Visualization 101 2026
Data Visualization In R
1 Visualization 101 2026
This book is the working text for a five-day course on data visualization in R. It is designed to be read, run, edited, rendered, and reused. The goal is not only to make attractive graphs, but to make visual choices that clarify an argument, reveal a pattern, and leave a reproducible record of how the result was made.
Visualization is a sequence of choices. A plot emphasizes some comparisons and pushes others into the background. It chooses what counts as the main unit, what deserves color or space, what should be labeled directly, what can remain implicit, and what should be excluded because it distracts from the point. The course therefore treats visualization as part of analysis rather than as decoration added at the end.
R is the statistical language used throughout the course. RStudio Desktop and Posit Cloud are the interfaces used to write and run code. The tidyverse is the central programming framework: it supplies a readable grammar for importing, reshaping, summarizing, and plotting data. ggplot2, the tidyverse visualization package, is the main plotting system.
Quarto is the document system used for the course files. A Quarto document can contain prose, code, output, figures, tables, citations, and links in one place. The same source document can produce HTML, PDF, slides, dashboards, and reports. When the data or code changes, the outputs can be regenerated from the source instead of manually updated across Word documents, spreadsheets, and slide decks.
Literate programming also supports replication. Code and interpretation live together, which makes it easier to return to a project after a long break, share work with collaborators, and identify exactly how a result was produced. Writing the explanation forces decisions to be made explicit while they are still fresh.
1.1 Schedule
| Day | Main Focus | Chapters |
|---|---|---|
| 1 | Workflow, Quarto, data import, and an introduction to ggplot2 |
2–3, start 4 |
| 2 | Core ggplot2, separating and comparing data, distributions |
finish 4, 5–6b |
| 3 | Lines, bars, annotation, and from question to final figure | 7–8 |
| 4 | Interactivity, dashboards, and tables | 9–10 |
| 5 | Model visualization, mapping, and agentic AI | 11–13 |
Each day has five instructional sessions. The written chapters contain more material than the minimum live path so that faster pacing, review, and post-course study are supported.
1.2 Setup
The Posit Cloud project should already include the packages needed for the course. The file install_packages.R automates installation if the materials are moved to a local computer.
Run check_setup.R to verify that the packages and data files are available.
1.3 Files
The main book chapters are the numbered .qmd files in this project. Data files are under Data/. Standalone examples are under Demos/. Short exercises and solutions are under Exercises/.
The course examples use files included with the project, such as Data/gapminder/gapminder.csv.