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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

Using the map family of functions in purrr

R’s base functional programming tool kit is a little sparse. The purrr package was created in order to extend it and create a complete and consistent set of tools for working with functions and data structures. purrr provides a set of functions for functional programming, including the widely used map family of functions. The map functions allow you to iterate over a collection (such as a list or vector) and apply a function to each element, returning the results as a new list or vector.

The functions vary according to what they expect as input, what they iterate over, and what types and structures they return. Like the apply functions, they can simplify repetitive tasks, such as data manipulation or model fitting, by automatically handling the iteration process for you but by ensuring they always return the specified types and structures they help us to build more streamlined and effective code that is less prone to bugs.

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