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)

Plotting variability and confidence intervals better with ggdist

Confidence intervals are used to make inferences about a population based on a sample of data. They capture the variability of the data by providing a range of possible values for some parameter, rather than a single point estimate. The interval is a measure of how sure we are that the interval contains the true population parameter. It is common to show distributions and annotate them with range markers or confidence intervals. With this recipe, we will look at how to use ggplot’s ggdist extension to make informative and great-looking plots of distributions.

Getting ready

For this recipe, we need the ggdist, ggplot2, and palmerpenguins packages.

How to do it…

We can create plots with confidence intervals as follows:

  1. Create a raincloud plot:
    library(ggplot2)library(ggdist)library(palmerpenguins)ggplot(penguins) +  aes(x = flipper_length_mm, y = island) +  geom_dots...