ data }: Props) => {
const isMobile = useMediaQuery('(max-width: 640px)');
return (
<ResponsiveContainer width="100%" height={isMobile ? 200 : 400}>
<LineChart data={data}>
{!isMobile && <CartesianGrid strokeDasharray="3 3" />}
<XAxis
dataKey="date"
tick={isMobile ? { fontSize: 10 } : undefined}
interval={isMobile ? 'preserveStartEnd' : 'auto'}
/>
<YAxis tick={isMobile ? false : undefined} />
<Tooltip />
<Line type="monotone" = = />
);
};
Touch-Friendly Interactions
- Minimum touch target: 44×44px - Tooltips, buttons, interactive elements
- Swipe gestures - Navigate time series, change views
- Pinch-to-zoom - For dense charts (use carefully)
- Long-press context menus - Advanced actions
Data Storytelling
Every visualization tells a story. Follow the narrative arc:
- Hook - What's the surprising insight?
- Context - Why should we care?
- Evidence - Show the data clearly
- Conclusion - What should we do?
Narrative Techniques
- Scrollytelling - Charts animate as user scrolls
- Progressive disclosure - Start simple, reveal complexity
- Annotations - Point out the insight, don't make users hunt
- Comparison - Show before/after, us vs. them, expected vs. actual
Read references/data-storytelling.md for narrative frameworks.
Common Anti-Patterns
❌ The "Rainbow Vomit" Pie Chart
Problem: 12 colors, tiny slices, legend on the side
Solution: Max 5 categories, direct labels, consider bar chart instead
❌ The "Misleading Axis" Bar Chart
Problem: Y-axis doesn't start at zero, exaggerates differences
Solution: Always start at zero for bar charts (lines can vary)
❌ The "Dual-Axis Confusion" Line Chart
Problem: Two Y-axes with different scales mislead viewers
Solution: Use separate charts or normalize to same scale
❌ The "3D Perspective" Lie
Problem: 3D effects distort data perception
Solution: Stick to 2D, use color/size for third dimension
❌ The "Spinner of Death" Loading State
Problem: Empty screen with spinner for 2+ seconds
Solution: Skeleton loading that shows chart structure immediately
Read references/antipatterns.md for exhaustive catalog.
Implementation Workflow
1. Explore Your Data
npm install @observablehq/plot
2. Build Production Component
3. Test Thoroughly
npx percy snapshot
npm test -- --coverage
npx axe-core src/components/charts
4. Document & Deploy
AI-Enhanced Visualizations
When to Use Claude/Haiku
- Dynamic annotations - Generate insights from data
- Color palette suggestions - AI-powered color harmony
- Chart type recommendations - "What's the best way to show this?"
- Accessibility descriptions - Auto-generate alt text
Example: AI Annotation
const generateInsight = async (data: DataPoint[]) => {
const response = await fetch('/api/claude', {
method: 'POST',
body: JSON.stringify({
model: 'claude-haiku',
prompt: `Analyze this data and provide ONE key insight (max 15 words): ${JSON.stringify(data)}`
})
});
return response.text();
};
Inspiration Galleries
Study these regularly:
Performance Optimization
Bundle Size Management
import { LineChart } from 'recharts';
import LineChart from 'recharts/lib/chart/LineChart';
const HeavyChart = dynamic(() => import('./HeavyChart'), {
loading: () => <ChartSkeleton />,
ssr: false
});
Canvas vs SVG
- SVG - Better for < 1000 data points, accessibility, crisp at any scale
- Canvas - Better for > 1000 data points, animations, performance
- WebGL - Best for > 10,000 data points, 3D, gaming-level performance
Virtualization
For large datasets, render only visible portion:
Accessibility Standards (WCAG AA)
Requirements
- Keyboard navigation - All interactive elements accessible via Tab
- Screen reader support - Provide data tables as alternative
- Focus indicators - Visible focus states for interactive elements
- Color contrast - ≥4.5:1 for small text, ≥3:1 for large text
- Reduced motion - Respect
prefers-reduced-motion: reduce
Implementation
<figure role="img" aria-labelledby="chart-title chart-desc">
<h2 id="chart-title">Sales Over Time</h2>
<p id="chart-desc">
Line chart showing sales increased 45% from Q1 to Q4,
peaking in November at $2.3M.
</p>
<LineChart data={data} />
{}
<details>
<summary>View data table</summary>
<table>...</table>
</details>
</figure>
Reference Materials
This skill includes comprehensive reference documentation:
references/tufte-principles.md - Edward Tufte's data visualization principles with examples
references/library-comparison.md - Deep dive on Observable Plot, Recharts, Nivo, Visx, D3
references/testing-strategies.md - Visual regression, component testing, accessibility testing
references/animation-patterns.md - Motion design patterns for charts
references/data-storytelling.md - Narrative techniques and scrollytelling patterns
references/antipatterns.md - Common mistakes and how to avoid them
references/nyt-workflow.md - New York Times graphics team best practices
Utility Scripts
scripts/data-transform.ts - Common data transformations (rollup, pivot, normalize)
scripts/chart-test-helpers.ts - Testing utilities for verifying chart accuracy
scripts/color-palette-generator.ts - Generate accessible color palettes
scripts/performance-benchmark.ts - Benchmark chart rendering performance
Quick Start: Building Your First Chart
import { LineChart, Line, XAxis, YAxis, Tooltip, ResponsiveContainer } from 'recharts';
import { motion } from 'framer-motion';
const data = [
{ month: 'Jan', value: 400 },
{ month: 'Feb', value: 300 },
{ month: 'Mar', value: 600 },
];
export const SalesChart = () => (
<motion.div
initial={{ opacity: 0, y: 20 }}
animate={{ opacity: 1, y: 0 }}
transition={{ duration: 0.5 }}
>
<ResponsiveContainer width="100%" height={300}>
);
Remember: The best visualization is the one that makes the insight obvious. When in doubt, simplify. When confused, prototype 10 options. When shipping, test ruthlessly.
This skill guides: Chart selection | Library integration | Testing strategies | Animation patterns | Accessibility compliance | Performance optimization