R Visualizations: Deriving Meaning from Data, 1st Edition (Hardback) book cover

R Visualizations

Deriving Meaning from Data, 1st Edition

By David Gerbing

Chapman and Hall/CRC

237 pages | 100 B/W Illus.

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Hardback: 9781138599635
pub: 2020-05-11
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Description

This book focuses on one of the two major topics of data analytics: data visualization, aka, computer graphics. In one place 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.
  • Offers instruction in the author’s visualization system, lessR, which is generally less verbose than ggplot2 and lattice.
  • Inclusion of the various approaches to R graphics organized by topic instead of by system.
  • Presents the recent work on interactive visualization in R.

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.

About the Author

David W. Gerbing has a Quantitative Methods B.A. from Western Washington State College, and M.A. from Michigan State University, and a Ph.D. from Michigan State University. Dr. Gerbing teaches statistics, quantitative methods, and business research techniques. His research interests are in the areas of quantitative analysis, multivariate statistics, and behavioral measurement and assessment. Currently his primary interest is in the increasing the accessibility of the R programming language for data science so that non-programmers can access the free, open source data analysis system without a steep learning curve.

Subject Categories

BISAC Subject Codes/Headings:
COM012000
COMPUTERS / Computer Graphics
MAT029000
MATHEMATICS / Probability & Statistics / General