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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

Creating subsets of vectors


Creating subsets of data is one of the fundamental operations in data analysis. In this section, we will cover the two basic ways to create subsets of a vector. The first way involves numeric vectors, which specify the requested indices to be included in the subset. The second way involves using logical vectors, which specify for each element whether we would like to keep it or not.

Subsetting with numeric vectors of indices

Subsetting using numeric vectors of indices is done using the square brackets operator [, by providing the vector of indices within the square brackets. For example, we can select a single element of a vector by putting the value of the required index within brackets, as follows:

> x = c(5,6,1,2,3,7)
> x[3]
[1] 1
> x[1]
[1] 5
> x[6]
[1] 7

If we would like to, for example, find out the value of the last element in a given vector, we can use the length function, which returns its length (the index of the vectors' last element), as follows...

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