Displaying Time Series, Spatial, and Space-Time Data with R

by Oscar Perpinan Lamigueiro

Overview

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 static graphics, interactive visualizations, and animations, of time series, spatial, and space-time data. Practical examples using real-world datasets help you understand how to apply the methods and code.

 

While the first edition primarily focused on static graphics, and the second edition significantly expanded upon interactive elements, this edition has been thoroughly updated with a modern emphasis. Key enhancements include:

·        Comprehensive updates to all chapters: Incorporating the latest advancements in packages like "sf" and "terra."

·        New content: Introducing valuable topics such as trajectories, hill shading, and cartograms, enriching the scope of the book.

 

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 three parts of the book is devoted to different types of data. In each part, the chapters are grouped according to the various visualization methods or data characteristics.

 

Features

•        Offers detailed information on producing high-quality graphics, interactive visualizations, and animations

•        Uses real data from meteorological, climate, economic, social science, energy, engineering, environmental, and epidemiological research in many practical examples

•        Shows how to improve graphics based on visualization theory

•        Provides the graphics, data, and R code on the author’s website, enabling you to practice with the methods and modify the code to suit your own needs.

Product Details

ISBN-13:	9781032779201
Publisher:	CRC Press
Publication date:	11/09/2025
Series:	Chapman & Hall/CRC The R Series
Edition description:	3rd ed.
Pages:	310
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

Oscar Perpiñán-Lamigueiro is an Associate Professor at the Universidad Politécnica de Madrid, involved in teaching and research of Electrical Engineering, Electronics and Programming. He is also a lecturer of Photovoltaic and Solar Energy at the Escuela de Organización Industrial. He holds a Master's Degree in Telecommunications Engineering and a PhD in Industrial Engineering. At present, his research focuses on solar radiation (forecasting, spatial interpolation, open data) and software development with R (packages rasterVis, solaR, meteoForecast, PVF, tdr). https://www.routledge.com/authors/i2144-oscar-perpin-lamigueiro

Table of Contents

List of Figures 1 Introduction 1.1 What This Book Is About 1.2 What You Will Not Find in This Book 1.3        How to Read This Book 1.4 R Graphics 1.5 Packages 1.6 Software Used to Write This Book 1.7 About the Author 1.8 Acknowledgments I Time Series 2 Displaying Time Series: Introduction 2.1 Packages  2.2 Further Reading  3 Time on the Horizontal Axis 3.1 Time Graph of Variables with Different Scales  3.2 Time Series of Variables with the Same Scale  3.3 Stacked Graphs  3.4 Interactive Graphics  4 Time as a Conditioning or Grouping Variable 4.1 Scatterplot Matrix: Time as a Grouping Variable  4.2 Scatterplot with Time as a Conditioning Variable 5 Time as a Complementary Variable 5.1 Polylines 5.2 A Panel for Each Year 5.3 Interactive Graphics: Animation  6   About the Data 6.1 SIAR   6.2 Unemployment in the United States 6.3 Gross National Income and CO2 Emissions  IISpatial Data 7 Displaying Spatial Data: Introduction 7.1 Packages  7.2 Further Reading 8 Thematic Maps: Proportional Symbol Mapping 8.1  Introduction  8.2 Proportional Symbol Mapping  8.3 Optimal Classification to Improve Discrimination  8.4 Spatial Context with Underlying Layers and Labels 8.5 Spatial Interpolation  8.6 Interactive Graphics  9 Thematic Maps: Choropleth Maps 9.1 Introduction 9.2 Quantitative Variable 9.3 Qualitative Variable 9.4  Small Multiples with Choropleth Maps   9.5 Bivariate Map  9.6 Interactive Graphics 10  Thematic Maps: Cartogram Maps 10.1 Introduction  10.2 Data 10.3 Cartograms 11 Thematic Maps: Raster Maps 11.1Quantitative Data 11.2 Categorical Data 11.3 Bivariate Legend 11.4 Interactive Graphics 12 Vector Fields  12.1 Introduction 12.2 Arrow Plot 12.3 Streamlines 13  Physical Maps 13.1 Introduction 13.2 Retrieving Data 13.3 Labels 13.4  Overlaying Layers of Information 14 Reference Maps 14.1 Introduction 14.2  Retrieving Data from OpenStreetMap 14.3 Hill Shading 14.4 Overlaying Layers of Information 15  About the Data  15.1  Air Quality in Madrid 15.2  Spanish General Elections 15.3 Gross Domestic Product and Population  15.4  CM SAF 15.5  Land Cover and Population RastersIII Space-Time Data 16  Displaying Spatiotemporal Data: Introduction 16.1 Packages  16.2 Further Reading  17  Spatiotemporal Point Observations  17.1 Introduction  17.2     Data processing 17.3  Graphics  17.4  Animation 18  Spatiotemporal Areal Data 8.1 Introduction 18.2 Data  18.3 Choropleth   18.4 Cartogram  19  Spatiotemporal Raster Data  19.1 Introduction  19.2  Level Plots   19.3  Graphical Exploratory Data Analysis 19.4   Space-Time and Time Series Plots 19.5  Animation  20  Animation 20.1 Time Trajectory   20.2     Fly-by Animation  20.3Hill shading animation  IV  Glossary, Bibliography and Index   Bibliography   Index  

Editorial Reviews

"The author is knowledgeable in the different data formats for time series in R as well as various different displays from modern R packages that can be used to present time series data. A small proportion of the material discusses the findings that can be drawn from each time series. The major focus of the book is on how time series are manipulated, or R functions are used to produce a specific figure. Both static and dynamic summaries of data are provided, and much discussion is given to displaying multiple time series."
~Peter Craigmile, The Ohio State University

"This book addresses a fundamental gap that makes R a more usable geographic information system for applied statisticians…This book is incredibly useful for any person wanting to do modern spatial and spatio-temporal statistics. This book is technically correct. It is also clearly written and quite easy for a person with a moderate level of R programing experience to use."
~Trevor Hefley, Kansas State University

"(This book) should be useful and successful across a range of audiences: researchers and practitioners working with temporal/spatial data; professors using the manuscript to supplement their courses on temporal/spatial data; graduate students learning about temporal/spatial data. I have been a part of these audiences at various stages of my own professional career, and would have loved to be ‘exposed’ to the manuscript earlier."
~Vladas Pipiras, University of North Carolina Chapel Hill

"While texts on spatiotemporal data analysis exist, there is a lack of resources and references when it comes to address the challenges of producing spatiotemporal visualizations, particularly in combination with reproducible example code and data. This book aims to address this void, and in this regard, is a very valuable and needed contribution."
~Claudia Engel, Stanford University

"This is a book specializing on visualization of time/space data. The topics covered are relevant and interesting…The updates planned for the second edition focus on ggplot2 and interactive web-based plots. These have both become mainstream, so such an update would be appropriate and topical."
~Deepayan Sarkar, Indian Statistical Institute, Delhi

"Overall, the book is unique in what it tries to achieve. It is an excellent resource that researchers and other users can use to explore different visualisations and read on how to build them from scratch in R."
~Andrew Zammit Mangion, University of Wollongong

 "In summary, Displaying Time Series, Spatial, and Space-Time Data with R is a useful handbook for those wanting to learn more about temporal, spatial, and space-time data classes in R; methods for wrangling such data; and, of course, approaches for visualizing the data. Those who are already familiar with temporal/spatial/space-time data may also find it a useful overview of methods they may not have previously encountered. It is well-written and provides a nice synthesis of additional resources for those who might be interested in digging deeper into a particular topic."
~Silas Bergen, Winona State University

From the Publisher