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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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Toc

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

ggplot2 and Extensions for Publication Quality Plots

Clear and informative data visualizations are the most important tool that bioinformaticians have to effectively communicate complex data and findings to other scientists in the field. They allow for easy and efficient exploration and understanding of large and complex datasets. The process of creating a good visualization is very iterative, and many drafts of a visualization are discarded before a final one is settled on, so it is important that we have plotting tools that allow for quick and easy plot creation and customization.

ggplot2 is a popular data visualization library in R that provides an elegant solution for bioinformaticians. It is based on the Grammar of Graphics, a principle that allows users to easily create complex and customizable visualizations by breaking them down into small, modular components, defined by a consistent interface. These make ggplot2 highly flexible and allow for the creation of a wide variety...

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