Thomas Mailund

R 4 Data Science Quick Reference

A Pocket Guide to APIs, Libraries, and Packages

2nd ed.
Thomas Mailund
Aarhus, Denmark
ISBN 978-1-4842-8779-8e-ISBN 978-1-4842-8780-4
https://doi.org/10.1007/978-1-4842-8780-4
© Thomas Mailund 2019, 2022
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Apress imprint is published by the registered company APress Media, LLC, part of Springer Nature.

The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Table of Contents
Chapter 1:​ Introduction 1
Chapter 2: Importing Data: readr 5
Functions for Reading Data 5
File Headers 8
Column Types 13
String-Based Column Type Specification 13
Function-Based Column Type Specification 21
Parsing Time and Dates 24
Space-Separated Columns 30
Functions for Writing Data 31
Chapter 3: Representing Tables: tibble 33
Creating Tibbles 33
Indexing Tibbles 37
Chapter 4:​ Tidy Select 43
Ranges 44
Complements 44
Unions and Intersections 45
Select Columns Based on Name 46
Everything 47
Indexing from the Last Column 48
Selecting from Strings 49
Selecting Columns Based on Their Content 50
It Is a Growing Language, so Check for Changes 51
Chapter 5: Reformatting Tables: tidyr 53
Tidy Data 53
Pivoting 54
Complex Column Encodings 60
Expanding, Crossing, and Completing 67
Missing Values 70
Nesting Data 74
Chapter 6: Pipelines: magrittr 77
The Problem with Pipelines 77
Pipeline Notation 80
Pipelines and Function Arguments 82
Function Composition 85
Other Pipe Operations 86
Chapter 7: Functional Programming: purrr 89
General Features of purrr Functions 90
Filtering 90
Mapping 92
Reduce and Accumulate 102
Partial Evaluation and Function Composition 105
Lambda Expressions 107
Chapter 8: Manipulating Data Frames: dplyr 111
Selecting Columns 111
Filter 119
Sorting 126
Modifying Data Frames 127
Grouping and Summarizing 132
Joining Tables 139
Income in Fictional Countries 147
Chapter 9: Working with Strings: stringr 155
Counting String Patterns 155
Splitting Strings 158
Capitalizing Strings 160
Wrapping, Padding, and Trimming 160
Detecting Substrings 164
Extracting Substrings 166
Transforming Strings 167
Chapter 10: Working with Factors: forcats 173
Creating Factors 173
Concatenation 175
Projection 177
Adding Levels 180
Reorder Levels 182
Chapter 11: Working with Dates: lubridate 185
Time Points 185
Time Zones 187
Time Intervals 188
Chapter 12: Working with Models: broom and modelr 193
broom 193
modelr 196
Chapter 13: Plotting: ggplot2 207
The Basic Plotting Components in ggplot2 207
Adding Components to a Plot Object 209
Adding Data 210
Adding Aesthetics 211
Adding Geometries 211
Facets 218
Adding Coordinates 220
Modifying Scales 222
Chapter 14:​ Conclusions 225
Index 227
About the Author
Thomas Mailund

is an associate professor in bioinformatics at Aarhus University, Denmark. His background is in math and computer science, but for the last decade, his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.

 
About the Technical Reviewer
Matt Wiley

leads institutional effectiveness, research, and assessment at Victoria College, facilitating strategic and unit planning, data-informed decision making, and state/regional/federal accountability. As a tenured, associate professor of mathematics, he won awards in both mathematics education (California) and student engagement (Texas). Matt earned degrees in computer science, business, and pure mathematics from the University of California and Texas A&M systems.

Outside academia, he coauthors books about the popular R programming language and was managing partner of a statistical consultancy for almost a decade. He has programming experience with R, SQL, C++, Ruby, Fortran, and JavaScript.

A programmer, a published author, a mathematician, and a transformational leader, Matt has always melded his passion for writing with his joy of logical problem solving and data science. From the boardroom to the classroom, he enjoys finding dynamic ways to partner with interdisciplinary and diverse teams to make complex ideas and projects understandable and solvable.

 
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