visualizing-data

# 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:

1. **Selection Framework**: Systematic decision trees from data type + purpose → chart type
2. **24+ Visualization Methods**: Organized by analytical purpose
3. **Accessibility Patterns**: WCAG 2.1 AA compliance, colorblind-safe palettes
4. **Performance Strategies**: Optimize for dataset size (<1000 to >100K points)
5. **Multi-Language Support**: JavaScript/TypeScript (primary), Python, Rust, Go

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## 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

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## Purpose-First Selection

**Match analytical purpose to chart type:**

| Pur