Modern Data Science with R

From a review of the first edition: ＂Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics＂ (The American Statistician).

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.

The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.

Benjamin S. Baumer is an assistant professor in the Statistical & Data Sciences program at Smith College. Ben is a co-author of The Sabermetric Revolution and won the 2016 Contemporary Baseball Analysis Award from the Society for American Baseball Research.

Daniel T. Kaplan is the DeWitt Wallace professor of mathematics and computer science at Macalester College. He is the author of several textbooks on statistical modeling and statistical computing, and received the 2006 Macalester Excellence in Teaching award.

Nicholas J. Horton is a professor of statistics at Amherst College. He is a Fellow of the American Statistical Association (ASA), member of the NRC Committee on Applied and Theoretical Statistics, recipient of a number of national teaching awards, author of a series of books on statistical computing.