Short description:

This book is focused on one of the two major topics of doing data analysis: data visualization, aka, computer graphics. In one place the major R systems for visualization are discussed, organized by topic and not by system. Anyone doing data analysis will be shown how to use R to generate basic visualizations with any of R visualization systems.

Long description:

R Visualizations: Derive Meaning from Data focuses on one of the two major topics of data analytics: data visualization, a.k.a., computer graphics. In the book, major R systems for visualization are discussed, organized by topic and not by system. Anyone doing data analysis will be shown how to use R to generate any of the basic visualizations with the R visualization systems. Further, this book introduces the author?s lessR system, which always can accomplish a visualization with less coding than the use of other systems, sometimes dramatically so, and also provides accompanying statistical analyses.

Key Features

• Presents thorough coverage of the leading R visualization system, ggplot2.
• Gives specific guidance on using base R graphics to attain visualizations of the same quality as those provided by ggplot2.

• Shows how to create a wide range of data visualizations: distributions of categorical and continuous variables, many types of scatterplots including with a third variable, time series, and maps.

• Inclusion of the various approaches to R graphics organized by topic instead of by system.
• Presents the recent work on interactive visualization in R.

David W. Gerbing received his PhD from Michigan State University in 1979 in quantitative analysis, and currently is a professor of quantitative analysis in the School of Business at Portland State University. He has published extensively in the social and behavioral sciences with a focus on quantitative methods. His lessR package has been in development since 2009.

'Finally, this is an easy- to-read book to begin the data visualization by using R for those who want to start or to develop their background in R for data visualization. It is a good companion to facilitate the use of R for data visualization with less coding than the better known ggplot2 package.'

- Sébastien Bailly, International Society for Clinical Biostatistics, 71, 2021

Table of Contents:

1. R, Data and Visualizations. 2. R Visualization Quick Start. 3. Customization. 4. Visualize the Distribution of a Categorical Variable. 5. Visualize the Distribution of a Continuous Variable. 6. Visualize the Distributions of Values over Time. 7. Visualize Spatial Data with Maps. 8. Visualize Three Dimensions. 9 Visualize Dimensionality Reduction. 10. Interactive Visualizations.