Most programmers test code obsessively and the practice of unit testing has arisen so that we have a formal way of testing functions that can be automated and help to reduce the time it takes to build even moderately complex code projects. A well-engineered and maintained software package has a unit test suite for as many of its component functions as it is possible to do. In this recipe, we'll look at how to use the usethis package to add the component files and folders for an automated test suite that uses the testthat package. It's beyond the scope of this book to look at the philosophy of why and how to write tests in any detail, but you can check out the testthat package documentation here, https://testthat.r-lib.org/, for a nice primer.
R Bioinformatics Cookbook
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R Bioinformatics Cookbook
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Overview of this book
Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples.
This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse.
By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.
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Current Title:
R Bioinformatics Cookbook
Table of Contents (13 chapters)
Preface
Free Chapter
Performing Quantitative RNAseq
Finding Genetic Variants with HTS Data
Searching Genes and Proteins for Domains and Motifs
Phylogenetic Analysis and Visualization
Metagenomics
Proteomics from Spectrum to Annotation
Producing Publication and Web-Ready Visualizations
Working with Databases and Remote Data Sources
Useful Statistical and Machine Learning Methods
Programming with Tidyverse and Bioconductor
Building Objects and Packages for Code Reuse
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