What this book covers

Chapter 1, Setting Up Your R Bioinformatics Working Environment, shows how to set up your computer and toolchain for easy and efficient work.

Chapter 2, Loading, Tidying, and Cleaning Data in the tidyverse, shows how to load and prepare external tabular data for use in complex pipelines.

Chapter 3, ggplot2 and Extensions for Publication Quality Plots, explains the structure of ggplots and how to create attractive and informative plots of many types.

Chapter 4, Using Quarto to Make Data-Rich Reports, Presentations, and Websites, covers how to mix code and written text into literate computing documents in a powerful and flexible way.

Chapter 5, Easily Performing Statistical Tests Using Linear Models, explores how to do the most common tests in bioinformatics in R’s powerful statistical model framework.

Chapter 6, Performing Quantitative RNA-seq, uses the latest, most widely used tools for RNA-seq, including EdgeR, DESeq2, and sleuth.

Chapter 7, Finding Genetic Variants with HTS Data, uses powerful Bioconductor packages to work with high-throughput genome sequencing data.

Chapter 8, Searching Gene and Protein Sequences for Domains and Motifs, explores functional sequence features using predictive tools and databases.

Chapter 9, Phylogenetic Analysis and Visualization, looks at carrying out genome and gene alignments and how to create attractive and informative phylogenetic trees.

Chapter 10, Analyzing Gene Annotations, shows how to infer biological properties of gene sets from annotations of those genes.

Chapter 11, Machine Learning with mlr3, explains how to develop effective and useful pipelines for machine learning with the powerful and flexible mlr3 package.

Chapter 12, Functional Programming with purrr and base R, shows how to apply functional programming styles to streamline and empower your analysis pipelines.

Chapter 13, Turbo-Charging Development in R with ChatGPT, explains how to make R code development and testing easier by making use of the latest ChatGPT models.