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Learning R for Geospatial Analysis

You're reading from   Learning R for Geospatial Analysis Leverage the power of R to elegantly manage crucial geospatial analysis tasks

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Product type Paperback
Published in Dec 2014
Publisher Packt
ISBN-13 9781783984367
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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Michael Dorman Michael Dorman
Author Profile Icon Michael Dorman
Michael Dorman
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Table of Contents (13) Chapters Close

Preface 1. The R Environment FREE CHAPTER 2. Working with Vectors and Time Series 3. Working with Tables 4. Working with Rasters 5. Working with Points, Lines, and Polygons 6. Modifying Rasters and Analyzing Raster Time Series 7. Combining Vector and Raster Datasets 8. Spatial Interpolation of Point Data 9. Advanced Visualization of Spatial Data A. External Datasets Used in Examples
B. Cited References
Index

Vectors – the basic data structures in R

A vector is an ordered collection of values of the same type (or mode, in R terminology). As mentioned in the previous chapter, the three types of values that are useful for most purposes (including the topics of this book) are numeric, character, and logical. In this section, you are going to learn about several methods to create vectors, check the properties of interest for the given vectors, and perform operations involving pairs of vectors. You are also going to learn how to save the objects we create in the temporary computer memory via assignment.

Different types of vectors

Vectors are the most basic data structures in R since single elements (such as the number 10) are also represented in R by vectors (of length 1). As we have previously seen, when we enter a numeric value on the command line, it is printed on the screen. The number in square brackets to the left of the value is, in fact, the position of the leftmost element in the respective...

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