Data Visualization Component Library

Systematic guidance for selecting and implementing effective data visualizations, matching data characteristics with appropriate visualization types, ensuring clarity, accessibility, and impact.

Overview

Data visualization transforms raw data into visual representations that reveal patterns, trends, and insights. This skill provides:

Selection Framework: Systematic decision trees from data type + purpose → chart type
24+ Visualization Methods: Organized by analytical purpose
Accessibility Patterns: WCAG 2.1 AA compliance, colorblind-safe palettes
Performance Strategies: Optimize for dataset size (<1000 to >100K points)
Multi-Language Support: JavaScript/TypeScript (primary), Python, Rust, Go

Quick Start Workflow

Step 1: Assess Data

What type? [categorical | continuous | temporal | spatial | hierarchical]
How many dimensions? [1D | 2D | multivariate]
How many points? [<100 | 100-1K | 1K-10K | >10K]

Step 2: Determine Purpose

What story to tell? [comparison | trend | distribution | relationship | composition | flow | hierarchy | geographic]

Step 3: Select Chart Type

Quick Selection:

Compare 5-10 categories → Bar Chart
Show sales over 12 months → Line Chart
Display distribution of ages → Histogram or Violin Plot
Explore correlation → Scatter Plot
Show budget breakdown → Treemap or Stacked Bar

Complete decision trees: See

references/selection-matrix.md

Step 4: Implement

See language sections below for recommended libraries.

Step 5: Apply Accessibility

Add text alternative (aria-label)
Ensure 3:1 color contrast minimum
Use colorblind-safe palette
Provide data table alternative

Step 6: Optimize Performance

<1000 points: Standard SVG rendering
1000 points: Sampling or Canvas rendering
Very large: Server-side aggregation

Purpose-First Selection

Match analytical purpose to chart type:

Purpose	Chart Types
Compare values	Bar Chart, Lollipop Chart
Show trends	Line Chart, Area Chart
Reveal distributions	Histogram, Violin Plot, Box Plot
Explore relationships	Scatter Plot, Bubble Chart
Explain composition	Treemap, Stacked Bar, Pie Chart (<6 slices)
Visualize flow	Sankey Diagram, Chord Diagram
Display hierarchy	Sunburst, Dendrogram, Treemap
Show geographic	Choropleth Map, Symbol Map

Visualization Catalog

Tier 1: Fundamental Primitives

General audiences, straightforward data stories:

Bar Chart: Compare categories
Line Chart: Show trends over time
Scatter Plot: Explore relationships
Pie Chart: Part-to-whole (max 5-6 slices)
Area Chart: Emphasize magnitude over time

Tier 2: Purpose-Driven

Specific analytical insights:

Comparison: Grouped Bar, Lollipop, Bullet Chart
Trend: Stream Graph, Slope Graph, Sparklines
Distribution: Violin Plot, Box Plot, Histogram
Relationship: Bubble Chart, Hexbin Plot
Composition: Treemap, Sunburst, Waterfall
Flow: Sankey Diagram, Chord Diagram

Tier 3: Advanced

Complex data, sophisticated audiences:

Multi-dimensional: Parallel Coordinates, Radar Chart, Small Multiples
Temporal: Gantt Chart, Calendar Heatmap, Candlestick
Network: Force-Directed Graph, Adjacency Matrix

Detailed descriptions: See

references/chart-catalog.md

Accessibility Requirements (WCAG 2.1 AA)

Text Alternatives

html

<figure role="img" aria-label="Sales increased 15% from Q3 to Q4">
  <svg>...</svg>
</figure>

Color Requirements

Non-text UI elements: 3:1 minimum contrast
Text: 4.5:1 minimum (or 3:1 for large text ≥24px)
Don't rely on color alone - use patterns/textures + labels

Colorblind-Safe Palettes

IBM Palette (Recommended):

#648FFF (Blue), #785EF0 (Purple), #DC267F (Magenta),
#FE6100 (Orange), #FFB000 (Yellow)

Avoid: Red/Green combinations (8% of males have red-green colorblindness)

Keyboard Navigation

Tab through interactive elements
Enter/Space to activate tooltips
Arrow keys to navigate data points

Complete accessibility guide: See

references/accessibility.md

Performance by Data Volume

Rows	Strategy	Implementation
<1,000	Direct rendering	Standard libraries (SVG)
1K-10K	Sampling/aggregation	Downsample to ~500 points
10K-100K	Canvas rendering	Switch from SVG to Canvas
>100K	Server-side aggregation	Backend processing

JavaScript/TypeScript Implementation

Recharts (Business Dashboards)

Composable React components, declarative API, responsive by default.

bash

npm install recharts

tsx

import { LineChart, Line, XAxis, YAxis, Tooltip, ResponsiveContainer } from 'recharts';

const data = [
  { month: 'Jan', sales: 4000 },
  { month: 'Feb', sales: 3000 },
  { month: 'Mar', sales: 5000 },
];

export function SalesChart() {
  return (
    <ResponsiveContainer width="100%" height={300}>
      <LineChart data={data}>
        <XAxis dataKey="month" />
        <YAxis />
        <Tooltip />
        <Line type="monotone" dataKey="sales" stroke="#8884d8" />
      </LineChart>
    </ResponsiveContainer>
  );
}

D3.js (Custom Visualizations)

Maximum flexibility, industry standard, unlimited chart types.

bash

npm install d3

Plotly (Scientific/Interactive)

3D visualizations, statistical charts, interactive out-of-box.

bash

npm install react-plotly.js plotly.js

Detailed examples: See

references/javascript/

Python Implementation

Common Libraries:

Plotly - Interactive charts (same API as JavaScript)
Matplotlib - Publication-quality static plots
Seaborn - Statistical visualizations
Altair - Declarative visualization grammar

When building Python implementations:

Follow universal patterns above
Use RESEARCH_GUIDE.md to research libraries
Add to
```
references/python/
```

Integration with Design Tokens

Reference the design-tokens skill for theming:

css

--chart-color-primary
--chart-color-1 through --chart-color-10
--chart-axis-color
--chart-grid-color
--chart-tooltip-bg

tsx

<Line stroke="var(--chart-color-primary)" />

Light/dark/high-contrast themes work automatically via design tokens.

Common Mistakes to Avoid

Chart-first thinking - Choose based on data + purpose, not aesthetics
Pie charts for >6 categories - Use sorted bar chart instead
Dual-axis charts - Usually misleading, use small multiples
3D when 2D sufficient - Adds complexity, reduces clarity
Rainbow color scales - Not perceptually uniform, not colorblind-safe
Truncated y-axis - Indicate clearly or start at zero
Too many colors - Limit to 6-8 distinct categories
Missing context - Always label axes, include units

Quick Decision Tree

START: What is your data?

Categorical (categories/groups)
  ├─ Compare values → Bar Chart
  ├─ Show composition → Treemap or Pie Chart (<6 slices)
  └─ Show flow → Sankey Diagram

Continuous (numbers)
  ├─ Single variable → Histogram, Violin Plot
  └─ Two variables → Scatter Plot

Temporal (time series)
  ├─ Single metric → Line Chart
  ├─ Multiple metrics → Small Multiples
  └─ Daily patterns → Calendar Heatmap

Hierarchical (nested)
  ├─ Proportions → Treemap
  └─ Show depth → Sunburst, Dendrogram

Geographic (locations)
  ├─ Regional aggregates → Choropleth Map
  └─ Point locations → Symbol Map

References

Selection Guides:

```
references/chart-catalog.md
```
- All 24+ visualization types
```
references/selection-matrix.md
```
- Complete decision trees

Technical Guides:

```
references/accessibility.md
```
- WCAG 2.1 AA patterns
```
references/color-systems.md
```
- Colorblind-safe palettes
```
references/performance.md
```
- Optimization by data volume

Language-Specific:

```
references/javascript/
```
- React, D3.js, Plotly examples
```
references/python/
```
- Plotly, Matplotlib, Seaborn

Assets:

```
assets/color-palettes/
```
- Accessible color schemes
```
assets/example-datasets/
```
- Sample data for testing

Examples

Working code examples:

```
examples/javascript/bar-chart.tsx
```
```
examples/javascript/line-chart.tsx
```
```
examples/javascript/scatter-plot.tsx
```

examples/javascript/accessible-chart.tsx

bash

cd examples/javascript && npm install && npm start

Validation

bash

# Validate accessibility
scripts/validate_accessibility.py <chart-html>

# Test colorblind
# Use browser DevTools color vision deficiency emulator

Progressive disclosure: This SKILL.md provides overview and quick start. Detailed documentation, code examples, and language-specific implementations in

references/

and

examples/

directories.

visualizing-data

NPX Install

SKILL.md Content

Data Visualization Component Library

Overview

Quick Start Workflow

Step 1: Assess Data

Step 2: Determine Purpose

Step 3: Select Chart Type

Step 4: Implement

Step 5: Apply Accessibility

Step 6: Optimize Performance

Purpose-First Selection

Visualization Catalog

Tier 1: Fundamental Primitives

Tier 2: Purpose-Driven

Tier 3: Advanced

Accessibility Requirements (WCAG 2.1 AA)

Text Alternatives

Color Requirements

Colorblind-Safe Palettes

Keyboard Navigation

Performance by Data Volume

JavaScript/TypeScript Implementation

Recharts (Business Dashboards)

D3.js (Custom Visualizations)

Plotly (Scientific/Interactive)

Python Implementation

Integration with Design Tokens

Common Mistakes to Avoid

Quick Decision Tree

References

Examples

Validation