Learn how to create data visualizations in R using ggplot2's layered Grammar of Graphics approach.
Tutorial series
R Data Visualization
11 tutorials — follow in order for the best learning path.
- ggplot2 Basics
- Introduction to ggplot2
Learn the basics of ggplot2, the most popular data viz package in R. Covers the grammar of graphics, aesthetic mappings, and creating your first plots.
- Customizing ggplot2 Charts
Learn how to customize ggplot2 charts with custom themes, colors, labels, scales, and legends.
- Customizing ggplot2 Themes
Learn to customize ggplot2 themes with theme(), element_text(), element_line(), and element_rect(). Build reusable themes for data visualizations.
- Faceting in ggplot2
Split your plots into panels to compare data subsets side by side. Learn facet_wrap() and facet_grid() with practical examples.
- Facets, Scales, and Themes in ggplot2
Learn to create multi-panel plots with facets, customize scales for precise axis and color control, and apply professional themes in ggplot2.
- Advanced Geoms in ggplot2
Master advanced ggplot2 geometries including box plots, violin plots, density plots, and multi-layer visualizations for effective data visualization in R.
- Maps with ggplot2
Learn to create maps in R with ggplot2, covering choropleth maps, bubble maps, sf objects, and geographic projections for spatial visualizations.
- Interactive Plots with plotly
Learn how to create interactive plots with plotly in R. Convert ggplot2 charts, customize tooltips, and build interactive dashboards.
- Publication-Ready Figures in R
Learn how to export ggplot2 figures that meet journal standards—correct dimensions, DPI, embedded fonts, and colorblind-safe palettes.
- Animations with gganimate
Learn to create animated charts in R with gganimate—transform your ggplot2 plots into smooth, shareable animations.