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R Bioinformatics Cookbook
R Bioinformatics Cookbook

R Bioinformatics Cookbook: Utilize R packages for bioinformatics, genomics, data science, and machine learning , Second Edition

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R Bioinformatics Cookbook

Loading, Tidying, and Cleaning Data in the tidyverse

Cleaning data is a crucial step in the data science process. It involves identifying and correcting errors, inconsistencies, and missing values in the data, as well as formatting and structuring the data in a way that makes it easy to work with. This allows the data to be used effectively for analysis, modeling, and visualization. The R tidyverse is a collection of packages designed for data science and includes tools for data manipulation, visualization, and modeling. The dplyr and tidyr packages are two of the most widely used packages within the tidyverse for data cleaning. dplyr provides a set of functions for efficiently manipulating large datasets, such as filtering, grouping, and summarizing data. tidyr is specifically designed for tidying (or restructuring) data, making it easier to work with. It provides functions for reshaping data, such as gathering and spreading columns, and allows for the creation of a consistent structure in the data. This makes it easier to perform data analysis and visualization. Together, these packages provide powerful tools for cleaning and manipulating data in R, making it a popular choice among data scientists. In this chapter, we will look at tools and techniques for preparing data in the tidyverse set of packages. You will learn how to deal with different formats and quickly interconvert them, merge different datasets, and summarize them. You will also learn how to bring data from outside sources not in handy files into your work.

In this chapter, we will cover the following recipes:

  • Loading data from files with readr
  • Tidying a wide format table into a tidy table with tidyr
  • Tidying a long format table into a tidy table with tidyr
  • Combining tables using join functions
  • Reformatting and extracting existing data into new columns using stringr
  • Computing new data columns from existing ones and applying arbitrary functions using mutate()
  • Using dplyr to summarize data in large tables
  • Using datapasta to create R objects from cut-and-paste data

Technical requirements

We will use renv to manage packages in a project-specific way. To use renv to install packages, you will first need to install the renv package. You can do this by running the following commands in your R console:

  1. Install renv:
    install.packages("renv")
  2. Create a new renv environment:
    renv::init()

This will create a new directory called .renv in your current project directory.

  1. You can then install packages with the following command:
    renv::install_packages()
  2. You can also use the renv package manager to install Bioconductor packages by running the following command:
    renv::install("bioc::package name")
  3. For example, to install the Biobase package, you would run the following command:
    renv::install("bioc::Biobase")
  4. You can use renv to install development packages from GitHub like this:
    renv::install("user name/repo name")
  5. For example, to install the danmaclean user rbioinfcookbook package, you would run the following command:
    renv::install("danmaclean/rbioinfcookbook")

You can also install multiple packages at once by separating the package names with a comma. renv will automatically handle installing any required dependencies for the packages you install.

Under renv, all packages will be installed into the local project directory and not the main central library, meaning you can have multiple versions of the same package—a specific one for each project.

All the sample data you need for this package is in the specially created danmaclean/rbioinfcookbook data package on GitHub. The data will become available in your project after installing that.

For this chapter, we’ll need the following packages:

  • Regular packages:
    • dplyr
    • fs
    • readr
    • tidyr
    • stringr
    • purrr
  • GitHub:
    • danmaclean/rbioinfcookbook
  • Custom install:
    • datapasta

In addition to these packages, we will also need some R tools such as conda; all these will be described when needed.

Further information

The packages that require a custom install procedure will be described in the relevant recipes.

In R, it is normal practice to load a library and use functions directly by name. Although this is great in short interactive sessions, it can cause confusion when many packages are loaded at once and share function names. To clarify which package and function I am using at a given moment, I will occasionally use the packageName::functionName() convention.

Sometimes, in the middle of a recipe I’ll interrupt the code to dive into some intermediate output or to look at the structure of an object. When that happens, you’ll see a code block where each line begins with ## (double hash symbols). Consider the following command:

letters[1:5]

This will give us the following output:

## a b c d e

Note that the output lines are prefixed with ##.

Loading data from files with readr

The readr R package is a package that provides functions for reading and writing tabular data in a variety of formats, including comma-separated values (CSV), tab-separated values (TSV), and delimiter-separated files. It is designed to be flexible and stop helpfully when data changes or unexpected items appear in the input. The two main advantages over base R functions include consistency in interface and output and the ability to be explicit about types and inspect those types.

This latter advantage can help to avoid errors when reading data, as well as make data cleaning and manipulation easier. readr functions can also automatically infer the data types of each column, which can be useful for a preliminary inspection of large datasets or when the data types are not known.

Consistency in interface and output is one of the main advantages of readr functions. readr functions provide a consistent interface for reading different types of data, which can make it easier to work with multiple types of files. For example, the read_csv() function can be used to read CSV files, while the read_tsv() function can be used to read TSV files. Additionally, readr functions return a tibble, a modern version of a data frame that is more consistent in its output and easier to read than the base R data frame.

Getting ready

For this recipe, we’ll need the readr and rbioinfcookbook packages. The latter contains a census_2021.csv file that carries UK census data from 2021, from the UK Office for National Statistics (https://www.ons.gov.uk/). You will need to inspect it, especially its header, to understand the process in this recipe. The first step shows you how to find where the file is on your filesystem.

Note the delimiters in the file are commas (,) and that the first seven lines contain metadata that isn’t part of the main data. Also, look at the messy column headings and note that the numbers themselves are internally delimited by commas.

How to do it…

We begin by getting a filename for the sample in the package:

  1. Load the package and get a filename:
    library(readr)filename <- fs::path_package("extdata",                              "census_2021.csv",                              package="rbioinfcookbook"                             )
  2. Specify a vector of new names for columns:
    col_names = c(            c("area_code", "country", "region",               "area_name", "all_persons", "under_4"),            paste0(seq(5, 85, by = 5),"_to_",seq(9, 89, by =5)),c("over_90"))
  3. Set the column types based on new names and contents:
    col_types = cols(  area_code = col_character(),  country = col_factor(levels = c("England", "Wales")),  region = col_factor(levels = c("North East",                                 "Yorkshire Humber",                                 "East Midlands",                                 "West Midlands",                                 "East of England",                                          "London",                                 "South East",                                 "South West",                                 "Wales"), ordered = TRUE                      ),  area_name = col_character(),  .default = col_number())
  4. Put it together and read the file:
    df <- read_csv(filename,               skip = 8,               col_names = col_names,               col_types = col_types )

And with that, we’ve loaded in a file with careful checking of the data types.

How it works…

Step 1 loads the library(readr) package. This package contains functions for reading and writing tabular data in a variety of formats, including CSV. The fs::path_package("extdata", "census_2021.csv", package="rbioinfcookbook") function is used to create a file path to the census_2021.csv file. It simply finds the place where the rbioinfcookbook package was installed and looks inside the extdata directory for the file, then it returns the full file path that leads to the file. Quite often, we would see the system.file() function used for this purpose. system.file() is a fine choice when everything works, but when it can’t find the file, it returns a blank string, which can be hard to debug. fs::path_package() is nicer to work with and will return an error when it can’t find the file.

In step 2, a vector of new column names is specified by the code. The vector contains several strings for the first few column names, and then a sequence of strings is created and a long list of age-related columns is created by concatenating two sequences of numbers. The resulting vector is stored in the col_names variable.

In step 3, we specify the R type we want each column to be. The categorical columns are set explicitly to factors, with the region being ordered explicitly in a rough geographical northeast to southwest way. The area_name column contains over 300 names, so we won’t make them an explicit factor and stick with it as a general text containing character type. The rest of the columns contain numeric data, so we make that the default with .default.

Finally, the read_csv() function is used to read the file specified in step 1 and create a data frame. The skip argument is used to skip the first eight rows, which include the metadata in the file and the messy header, the col_names argument is used to specify the new column names stored in col_names, and the col_types argument is used to specify the column types stored in col_types.

There’s more…

We used the read_csv() function for comma-separated data, but many more functions are available for different delimiters:

Function

Delimiter

read_csv()

CSV

read_tsv()

TSV

read_delim()

User-specified delimited files

read_fwf()

Fixed-width files

read_table()

Whitespace-separated files

read_log()

Web log files

Table 2.1 – Parser functions and the type of input file delimiter they work on in readr

For different local conventions on—for example—decimal separators and grouping marks, you can use the locale functions.

See also

The data.table package has a similar aim to readr and is especially good for very large data frames where compute speed is important.

Tidying a wide format table into a tidy table with tidyr

The tidyr package in R is a package that provides tools for tidying and reshaping data. It is designed to make it easy to work with data in a consistent and structured format, which is known as a tidy format. Tidy data is a standard way of organizing data that makes it easy to perform data analysis and visualization.

The main principles of tidy data are as follows:

  • Each variable forms a column
  • Each observation forms a row
  • Each type of observational unit forms a table

Data in a tidy format is easier to work with because the structure of the data is consistent, facilitating operations such as filtering, grouping, and reshaping the data. Tidy data is also more compatible with various data visualization and analysis tools, such as ggplot2, dplyr, and other tidyverse packages.

Our aim in this recipe will be to take a wide format data frame where a lot of information is hiding in column names and squeeze and reformat them into a data column of their own and rationalize them in the process.

Getting ready

We’ll need the rbioinfcookbook and tidyr packages. We’ll use the finished output from recipe 1, which is saved in the package.

How to do it…

We have to use just one function, but the options are many.

Specify the transformation to the table:

library(rbioinfcookbook)library(dplyr)
library(tidyr)
long_df <- census_df |> 
  rename("0_to_4" = "under_4", "90_to_120" = "over_90") |> 
  pivot_longer(
    cols = contains("_to_"),
    names_to = c("age_from", "age_to"),
    names_pattern = "(.*)_to_(.*)",
    names_transform = list("age_from" = as.integer,
                         age.to = as.integer),
    values_to = "count"
  )

And that’s it. This short recipe is very dense, though.

How it works…

The tidyr package has functions that work by allowing the user to specify a particular transformation that will be applied to the data frame to generate a new one. In this single step, we specify a table row-count increasing operation that will find all the columns that contain age information. Next, we split the title of that column into data for two new columns—one for the lower boundary of the age category and one for the upper boundary of the age category. Then, we change the type of those new columns to integer and, lastly, put the actual counts in a new column.

The first function in this pipeline is code from dplyr, which helps us rename column headings. Our age data column names are largely consistent, except for the lower bound and the upper one, so we rename those columns to match the pattern of the others, simplifying the transform specification.

The pivot_longer() function specifies the transform in the arguments, with the cols argument we choose to operate on any columns containing the text to. The names_pattern argument takes a regular expression (regex) that captures the bits of text before and after the to string in the column names and uses them as values for the columns defined in the names_to argument. The actual counts from the cell are put into a new column called counts. The transformation is then applied in one step and reduces the data frames column count to eight, increasing the row count to 6,935, and in the process making the data tidy and easier to use in downstream packages.

See also

The recipe uses a regex to describe a pattern in text. If you haven’t seen these before and need a primer, try typing ?"regular expression" to view the R help on the topic.

Tidying a long format table into a tidy table with tidyr

In this recipe, we look at the complementary operation to that of the Tidying a wide format table into a tidy table with tidyr recipe. We’ll take a long table and split one of its columns out to make multiple new columns. Initially, this might seem like we’re now violating our tidy data frame requirement, but we do occasionally come across data frames that have more than one variable squeezed into a single column. As in the previous recipe, tidyr has a specification-based function to allow us to correct our data frame.

Getting ready

We’ll use the tidyr package and the treatment data frame in the rbioinfcookbook package. This data frame has four columns, one of which—measurement—has got two variable names in it that need splitting into columns of their own.

How to do it…

In stark contrast to the Tidying a wide format table into a tidy table with tidyr recipe, this expression is extremely terse; we can tidy the wide table very easily:

library(rbioinfcookbook)library(tidyr)
treatments |> 
  pivot_wider(
    names_from = measurement,
    values_from = value
  )

This is so simple because all the data we need is already in the data frame.

How it works…

In this very simple-looking recipe, the specification is gloriously clear: simply take the measurement column and create new column names from its values, moving the value appropriately. The names_from argument specifies the column to split, and values_from specifies where its values come from.

There’s more…

It is quite possible to incorporate values from more than one column at a time; just pass a vector of columns to the names_from argument, and you can format the computed column names in the output with names_glue.

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Key benefits

  • Apply modern R packages to process biological data using real-world examples
  • Represent biological data with advanced visualizations and workflows suitable for research and publications
  • Solve real-world bioinformatics problems such as transcriptomics, genomics, and phylogenetics
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

The updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You’ll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools. This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses. By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.

Who is this book for?

This book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning via a recipe-based approach. Working knowledge of the R programming language and basic knowledge of bioinformatics are prerequisites.

What you will learn

  • Set up a working environment for bioinformatics analysis with R
  • Import, clean, and organize bioinformatics data using tidyr
  • Create publication-quality plots, reports, and presentations using ggplot2 and Quarto
  • Analyze RNA-seq, ChIP-seq, genomics, and next-generation genetics with Bioconductor
  • Search for genes and proteins by performing phylogenetics and gene annotation
  • Apply ML techniques to bioinformatics data using mlr3
  • Streamline programmatic work using iterators and functional tools in the base R and purrr packages
  • Use ChatGPT to create, annotate, and debug code and workflows

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Table of Contents

15 Chapters
Chapter 1: Setting Up Your R Bioinformatics Working Environment Chevron down icon Chevron up icon
Chapter 2: Loading, Tidying, and Cleaning Data in the tidyverse Chevron down icon Chevron up icon
Chapter 3: ggplot2 and Extensions for Publication Quality Plots Chevron down icon Chevron up icon
Chapter 4: Using Quarto to Make Data-Rich Reports, Presentations, and Websites Chevron down icon Chevron up icon
Chapter 5: Easily Performing Statistical Tests Using Linear Models Chevron down icon Chevron up icon
Chapter 6: Performing Quantitative RNA-seq Chevron down icon Chevron up icon
Chapter 7: Finding Genetic Variants with HTS Data Chevron down icon Chevron up icon
Chapter 8: Searching Gene and Protein Sequences for Domains and Motifs Chevron down icon Chevron up icon
Chapter 9: Phylogenetic Analysis and Visualization Chevron down icon Chevron up icon
Chapter 10: Analyzing Gene Annotations Chevron down icon Chevron up icon
Chapter 11: Machine Learning with mlr3 Chevron down icon Chevron up icon
Chapter 12: Functional Programming with purrr and base R Chevron down icon Chevron up icon
Chapter 13: Turbo-Charging Development in R with ChatGPT Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
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Rudrendu Kumar Paul Nov 04, 2023
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The R Bioinformatics Cookbook by Dan MacLean is a practical, comprehensive guide aimed at bioinformaticians looking to expand their data analysis skills using R and Bioconductors. With the author's extensive background in genomics, molecular biology, and bioinformatics, readers can trust that the book provides authoritative content.A major strength of the cookbook format is it supplies readers with recipe-based solutions to tackle common bioinformatics challenges. Rather than just theory, professionals get hands-on guidance to put concepts into practice. The book spans a versatile range of topics - from the initial setup of the R environment to sophisticated methods like machine learning. This ensures readers can find both introductory building blocks as well as more advanced techniques to round out their skills.Notably, the book thoroughly covers specialized packages and frameworks within the R ecosystem that are tailored for bioinformatics. Readers are immersed in the diverse capabilities of Bioconductor along with packages like ggplot2 for publication-quality graphics and mlr3 for machine learning. The focus on R capabilities designed for biological data analysis is a distinguishing factor.The content covers crucial bioinformatics techniques such as RNA-seq analysis, variant calling from DNA sequencing data, statistical modeling, and phylogenetic approaches. There is also a significant emphasis on data visualization and interpretation - essential skills for research. Interactive elements utilizing Shiny and Quarto demonstrate new avenues for impactful data presentation.While the book is geared towards those with some existing R familiarity, beginners may need help due to the assumed knowledge and density of information spanning many techniques. However, intermediate to advanced R users in bioinformatics will find an invaluable guide for expanding their toolkit in a hands-on manner. They can level up skills in both fundamental and cutting-edge data analysis areas.Additional topics could potentially enhance the book even further. As biological datasets explode in size, guidance on leveraging cloud computing could prove useful. More coverage of sophisticated machine learning methods like deep learning would keep readers abreast of the latest techniques for modeling complex data. Single-cell sequencing and metagenomics represent rapidly growing areas where extra content would make this guide more comprehensive for the field.The R Bioinformatics Cookbook is a practical, authoritative resource for bioinformaticians looking to hone their R skills for impactful data analysis. The hands-on solutions, specialized coverage of R packages, and techniques like statistical modeling and visualization make it a robust guide for intermediate to advanced practitioners seeking to expand their toolkits. While beginners may need more foundational content, the book's recipe-based approach helps professionals readily apply concepts to real-world biological studies.
Amazon Verified review Amazon
Amazon Customer Nov 04, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
AI practitioners looking to upgrade their data analysis skills with R, the R Bioinformatics Cookbook by Dan MacLean offers an authoritative, practical guide. True to its name, the cookbook format provides proven recipes to solve common challenges faced in biological research. Rather than just theory, professionals get hands-on solutions readily applicable to real-world work.The book strikes an effective balance between breadth and depth across two dimensions. First, it spans a diverse range of topics - from R environment setup to advanced machine learning. Second, it delves deeply into specialized R packages tailored for bioinformatics within the expansive Bioconductor ecosystem. Readers tour the diverse landscape of tools designed specifically for genomics and computational biology.Notably, the book moves beyond just core techniques to incorporate cutting-edge methods poised to shape the future. The coverage of sophisticated machine learning using mlr3 and interactive dashboards with Shiny/Quarto keeps readers ahead of the curve. While leveraging time-tested tools like ggplot2 for publication-quality graphics, the book also showcases next-gen avenues for analysis and communication.The content is firmly rooted in the practical needs of bioinformaticians. Crucial workflows like sequencing data analysis, statistical modeling, and phylogenetics all receive thorough treatment. Each chapter focuses on a single technique, ensuring digestible depth. And code is structured to promote understanding and extensibility rather than just solutions.For readers with some existing R familiarity, the book serves as an invaluable springboard to enhanced productivity. It provides building blocks to level up both fundamental and advanced skills. However, complete beginners may need supplementary learning first to establish core competencies before benefiting fully. The assumed knowledge makes this more suited for intermediate learners onward.To make this comprehensive guide even more holistic, additional topics on large-scale cloud computing, deep learning for omics data, and single-cell analysis could prove valuable amendments. As data complexity escalates, guidance on these emerging fronts would increase the book's utility for tackling real-world biological complexities.The practitioners seeking to expand their R toolkit in order to derive greater insights from modern molecular datasets, the R Bioinformatics Cookbook hits the sweet spot between theory and practice. Its competent stewardship of both foundational and innovative R-based techniques makes it a recipe for success in bioinformatics.
Amazon Verified review Amazon
Om S Nov 09, 2023
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
In the second edition of the "R Bioinformatics Cookbook," readers are taken on a hands-on journey through over 80 recipes, providing a practical guide to conducting research and analysis in computational biology using the R ecosystem. The book emphasizes real-world applications, offering a treasure trove of modern R packages for processing biological data with tangible examples. From setting up a functional R working environment to analyzing RNA-seq, ChIP-seq, genomics, and more, each chapter introduces essential tools and techniques for bioinformatics enthusiasts.The book's strength lies in its recipe-based approach, allowing bioinformaticians, data analysts, researchers, and R developers to tackle intermediate-to-advanced biological problems effectively. Notably, the inclusion of Bioconductor, ggplot2, and Quarto tools enhances the reader's ability to represent biological data through advanced visualizations suitable for research and publication. The concluding chapters delve into machine learning with mlr3 and harnessing the power of ChatGPT for code generation and workflow understanding. With a clear structure and a focus on practical applications, this cookbook equips readers to become proficient bioinformatics specialists, navigating the complexities of large and intricate biological datasets.
Amazon Verified review Amazon
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  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.