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Learning R for Geospatial Analysis

Learning R for Geospatial Analysis

By : Michael Dorman
3.9 (7)
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Learning R for Geospatial Analysis

Learning R for Geospatial Analysis

3.9 (7)
By: Michael Dorman

Overview of this book

This book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external software—a working installation of R is all that is necessary to begin.
Table of Contents (13 chapters)
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10
A. External Datasets Used in Examples
11
B. Cited References
12
Index

Conventions

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, folder names, filenames, file extensions, and pathnames are shown as follows: "Here, we created a data.frame object named df by combining the vectors num, lower, and upper."

A block of code is set as follows:

> num = 1:4
> lower = c("a","b","c","d")
> upper = c("A","B","C","D")
> df = data.frame(num, lower, upper)
> df
  num lower upper
1   1     a     A
2   2     b     B
3   3     c     C
4   4     d     D

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Under the Getting Started section, select the download R link."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Learning R for Geospatial Analysis
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