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.
Table of Contents
Background and motivation
Key features of this book
Changes in the second edition
Key role of technology
How to use this book
I Part I: Introduction to Data Science
1. Prologue: Why data science?
What is data science?
Case study: The evolution of sabermetrics
2. Data visualization
The federal election cycle
Composing data graphics
Importance of data graphics: Challenger
Creating effective presentations
The wider world of data visualization
3. A grammar for graphics
A grammar for data graphics
Canonical data graphics in R
Extended example: Historical baby names
4. Data wrangling on one table
A grammar for data wrangling
Extended example: Ben’s time with the Mets
5. Data wrangling on multiple tables
Extended example: Manny Ramirez
6. Tidy data
Using across() with dplyr functions
The map() family of functions
Iterating over a one-dimensional vector
Iteration over subgroups
Extended example: Factors associated with BMI
8. Data Science Ethics
Role of data science in society
Some settings for professional ethics
Some principles to guide ethical action
Data and disclosure
Professional guidelines for ethical conduct
II Part II: Statistics and Modeling
9. Statistical foundations
Samples and populations
Statistical models: Explaining variation
Confounding and accounting for other factors
The perils of p-values
10. Predictive modeling
Simple classification models
Extended example: Who has diabetes?
11. Supervised learning
Example: Evaluation of income models redux
Extended example: Who has diabetes this time?
12. Unsupervised learning
Reasoning in reverse
Extended example: Grouping cancers
Key principles of simulation
III Part III: Topics in Data Science
14. Dynamic and customized data graphics
Rich Web content using Djs and htmlwidgets
Interactive Web apps with Shiny
Customization of library(ggplot)ggplot graphics
Extended example: Hot dog eating
15. Database querying using SQL
From dplyr to SQL
The SQL universe
The SQL data manipulation language
Extended example: FiveThirtyEight flights
SQL vs R
16. Database administration
Constructing efficient SQL databases
Changing SQL data
Extended example: Building a database
17. Working with geospatial data
Motivation: What’s so great about geospatial data?
Spatial data structures
Extended example: Congressional districts
Effective maps: How (not) to lie
Playing well with others
18. Geospatial computations
Extended example: Trail elevations at MacLeish
19. Text as data
Regular expressions using Macbeth
Extended example: Analyzing textual data from arXivorg
20. Network science
Introduction to network science
Extended example: Six degrees of Kristen Stewart
Extended example: men’s college basketball
21. Epilogue: Towards "big data"
Notions of big data
Tools for bigger data
Alternatives to R
IV Part IV: Appendices
A Packages used in this book
The mdsr package
B Introduction to R and RStudio
Fundamental structures and objects
C Algorithmic thinking
Extended example: Law of large numbers
Debugging and defensive coding
D Reproducible analysis and workflow
Scriptable statistical computing
Reproducible analysis with R Markdown
Projects and version control
E Regression modeling
Inference for regression
Assumptions underlying regression
F Setting up a database server
Connecting to SQL
Benjamin S. Baumer is an associate professor in the Statistical & Data Sciences program at Smith College. He has been a practicing data scientist since 2004, when he became the first full-time statistical analyst for the New York Mets. Ben is a co-author of The Sabermetric Revolution and Analyzing Baseball Data with R. He received the 2019 Waller Education Award and the 2016 Significant Contributor Award from the Society for American Baseball Research.
Daniel T. Kaplan is the DeWitt Wallace emeritus professor of mathematics and computer science at Macalester College. He is the author of several textbooks on statistical modeling and statistical computing. Danny received the 2006 Macalester Excellence in Teaching award and the 2017 CAUSE Lifetime Achievement Award.
Nicholas J. Horton is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. He is a Fellow of the ASA and the AAAS, co-chair of the National Academies Committee on Applied and Theoretical Statistics, recipient of a number of national teaching awards, author of a series of books on statistical computing, and actively involved in data science curriculum efforts to help students "think with data".
"[...] To answer a wide range of modern research questions, this book by Baumer, Kaplan, and Horton features an excellent introduction to data wrangling, visualization, statistical modeling, machine learning, and other advanced statistical applications through the RStudio environment following the tidyverse syntax. [...] Overall, Modern Data Science with R, 2nd edition serves as an excellent introductory resource to help develop techniques to extract, transform, visualize, and learn from datasets through the R environment. It focuses on implementing those techniques in R and does not provide a theoretical background for the discussed methods. The book will be a perfect reference for a broad audience ranging from undergraduates in data science courses to advanced graduate students and professionals from a variety of research fields."
-Kohma Arai and Vyacheslav Lyubchich, in Technometrics, July 2022
"Overall, I enjoyed reading this book. The authors were very good at creating a complete tool for studying data science. Therefore, I recommend this book, for its content, writing, and organization, to graduate students in data science and statistics. I also recommend the book to professionals who should prepare themselves for the challenges they are going to face in the future with the voluminous and heterogenous amount of data that should be timely analyzed to extract meaningful information to guide action."
-Georgios Nikolopoulos, in ISCB News, June 2022
"The authors have successfully completed the job of choosing the content with relevant topics and, deciding the extent of knowledge to be delivered, and finally, putting them in an understandable sequence. This is a well-written book and does not cover much theory. .. The book’s second edition contents are updated, expanded, revised, split, rewritten and rearranged compared to the first edition. The key changes are the use of recently developed R packages, .... (and) updated exercises in the chapters ..."
-Shalabh,in Journal of the Royal Statistical Society Series A, August 2021
"[This book] provides an excellent basis for statisticians who want to dig deeper into, for example, data handling, for computer scientists who aim to strengthen their knowledge of statistical methods as well as for all other researchers who are interested in data science in general. ... Each section is structured as an interplay between R-code and explanatory text for understanding. The division into several stand-alone segments is an advantage, because the reader may easily choose the section she or he is interested in without missing relevant information. A key feature of the book is its focus on different example data sets that are available via R-packages or from URLs that are embedded in the text. These data sets are used to illustrate the methodology presented using R-code. Their availability allows the reader to reproduce the code while working with the book. ... It can be warmly recommended to practical researchers who seek a comprehensive overview of different topics in data science with focus on implementations in R."
-Annika Hoyer, in Biometrical Journal, August 2021
"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data."
-Hunter Glanz, Cal Poly San Luis Obispo
"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills."
-Lynn Collen, St. Cloud Stat University
"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course."
-Matthew Beckman, Penn State University
"The authors have covered almost all aspects of data science, a revolutionary field that marries elements of computational thinking and traditional statistical theory. The book can thus equip the readers with the necessary knowledge and skills to extract data from a variety of sources, restructure observations in a form that allows analysis, store data in efficient databases, and work effectively on massive and complex data sets in order to produce actionable information."
- Georgios Nikolopoulos, University of Cyprus, ISCB Book Reviews, June 2022.