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 Title: R for Data Science, 2nd Edition: Visualize, Model, Transform, Tidy, and Import Data
 Author(s) Hadley Wickham (Author), Mine ÇetinkayaRundel (Author), Garrett Grolemund (Author)
 Publisher: O'Reilly Media; 2nd edition (July 18, 2023); eBook (Creative Commons Licensed)
 License(s): CC BYNCND 3.0 US
 Hardcover/Paperback: 576 pages
 eBook: HTML
 Language: English
 ISBN10/ASIN: 1492097403
 ISBN13: 9781492097402
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Book Description
This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it.
What exactly is data science? With this book, you'll gain a clear understanding of this discipline for discovering natural laws in the structure of data. Along the way, you’ll learn how to use the versatile R programming language for data analysis.
Whenever you measure the same thing twice, you get two results—as long as you measure precisely enough. This phenomenon creates uncertainty and opportunity. Author Garrett Grolemund, Master Instructor at RStudio, shows you how data science can help you work with the uncertainty and capture the opportunities. You'll learn about:
 Data Wrangling  how to manipulate datasets to reveal new information
 Data Visualization  how to create graphs and other visualizations
 Exploratory Data Analysis  how to find evidence of relationships in your measurements
 Modelling  how to derive insights and predictions from your data
 Inference  how to avoid being fooled by data analyses that cannot provide foolproof results
Through the course of the book, you'll also learn about the statistical worldview, a way of seeing the world that permits understanding in the face of uncertainty, and simplicity in the face of complexity.
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.
 The R Programming Language
 Data Science
 Data Analysis and Data Mining, Big Data
 Statistics, Mathematical Statistics, and SAS Programming
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 R for Data Science, 2nd Edition: Visualize, Model, Transform, Tidy, and Import Data
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