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## Book Description

Code and Methods for Creating High-Quality Data Graphics

A data graphic is not only a static image, but it also tells a story about the data. It activates cognitive processes that are able to detect patterns and discover information not readily available with the raw data. This is particularly true for time series, spatial, and space-time datasets.

Focusing on the exploration of data with visual methods, Displaying Time Series, Spatial, and Space-Time Data with R presents methods and R code for producing high-quality graphics of time series, spatial, and space-time data. Practical examples using real-world datasets help you understand how to apply the methods and code.

The book illustrates how to display a dataset starting with an easy and direct approach and progressively adding improvements that involve more complexity. Each of the book’s three parts is devoted to different types of data. In each part, the chapters are grouped according to the various visualization methods or data characteristics.

Web Resource
Along with the main graphics from the text, the author’s website offers access to the datasets used in the examples as well as the full R code. This combination of freely available code and data enables you to practice with the methods and modify the code to suit your own needs.

1. Preliminaries
1. Chapter 1 Introduction
1. 1.1 What This Book Is About
2. 1.2 What You Will Not Find in This Book
3. 1.3 How to Read This Book
4. 1.4 R Graphics
5. 1.5 Packages
6. 1.6 Software Used to Write This Book
8. 1.8 Acknowledgments
2. Part I Time Series
1. Chapter 2 Displaying Time Series: Introduction
1. 2.1 Packages
2. Chapter 3 Time on the Horizontal Axis
1. 3.1 Time Graph of Different Meteorological Variables
2. 3.2 Time Series of Variables with the Same Scale
3. 3.3 Stacked Graphs
3. Chapter 4 Time as a Conditioning or Grouping Variable
1. 4.1 Scatterplot Matrix: Time as a Grouping Variable
2. 4.2 Scatterplot with Time as a Conditioning Variable
4. Chapter 5 Time as a Complementary Variable
1. 5.1 Polylines
2. 5.2 Choosing Colors
3. 5.3 Labels to Show Time Information
4. 5.4 Country Names: Positioning Labels
5. 5.5 A Panel for Each Year
6. 5.6 Traveling Bubbles
5. Chapter 6 About the Data
1. 6.1 SIAR
2. 6.2 Unemployment in the United States
3. 6.3 Gross National Income and CO2 Emissions
3. Part II Spatial Data
1. Chapter 7 Displaying Spatial Data: Introduction
1. 7.1 Packages
2. Chapter 8 Thematic Maps
1. 8.1 Proportional Symbol Mapping
1. 8.1.1 Introduction
2. 8.1.2 Proportional Symbol with spplot
3. 8.1.3 Optimal Classification and Sizes to Improve Discrimination
4. 8.1.4 Spatial Context with Underlying Layers and Labels
5. 8.1.5 Spatial Interpolation
6. 8.1.6 Export to Other Formats
2. 8.2 Choropleth Maps
3. 8.3 Raster Maps
1. 8.3.1 Quantitative Data
2. 8.3.2 Categorical Data
3. 8.3.3 Multivariate Legend
4. 8.4 Vector Fields
3. Chapter 9 Reference and Physical Maps
1. 9.1 Physical Maps
2. 9.2 OpenStreetMap with Hill Shade Layers
4. Chapter 10 About the Data
1. 10.1 Air Quality in Madrid
2. 10.2 Spanish General Elections
3. 10.3 CM SAF
4. 10.4 Land Cover and Population Rasters
4. Part III Space-Time Data
1. Chapter 11 Displaying Spatiotemporal Data: Introduction
1. 11.1 Packages