Appendix B — Further reading, cheat sheets, and datasets

Author

Affiliation

Sean Davis

University of Colorado
Anschutz School of Medicine

No single book can cover everything, and this one does not try to. This appendix collects the resources I reach for most often: quick-reference cheat sheets, free online books that go deeper than we can here, documentation for the key packages used throughout the chapters, and the datasets and interactive tools that support the material. Treat it as a launch pad for the self-directed learning the preface argues for.

B.1 Cheat sheets

Cheat sheets are dense, one- or two-page summaries you keep open beside your code. Posit (formerly RStudio) maintains a gallery of polished cheat sheets for the tidyverse and the RStudio IDE, and the Bioconductor project publishes one for its core classes.

• Posit cheat sheet gallery — the hub for all of the sheets below (and the source repository)
• Base R cheat sheet
• RStudio IDE cheat sheet — see the RStudio chapter
• Data transformation with dplyr — see the dplyr chapter
• Data visualization with ggplot2 — see the visualization chapters
• Tidy data with tidyr
• Reading data with readr — see Reading and writing data
• Factors with forcats — see the factors chapter

B.2 Free online books

• R for Data Science (2e) — the standard tidyverse introduction
• Hands-on Programming with R — gentle, project-driven; the source of several figures in this book
• Modern Data Visualization with R
• ggplot2: Elegant Graphics for Data Analysis — the definitive ggplot2 reference
• Advanced R — for when you want to understand how R works
• Bioconductor: an introduction — Carpentries-style Bioconductor onboarding
• Orchestrating Single-Cell Analysis with Bioconductor (OSCA) — companion to the single-cell chapter
• Computational Genomics with R

B.3 Package and project documentation

• Bioconductor — the project home; every package has a vignette worth reading
• The tidyverse — dplyr, ggplot2, tidyr, and friends
• mlr3 book — the framework used in the machine learning chapters
• ComplexHeatmap reference — production heatmaps beyond pheatmap
• R Graph Gallery and From Data to Viz — pick the right chart and copy the code

B.4 Interactive practice: swirl

swirl teaches you R inside R: it runs lessons at the console and checks your answers as you go. To get started, install and launch it:

install.packages("swirl")
library("swirl")
swirl()

Then follow the prompts and pick a course.

B.5 Datasets used in this book

• BRFSS subset — the Behavioral Risk Factor Surveillance System sample analyzed in the EDA case study

Most other datasets are pulled directly from packages or public repositories (NCBI GEO, ENCODE, Bioconductor data packages) by the chapters that use them, so there is nothing to download by hand.

B.6 AI assistants

Large language models are a genuinely useful study partner for R and statistics. Rather than list them here, this book devotes a full appendix to using them well — see AI Tools for Enhanced Learning and Productivity.