Modern Data Visualization with R

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.

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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
Provides 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

The book is written for those new to data analysis as well as the seasoned data scientist. It can be used for both teaching and research, and 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.