Book Image

R Bioinformatics Cookbook - Second Edition

By : Dan MacLean
Book Image

R Bioinformatics Cookbook - Second Edition

By: Dan MacLean

Overview of this book

The updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You’ll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools. This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses. By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.
Table of Contents (16 chapters)

Customizing plots with ggeasy

One of the key aspects of customizing plots in ggplot2 is the theme() function, which allows users to customize elements of the plot’s overall appearance. Customizing plots in ggplot2 can be a little unintuitive. Although the theme() function is powerful, it does require the user to manually specify each element of the plot, such as axis labels, titles, colors, and shapes. The ggeasy package, built on top of ggplot2, aims to make plot customization more accessible by providing a simpler, more intuitive syntax for many common customization tasks. ggeasy provides a set of simple wrapper functions around theme() that make the important things a lot easier to remember. With this recipe, we’ll look at customizing labels, legends, and axes in a plot created initially in ggplot2.

Getting ready

We’ll need the ggplot2, ggeasy, and palmerpenguins packages.

How to do it…

We can customize a plot as follows.

Make a base plot...