Lesson Contents

• Introduction to Advanced Data Visualization
• Principles of Effective Data Visualization
• Choosing the Right Visualization for Different Data Types
• Advanced Features of ggplot2
• Custom Themes and Publication-Quality Formatting
• Working with Color Palettes (RColorBrewer, viridis, ggsci)
• Interactive Visualizations with plotly
• Combining Multiple Plots using patchwork and cowplot
• Faceted Visualizations for Multi-Panel Figures
• Correlation Heatmaps
• Clustered Heatmaps
• Bubble Charts
• Violin Plots
• Ridge (Joy) Plots
• Raincloud Plots
• Beeswarm and Sina Plots
• Hexbin and 2D Density Plots
• Pair Plots using GGally
• Correlation Matrix Visualization
• PCA Visualization
• Biplots
• Dendrograms and Hierarchical Clustering Plots
• Volcano Plots
• MA Plots
• Manhattan Plots (GWAS)
• Network Graphs
• Sankey Diagrams
• Alluvial Plots
• Chord Diagrams
• Treemaps
• Mosaic Plots
• Radar (Spider) Charts
• Circular Bar Charts
• Lollipop Charts
• Cleveland Dot Plots
• Waterfall Charts
• Funnel Charts
• Time Series Visualization
• Seasonal and Trend Plots
• Geospatial Mapping with ggmap and sf
• Choropleth Maps
• Spatial Point Maps
• 3D Visualization (Introduction)
• Creating Interactive Dashboards with Plotly
• Annotating and Highlighting Important Results
• Creating Multi-Figure Layouts for Publications
• Exporting High-Resolution Figures (PNG, TIFF, PDF, SVG, EPS)
• Creating Publication-Ready Figures for Q1 Journals
• Best Practices for Advanced Data Visualization
• Common Visualization Mistakes to Avoid
• Practical Examples Using Real-World Datasets
• Hands-on Exercises
• Chapter Summary
• Practice Questions
• Assignment