R for Data Science

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 tidyverseâ??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

Table of Contents

Part I. Whole Game

Chapter 1. Data Visualization

Chapter 2. Workflow: Basics

Chapter 3. Data Transformation

Chapter 4. Workflow: Code Style

Chapter 5. Data Tidying

Chapter 6. Workflow: Scripts and Projects

Chapter 7. Data Import

Chapter 8. Workflow: Getting Help

Part II. Visualize

Chapter 9. Layers

Chapter 10. Exploratory Data Analysis

Chapter 11. Communication

Part III. Transform

Chapter 12. Logical Vectors

Chapter 13. Numbers

Chapter 14. Strings

Chapter 15. Regular Expressions

Chapter 16. Factors

Chapter 17. Dates and Times

Chapter 18. Missing Values

Chapter 19. Joins

Part IV. Import

Chapter 20. Spreadsheets

Chapter 21. Databases

Chapter 22. Arrow

Chapter 23. Hierarchical Data

Chapter 24. Web Scraping

Part V. Program

Chapter 25. Functions

Chapter 26. Iteration

Chapter 27. A Field Guide to Base R

Part VI. Communicate

Chapter 28. Quarto

Chapter 29. Quarto Formats

About the Authors

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 University as 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.