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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
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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
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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
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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
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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
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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 1. The R Environment

In this chapter, we are going to introduce the R environment, learn how to install and use it, and introduce some of the main concepts related to writing R code. First, the technical issues of setting up the work environment are covered. After that, we will have R running and ready to receive instructions from the user. The basic concepts related to working in the R environment are also introduced.

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

  • Installing R
  • Using R's command line
  • Editing code using text editors
  • Executing simple commands
  • Arithmetic operations
  • Logical operations
  • Calling functions
  • Understanding errors and warning messages
  • Checking which class a given object belongs to
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Tech Concepts
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Programming languages
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Learning R for Geospatial Analysis
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