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El. knyga: Modern Data Visualization with R

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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.



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 mapping of geographic data. Both static and interactive graphics are described and the use of color, shape, shading, grouping, annotation, and animations are covered in detail. The book moves from a default look and feel for graphs, to graphs with customized colors, fonts, legends, annotations, and organizational themes.

Features:

• Contains a wide breadth of graph types including newer and less well-known approaches
• Connects each graph type to the characteristics of the data and the goals of the analysis
• Moves the reader from simple graphs, describing one variable, to building visualizations that describe complex relationships among many variables
• Newer approaches to creating interactive web graphics via JavaScript libraries
• Details how to customize each graph type to meet users’ needs and those of their audiences
• Gives methods for creating visualizations that are publication ready for print (in color or black and white) and the web
• Suggests best practices
• Offers examples from a wide variety of fields

It is written for those new to data analysis as well as the seasoned data scientist. It can be used for both teaching and research. It will particularly appeal to anyone who needs to describe data visually and wants to find and emulate the most appropriate method quickly. The reader should have some basic coding experience, but expertise in R is not required. Some of the later chapters (e.g., visualizing statistical models) assume exposure to statistical inference at the level of analysis of variance and regression.

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.

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.
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