R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

by Hadley Wickham, Mine etinkaya-Rundel, Garrett Grolemund

Overview

Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.

You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way.

You'll understand how to:

• Visualize: Create plots for data exploration and communication of results
• Transform: Discover variable types and the tools to work with them
• Import: Get data into R and in a form convenient for analysis
• Program: Learn R tools for solving data problems with greater clarity and ease
• Communicate: Integrate prose, code, and results with Quarto

Product Details

ISBN-13:	9781492097402
Publisher:	O'Reilly Media, Incorporated
Publication date:	07/18/2023
Edition description:	2nd ed.
Pages:	576
Product dimensions:	7.00(w) x 9.19(h) x 1.17(d)

About the Author

Hadley Wickham is Chief Scientist at RStudio and a member of the R Foundation. He builds tools (both computational and cognitive) that make data science easier, faster, and more fun. His work includes packages for data science (ggplot2, dplyr, tidyr), data ingest (readr, readxl, haven), and principled software development (roxygen2, testthat, devtools). He is also a writer, educator, and frequent speaker promoting the use of R for data science. Learn more on his homepage, http://hadley.nz.

Mine Çetinkaya-Rundel is Professor of the Practice and the Director of Undergraduate Studies at the Department of Statistical Science and an affiliated faculty in the Computational Media, Arts, and Cultures program at Duke Universityas well as Educator at RStudio. Mine works on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education. At RStudio, Mine's work focuses primarily on education for open-source R packages as well as building resources and tools for educators teaching statistics and data science with R and RStudio. Mine has authored four undergraduate statistics textbooks as part of the OpenIntro projects, teaches the popular MOOC Statistics with R on Coursera and is the developer and maintainer of Data Science in a Box. Mine is a Fellow of the ASA and an Elected Member of the ISI as well as the recipient of the 2021 Robert V. Hogg Award For Excellence in Teaching Introductory Statistics, the 2018 Harvard Pickard Award, and the 2016 ASA Waller Education Award.

Garrett Grolemund is the author of Hands-On Programming with R and co-author of R for Data Science and R Markdown: The Definitive Guide. He is Director of Learning at RStudio and holds a Ph.D. in Statistics, but specializes in teaching. He’s taught people how to use R at over 50 government agencies, small businesses, and multi-billion dollar global companies; and he’s designed RStudio’s training materials for R, Shiny, R Markdown and more. Garrett wrote the popular lubridate package for dates and times in R and creates the RStudio cheat sheets.

Table of Contents

Preface ix

Part I Explore

1 Data Visualization with ggplot2 3

Introduction 3

First Steps 4

Aesthetic Mappings 7

Common Problems 13

Facets 14

Geometric Objects 16

Statistical Transformations 22

Position Adjustments 27

Coordinate Systems 31

The Layered Grammar of Graphics 34

2 Workflow: Basics 37

Coding Basics 37

What's in a Name? 38

Calling Functions 39

3 Data Transformation with dplyr 43

Introduction 43

Filter Rows with filter() 45

Arrange Rows with arrange() 50

Select Columns with select() 51

Add New Variables with mutate() 54

Grouped Summaries with summarize() 59

Grouped Mutates (and Filters) 73

4 Workflow: Scripts 77

Running Code 78

RStudio Diagnostics 79

5 Exploratory Data Analysis 81

Introduction 81

Questions 82

Variation 83

Missing Values 91

Covariation 93

Patterns and Models 105

Ggplot2 Calls 108

Learning More 108

6 Workflow: Projects 111

What Is Real? 111

Where Does Your Analysis Live? 113

Paths and Directories 113

RStudio Projects 114

Summary 116

Part II Wrangle

7 Tibbles with tibble 119

Introduction 119

Creating Tibbles 119

Tibbles Versus data.frame 121

Interacting with Older Code 123

8 Data Import with readr 125

Introduction 125

Getting Started 125

Parsing a Vector 129

Parsing a File 137

Writing to a File 143

Other Types of Data 145

9 Tidy Data with tidyr 147

Introduction 147

Tidy Data 148

Spreading and Gathering 151

Separating and Pull 157

Missing Values 161

Case Study 163

Nontidy Data 168

10 Relational Data with dplyr 171

Introduction 171

Nycflights13 172

Keys 175

Mutating Joins 178

Filtering Joins 188

Join Problems 191

Set Operations 192

11 Strings with stringr 195

Introduction 195

String Basics 195

Matching Patterns with Regular Expressions 200

Tools 207

Other Types of Pattern 218

Other Uses of Regular Expressions 221

Stringi 222

12 Factors with forcats 223

Introduction 223

Creating Factors 224

General Social Survey 225

Modifying Factor Order 227

Modifying Factor Levels 232

13 Dates and Times with lubridate 237

Introduction 237

Creating Date/Times 238

Date-Time Components 243

Time Spans 249

Time Zones 254

Part III Program

14 Pipes with magrittr 261

Introduction 261

Piping Alternatives 261

When Not to Use the Pipe 266

Other Tools from magrittr 267

15 Functions 269

Introduction 269

When Should You Write a Function? 270

Functions Are for Humans and Computers 273

Conditional Execution 276

Function Arguments 280

Return Values 285

Environment 288

16 Vectors 291

Introduction 291

Vector Basics 292

Important Types of Atomic Vector 293

Using Atomic Vectors 296

Recursive Vectors (Lists) 302

Attributes 307

Augmented Vectors 309

17 Iteration with purrr 313

Introduction 313

For Loops 314

For Loop Variations 317

For Loops Versus Functionals 322

The Map Functions 325

Dealing with Failure 329

Mapping over Multiple Arguments 332

Walk 335

Other Patterns of For Loops 336

Part IV Model

18 Model Basics with modelr 345

Introduction 345

A Simple Model 346

Visualizing Models 354

Formulas and Model Families 358

Missing Values 371

Other Model Families 372

19 Model Building 375

Introduction 375

Why Are Low-Quality Diamonds More Expensive? 376

What Affects the Number of Daily Flights? 384

Learning More About Models 396

20 Many Models with purrr and broom 397

Introduction 397

Gapminder 398

List-Columns 409

Creating List-Columns 411

Simplifying List-Columns 416

Making Tidy Data with broom 419

Part V Communicate

21 R Markdown 423

Introduction 423

R Markdown Basics 424

Text Formatting with Markdown 427

Code Chunks 428

Troubleshooting 435

YAML Header 435

Learning More 438

22 Graphics for Communication with ggplot2 441

Introduction 441

Label 442

Annotations 445

Scales 451

Zooming 461

Themes 462

Saving Your Plots 464

Learning More 467

23 R Markdown Formats 469

Introduction 469

Output Options 470

Documents 470

Notebooks 471

Presentations 472

Dashboards 473

Interactivity 474

Websites 477

Other Formats 477

Learning More 478

24 R Markdown Workflow 479

Index 483