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R Bioinformatics Cookbook

You're reading from   R Bioinformatics Cookbook Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781789950694
Length 316 pages
Edition 1st Edition
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Authors (2):
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Dr Dan Maclean Dr Dan Maclean
Author Profile Icon Dr Dan Maclean
Dr Dan Maclean
Dan MacLean Dan MacLean
Author Profile Icon Dan MacLean
Dan MacLean
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Toc

Table of Contents (13) Chapters Close

Preface 1. Performing Quantitative RNAseq 2. Finding Genetic Variants with HTS Data FREE CHAPTER 3. Searching Genes and Proteins for Domains and Motifs 4. Phylogenetic Analysis and Visualization 5. Metagenomics 6. Proteomics from Spectrum to Annotation 7. Producing Publication and Web-Ready Visualizations 8. Working with Databases and Remote Data Sources 9. Useful Statistical and Machine Learning Methods 10. Programming with Tidyverse and Bioconductor 11. Building Objects and Packages for Code Reuse 12. Other Books You May Enjoy

Making base R objects tidy

The tidyverse set of packages (including dplyr, tidyr, and magrittr) have had a huge influence on data processing and analysis in R through their application of the tidy way of working. In essence, this means that data is kept in a particular tidy format, in which each row holds a single observation and each column keeps all observations of a single variable. Such a structure means that analytical steps have predictable inputs and outputs and can be built into fluid and expressive pipelines. However, most base R objects are not tidy and can often need significant programming work to extract the bits that are needed to assemble objects for use downstream. In this recipe, we'll look at some functions for automatically converting some common base R objects into a tidy dataframe.

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