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R Bioinformatics Cookbook - Second Edition

You're reading from  R Bioinformatics Cookbook - Second Edition

Product type Book
Published in Oct 2023
Publisher Packt
ISBN-13 9781837634279
Pages 396 pages
Edition 2nd Edition
Languages
Author (1):
Dan MacLean Dan MacLean
Profile icon Dan MacLean

Table of Contents (16) Chapters

Preface 1. Chapter 1: Setting Up Your R Bioinformatics Working Environment 2. Chapter 2: Loading, Tidying, and Cleaning Data in the tidyverse 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

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...
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