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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

Chapter 3. Working with Tables

Working with tables is central to programming in R, both with regards to spatial analysis (for example, working with attribute tables of geometries) and more generally. In this chapter, we will learn how to work with tables on their own, while in the subsequent chapters, we will see the ways that spatial data analysis involves dealing with tables. At the same time, two central subjects, which we will have to be familiar with for the subsequent chapters, will be introduced. These are working with contributed packages in R and controlling code execution.

As a central example, we will work with real-world data (monthly climatic records for Spain, which were downloaded from the NOAA archive) so that we can witness several very common cleaning and reshaping procedures of tables.

In this chapter, we'll cover the following topics:

  • Working with data.frame objects to represent tables in R
  • Controlling code execution through conditional statements and loops...
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