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  • Book Overview & Buying Learning R for Geospatial Analysis
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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
Buy this Book

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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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. The R Environment
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1. The R Environment
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Installing R and using the command line
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Coding with R beyond the command line
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Evaluating expressions
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Exploring the basic object types in R
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Summary
2
2. Working with Vectors and Time Series
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2. Working with Vectors and Time Series
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Vectors – the basic data structures in R
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Using functions with several parameters
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Creating subsets of vectors
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Dealing with missing values
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Writing new functions
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Working with dates and time series
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Introducing graphical functions
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Summary
3
3. Working with Tables
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3. Working with Tables
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Using the data.frame class to represent tabular data
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Controlling code execution
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Automated calculations using the apply family of functions
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Inference from tables by joining, reshaping, and aggregating
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Summary
4
4. Working with Rasters
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4. Working with Rasters
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Using the matrix and array classes
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Data structures for rasters in the raster package
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Subsetting rasters
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Overlay and reclassification of rasters
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Summary
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5. Working with Points, Lines, and Polygons
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5. Working with Points, Lines, and Polygons
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Data structures for vector layers in R
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Exploring vector layer properties and subsetting
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Geometrical calculations on vector layers
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Spatial relations between vector layers
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Joining geometries with tabular data
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Summary
6
6. Modifying Rasters and Analyzing Raster Time Series
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6. Modifying Rasters and Analyzing Raster Time Series
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Changing the spatial extent or resolution of rasters
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Raster resampling and reprojection
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Filtering and clumping
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Topography-related calculations with elevation data
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Aggregating spatio-temporal raster data
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Summary
7
7. Combining Vector and Raster Datasets
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7. Combining Vector and Raster Datasets
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Creating rasters from vector layers
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Creating vector layers from a raster
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Extracting raster values based on vector layers
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Summary
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8. Spatial Interpolation of Point Data
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8. Spatial Interpolation of Point Data
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Spatially interpolating point data
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Mapping the annual temperature in Spain
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Summary
9
9. Advanced Visualization of Spatial Data
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9. Advanced Visualization of Spatial Data
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Plotting with ggplot2 and ggmap
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Making 3D plots with lattice
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Summary
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A. External Datasets Used in Examples
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A. External Datasets Used in Examples
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B. Cited References
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B. Cited References
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Index
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Index

Chapter 2. Working with Vectors and Time Series

In this chapter, we are going to cover the basic data structure in R—a vector. Understanding vectors is the foundation for all the subsequent chapters. You will learn how to perform efficient operations on numeric and logical vectors and how to create subsets. After this, you will learn how to write custom functions in order to expand and customize R's capabilities. Working with dates and time series and the use of graphical functions are introduced at the end of this chapter.

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

  • Creating, saving, and examining the three main types of vectors
  • The principles of performing operations on vectors in R
  • Using functions that have more than one argument
  • Creating subsets of vectors
  • Dealing with missing values in vectors
  • Writing new functions
  • Working with dates
  • Displaying and saving graphical output
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Programming languages
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Learning R for Geospatial Analysis
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