Data Visualization with R

# Data Visualization with R

Effective data visualization is crucial for understanding patterns, communicating insights, and making data-driven decisions.

## Learning Objectives

In this lecture, you will learn:
- Grammar of Graphics principles
- ggplot2 fundamentals
- Different plot types and their uses
- Color theory and design principles
- Interactive visualizations

## Grammar of Graphics

**Components**:
- Data: The dataset
- Aesthetics: Visual mappings (x, y, color, size)
- Geometries: Plot types (points, lines, bars)
- Scales: Axis and legend controls
- Facets: Small multiples
- Themes: Overall appearance

## Essential Plot Types

**Univariate**:
- Histograms and density plots
- Box plots and violin plots
- Bar charts and pie charts

**Bivariate**:
- Scatter plots
- Line plots
- Heatmaps

**Multivariate**:
- Scatter plot matrices
- Parallel coordinate plots
- Treemaps

## Design Principles

- **Clarity**: Clear and readable
- **Accuracy**: Faithful to data
- **Efficiency**: Maximum insight per pixel
- **Aesthetics**: Visually appealing

## Advanced Techniques

- Custom themes and styling
- Annotations and labels
- Interactive plots with plotly
- Animation with gganimate
- Maps and spatial data

## Best Practices

- Choose appropriate plot types
- Use color effectively
- Avoid misleading visualizations
- Consider your audience
- Test with different data