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R Bioinformatics Cookbook

You're reading from   R Bioinformatics Cookbook Utilize R packages for bioinformatics, genomics, data science, and machine learning

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781837634279
Length 396 pages
Edition 2nd Edition
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Author (1):
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Dan MacLean Dan MacLean
Author Profile Icon Dan MacLean
Dan MacLean
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Table of Contents (16) Chapters Close

Preface 1. Chapter 1: Setting Up Your R Bioinformatics Working Environment 2. Chapter 2: Loading, Tidying, and Cleaning Data in the tidyverse FREE CHAPTER 3. Chapter 3: ggplot2 and Extensions for Publication Quality Plots 4. Chapter 4: Using Quarto to Make Data-Rich Reports, Presentations, and Websites 5. Chapter 5: Easily Performing Statistical Tests Using Linear Models 6. Chapter 6: Performing Quantitative RNA-seq 7. Chapter 7: Finding Genetic Variants with HTS Data 8. Chapter 8: Searching Gene and Protein Sequences for Domains and Motifs 9. Chapter 9: Phylogenetic Analysis and Visualization 10. Chapter 10: Analyzing Gene Annotations 11. Chapter 11: Machine Learning with mlr3 12. Chapter 12: Functional Programming with purrr and base R 13. Chapter 13: Turbo-Charging Development in R with ChatGPT 14. Index 15. Other Books You May Enjoy

Comparing changes in distributions with ggridges

Ridge plots, also known as joyplots, are a visualization tool that allows for the clear comparison of multiple distributions in a single plot. The ggridges R package provides an easy-to-use implementation of ridge plots, allowing for the clear comparison of multiple distributions of a single variable by superimposing them on top of each other in a single plot. The package also allows for easy customization of plot features such as color, fill, and theme. The ggridges package is particularly useful for comparing the distribution of a single variable across multiple groups or categories. In this recipe, we will look at implementing some useful ridge plots.

Getting ready

We will need the ggplot2, ggridges, and palmerpenguins packages.

How to do it…

We can look at the changes in distributions using the following steps:

  1. Plot overlapping distributions:
    library(ggplot2)library(ggridges)library(palmerpenguins)ggplot...
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