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)

Combining many plot types in ggplot2

The layer model of ggplot2 is a key feature of the library that allows users to create complex visualizations by building up layers of data, aesthetics, and geoms. Each layer represents a different aspect of the plot, and they are added on top of each other to create the final visualization. In this recipe, we’ll use the layer model to create a complex plot of data in the palmerpenguins package. It may be helpful to inspect the data in R directly by printing it to the screen. Also, the package is well documented at https://allisonhorst.github.io/palmerpenguins/, should you wish to look more into how it was generated.

Getting ready

Install the ggplot2 and palmerpenguins packages.

How to do it…

We can use the layer system to combine multiple plot types as follows:

  1. Create the base for the plot:
    library(ggplot2)library(palmerpenguins)p <- ggplot(data = penguins) +  aes(x = bill_length_mm, y = bill_depth_mm...