1st Edition

Modern Data Visualization with R

By Robert Kabacoff Copyright 2024
271 Pages 189 Color Illustrations
by Chapman & Hall

271 Pages 189 Color Illustrations
by Chapman & Hall

271 Pages 189 Color Illustrations
by Chapman & Hall

Modern Data Visualization with R describes the many ways that raw and summary data can be turned into visualizations that convey meaningful insights. It starts with basic graphs such as bar charts, scatter plots, and line charts, but progresses to less well-known visualizations such as tree maps, alluvial plots, radar charts, mosaic plots, effects plots, correlation plots, biplots, and the... Read more

1. Introduction

2. Data Preparation

3. Introduction to ggplot2

4. Univariate Graphs 

5. Bivariate Graphs 

6. Multivariate Graphs 

7. Maps 

8. Time-dependent graphs 

9. Statistical Models 

10. Other Graphs 

11. Customizing Graphs 

12. Saving Graphs 

13. Interactive Graphs 

14. Advice / Best Practices 

Biography

Robert Kabacoff is a data scientist with more than 30 years of experience in multivariate statistical methods, data visualization, predictive analytics, and psychometrics. A widely recognized expert in statistical programming, he is the author of R in Action: Data Analysis and Graphics with R (3rd ed.), and the popular Quick-R (www.statmethods.net) website. Dr. Kabacoff is also the co-author of Evaluating Research Articles from Start to Finish (3rd ed.), a textbook that uses a case-study approach to help students learn to read and evaluate empirical research.

Dr. Kabacoff earned his BA in psychology from the University of Connecticut and his PhD in clinical psychology from the University of Missouri-St. Louis. Following a postdoctoral fellowship in family research at Brown University, he joined the faculty at the Center for Psychological Studies at Nova Southeastern University, achieving the position of full professor in 1997. For 19 years, Dr. Kabacoff held the position of Vice President of Research for a global organizational development firm, providing research and consultation to academic, government, corporate, and humanitarian institutions in North America, Western Europe, Africa, and Asia. He is currently a professor of the practice in quantitative analysis at the Hazel Quantitative Analysis Center at Wesleyan University, teaching courses in exploratory data analysis, machine learning, and statistical software development.

"This book is a quick way to learn ggplot2."
~John M. Hoenig, The American Statistician