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.

part I|180 pages

Introduction to Data Science

chapter 21|6 pages

Prologue: Why data science?

chapter 2|26 pages

Data visualization

chapter 3|32 pages

A grammar for graphics

chapter 4|22 pages

Data wrangling on one table

chapter 5|14 pages

Data wrangling on multiple tables

chapter 6|36 pages

Tidy data

chapter 7|20 pages


chapter 8|22 pages

Data science ethics

part II|118 pages

Part II: Statistics and Modeling

chapter 1829|24 pages

Statistical foundations

chapter 10|22 pages

Predictive modeling

chapter 11|34 pages

Supervised learning

chapter 12|18 pages

Unsupervised learning

chapter 13|18 pages


part III|192 pages

Part III: Topics in Data Science

chapter 30014|24 pages

Dynamic and customized data graphics

chapter 15|38 pages

Database querying using SQL

chapter 16|14 pages

Database administration

chapter 17|30 pages

Working with geospatial data

chapter 18|18 pages

Geospatial computations

chapter 19|26 pages

Text as data

chapter 20|26 pages

Network science

chapter 21|14 pages

Epilogue: Towards “big data”