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Displaying time series, spatial, and...

Perpiñán Lamigueiro, Oscar.

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Displaying time series, spatial, and space-time data with R /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Displaying time series, spatial, and space-time data with R // Oscar Perpiñán Lamigueiro.
作者:	Perpiñán Lamigueiro, Oscar.
出版者:	Boca Raton :CRC Press, Taylor & Francis Group, : 2014.,
面頁冊數:	vii, 200 p. :ill. (chiefly color), color maps ;24 cm.
標題:	Time-series analysis - Data processing. -
ISBN:	9781466565203

Displaying time series, spatial, and space-time data with R /
Perpiñán Lamigueiro, Oscar.

Displaying time series, spatial, and space-time data with R /Oscar Perpiñán Lamigueiro. - Boca Raton :CRC Press, Taylor & Francis Group,2014. - vii, 200 p. :ill. (chiefly color), color maps ;24 cm. - Chapman & Hall/CRC the R series..

Includes bibliographical references (pages 191-196) and index.

This book explores methods to display time series, spatial and spacetimedata using R, and aims to be a synthesis of both groups providing code and detailed information to produce high quality graphics with practical examples. Organized into three parts, the book covers the various visualization methods or data characteristics. The chapters are structured as independent units so readers can jump directly to a certain chapter according to their needs. Dependencies and redundancies between the set of chapters have been conveniently signaled with cross-references.

ISBN: 9781466565203GBP49.99

LCCN: 2014001305Subjects--Topical Terms:

700459
Time-series analysis
--Data processing.

LC Class. No.: QA280 / .P475 2014

Dewey Class. No.: 519.5/502855133

Displaying time series, spatial, and space-time data with R /
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100 1 $a Perpiñán Lamigueiro, Oscar. $3 2121410
245 1 0 $a Displaying time series, spatial, and space-time data with R / $c Oscar Perpiñán Lamigueiro.
260 # $a Boca Raton : $b CRC Press, Taylor & Francis Group, $c 2014.
300 $a vii, 200 p. : $b ill. (chiefly color), color maps ; $c 24 cm.
490 1 0 $a Chapman & Hall/CRC the R series.
504 $a Includes bibliographical references (pages 191-196) and index.
520 # $a This book explores methods to display time series, spatial and spacetimedata using R, and aims to be a synthesis of both groups providing code and detailed information to produce high quality graphics with practical examples. Organized into three parts, the book covers the various visualization methods or data characteristics. The chapters are structured as independent units so readers can jump directly to a certain chapter according to their needs. Dependencies and redundancies between the set of chapters have been conveniently signaled with cross-references.
520 # $a Chapter 1 Introduction 1.1 What this book is about A data graphic is not only an static image. It tells an story about the data. It activates cognitive processes which 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 data sets. There are several excellent books about data graphics and visual perception theory, with guidelines and advice for displaying information including visual examples. Let's mention "The elements of graphical data" [Cleveland, 1994] and "Visualizing Data" [Cleveland, 1993] byW. S. Cleveland, "Envisioning information" [Tufte, 1990] and "The visual display of quantitative information" [Tufte, 2001] by E. Tufte, "The functional art" by A. Cairo [Cairo, 2012], and "Visual thinking for design" by C.Ware [Ware, 2008]. Ordinarily they don't include the code or software tools to produce those graphics. On the other hand, there are a collection of books which provide code and detailed information about the graphical tools available with R. Commonly they do not use real data in the examples, and do not provide advice to improve graphics according to visualization theory. Three books are the unquestioned representatives of this group: "R Graphics" by P. Murrell [Murrell, 2011], "lattice" by D. Sarkar [Sarkar, 2008], and "ggplot2" by H. Wickham [Wickham, 2009].
650 # 0 $a Time-series analysis $x Data processing. $3 700459
650 # 0 $a R (Computer program language). $3 740890