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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

Preface

In biology, genetics and genomics data is the driver of discovery. To harness the power of data, bioinformaticians rely on computational tools and none is more powerful in the statistical and visualization world than R. R Bioinformatics Cookbook, Second Edition, is designed to help you take control of all manner of bioinformatics analyses and to help you navigate the intricate world of bioinformatics with R.

The R Bioinformatics Cookbook, Second Edition book, is a resource for getting good work done quickly. It will help researchers to sharpen their skills and broaden their knowledge of important packages such as the tidyverse for data management, ggplot for visualization, and mlr for machine learning and delves deeply into the comprehensive Bioconductor framework of tools tailored specifically toward the most taxing and common analyses in bioinformatics.

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