# Data Visualization Types and Best Practices

## Chart Selection Guide

### When to Use Each Chart Type

**Line Charts**
- Time series data
- Trend analysis
- Multiple data series comparison over time

**Bar Charts** 
- Categorical comparisons
- Discrete data points
- Ranking or ordering data

**Scatter Plots**
- Correlation analysis
- Distribution patterns
- Outlier detection

**Pie Charts**
- Part-to-whole relationships
- Limited categories (2-6 maximum)
- Percentage breakdowns

**Heat Maps**
- Matrix data visualization
- Density or intensity representation
- Multi-dimensional categorical data

## Color Schemes

### Accessibility Guidelines
- Ensure sufficient contrast ratios (minimum 4.5:1)
- Avoid red-green combinations for colorblind users
- Test palettes with colorblind simulators

### Recommended Palettes
- **Sequential**: Blues, Greens, Purples (for ordered data)
- **Diverging**: Red-Blue, Orange-Purple (for data with meaningful midpoint)
- **Qualitative**: Set1, Set2, Dark2 (for categorical data)

## Interactive Features

### Essential Interactions
- Hover tooltips with detailed values
- Click-to-filter functionality
- Zoom and pan capabilities
- Legend toggling for series visibility

### Advanced Interactions
- Brush selection for range filtering
- Cross-chart linking and brushing
- Dynamic parameter adjustment
- Export functionality (PNG, SVG, CSV)

## Performance Optimization

### Large Dataset Handling
- Data aggregation and sampling strategies
- Progressive rendering techniques
- Web worker implementation for heavy computations
- Virtual scrolling for large datasets

### Rendering Best Practices
- Use canvas/WebGL for >10k data points
- Implement lazy loading for complex visualizations
- Optimize animation frame rates (60fps target)
- Minimize DOM manipulation in web-based visualizations

## Framework Recommendations

### Python Ecosystem
- **Matplotlib**: Publication-quality static plots
- **Plotly**: Interactive web-based visualizations  
- **Seaborn**: Statistical data visualization
- **Bokeh**: Interactive web applications

### JavaScript Ecosystem
- **D3.js**: Maximum flexibility and control
- **Chart.js**: Simple, responsive charts
- **Highcharts**: Enterprise-grade commercial solution
- **Observable Plot**: Declarative visualization grammar

### R Ecosystem
- **ggplot2**: Grammar of graphics implementation
- **plotly**: Interactive R visualizations
- **shiny**: Interactive web applications

## Output Formats

### Static Formats
- **PNG**: Raster format for web and presentations
- **SVG**: Vector format for scaling and editing
- **PDF**: Print-ready vector format
- **EPS**: Legacy vector format for academic publishing

### Interactive Formats
- **HTML**: Self-contained interactive documents
- **JSON**: Data format for web applications
- **Embeddable widgets**: iframe-compatible components

## Validation Checklist

Before finalizing any visualization:

✅ Data accuracy verified
✅ Appropriate chart type selected
✅ Labels and legends clear and complete
✅ Color scheme accessible and meaningful
✅ Scale and axes properly configured
✅ Performance optimized for target audience
✅ Export options provided as needed
✅ Mobile responsiveness tested (if applicable)