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)

Making base R objects “tidy”

The tidyverse packages (including dplyr, tidyr, and ggplot2) have had a huge influence on data processing and analysis in R, through their application of the “tidy” way of working. In essence, “tidy” means that data is kept in a particular format, in which each row holds a single observation of some variable , and columns specify the variables recorded and contain all values for those variables across all observations. Such a structure means that analytical steps have predictable input and output and can be built into complex pipelines with relative ease. Most base R objects are not tidy, and it can often take significant programming work to extract the parts that are needed downstream. In this recipe, we will look at some functions to automatically convert some common base R objects into a tidy dataframe.

Getting ready

We’ll need tidyr, broom, and also biobroom from Bioconductor. For data, we’...