Modern Data Visualization with R

ISBN-13: 9781032289496 / Twarda / 2024 / 256 str.

Robert Kabacoff

Opis

Ways that raw and summary data can be turned into visualizations that convey meaningful insights: basic graphs, bar charts, scatter plots, and line charts, and progresses to tree maps, alluvial plots, radar charts, mosaic plots, grouped dot plots, effects plots, multivariate presentations such as corrgrams, biplots, network diagrams.

Spis treści

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.

O autorze

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 (3nd 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 Ph.D. in Clinical Psychology from the University of Missouri-St. Louis. Following a postdoctoral fellowship in Family Research at Brown University, he joined the faculty in 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 the Quantitative Analysis Center at Wesleyan University, teaching courses in exploratory data analysis, machine learning, and statistical software development.