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.
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.
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Oscar Perpiñán Lamigueiro is a lecturer of photovoltaic and solar energy at the Escuela de Organización Industrial (EOI) and an assistant professor of electrical engineering at the Universidad Politécnica de Madrid (UPM). He develops R packages that provide graphical methods to display multivariate time series, spatial data, and space-time data (rasterVis and solaR).Review:
"... a practical guide for producing high-quality graphics for time series, spatial data and space-time data using the statistical software package R. ... recommended as a practical guide for readers with fair knowledge of programming with R. All visualization methods are presented by means of real data sets and programming code is available."
―Zentralblatt MATH 1306
"... a researcher in this field will particularly benefit from this compact account of some elegant visualization techniques implemented in R for time, space, and timespace data. ... Colorful illustrations that nicely complement the descriptive aspects of the book help in the process of understanding. ... a valuable source of graphical visualization analyses in R ..."
―International Statistical Review, 2015
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