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)

Setting up your machine for the compilation of source packages

Compilation of source code is the process of converting human-readable code into machine-readable code that can be executed by a computer. It varies on different chips and operating systems because the machine code that is produced must be specific to the architecture of the target platform. A compiler is a program that performs the compilation process.

Some R packages are written in C or Fortran because they are low-level programming languages that can execute faster than R in certain situations. Additionally, C code can be easily integrated into R packages, allowing for faster computations and improved performance. To take advantage of the better performance, some R packages from the source will need to have their foreign language parts compiled. In this brief recipe, we’ll look at tricks and shims for installing code that must be compiled on various platforms.

Getting ready

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