Additional Resources

Computational Education and Skills Training


R for Data Science, by Hadley Wickham and Garrett Grolemund

This book teaches how to do data science with R: this includes how to import data into R, load it into the most useful structure, transform it, visualize it, and model it. Also includes ​​an introduction to the grammar of graphics, literate programming, and best practices for reproducible research.


R Graphics Cookbook by Winston Chang

A practical guide that provides more than 150 recipes for generating high-quality graphs quickly, without having to comb through all the details of R’s graphic systems.


R Workflow, by Frank Harrel 

This book covers a very useful application for reproducible research reports. This is a general primer for using R and Quatro with many examples of code, output, and interactive graphics.


Mastering Shiny by Hadley Wickham

A primer for going from knowing nothing about Shiny to developing complex apps using Shiny’s reactive programming model. by Posit (formerly RStudio)

The website is a Posit Connect (formerly RStudio Connect) server provided by Posit to host books. It is free to publish the static output files of your book, and you hold the full copyright of your own books. Many interesting books are provided mostly covering various topics implemented in R.


Modern Statistics for Modern Biology, by Susan Holmes and Wolfgang Huber

Statistical analysis of biological high-throughput data with a focus on computational functionality in R and Bioconductor. Covers fundamental concepts of statistical analysis such as important probability distributions, generative models, and hypothesis testing with applications to real data. Provides statistical theory as well numerous code examples for various analysis steps including normalization, clustering, dimensionality reduction, and differential expression analysis.


Orchestrating Single-Cell Analysis with Bioconductor, by Robert Amezquita, Aaron Lun, Stephanie Hicks, and Raphael Gottardo

Comprehensive introduction to using the Bioconductor ecosystem for single-cell RNA-seq analysis. Includes walk-throughs for various steps of typical analysis workflows using real world example datasets. This includes computational methods, standards for data representation and manipulation, and interactive data visualization tools.