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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 use of the apply family of functions

Programming in R can sometimes seem a bit tricky; the control flow and looping structures it has, are a bit more basic than in other languages. As many R functions are vectorized, the language actually has some features and functions; that mean we don't need to take the same low-level approach we may have learned in Python or other places. Instead, base R provides the apply functions to do the job of common looping tasks. These functions all have a loop inside them, meaning we don't need to specify the loop manually. In this recipe, we'll look at using some apply family functions with common data structures to loop over them and get a result. The common thread in all of the apply functions is that we have an input data structure that we're going to iterate over and some code (often wrapped in a function definition...

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