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Learning R for Geospatial Analysis

By : Michael Dorman

3.9 (7)

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. The R Environment

1. The R Environment

Installing R and using the command line

Coding with R beyond the command line

Evaluating expressions

Exploring the basic object types in R

Summary

2. Working with Vectors and Time Series

2. Working with Vectors and Time Series

Vectors – the basic data structures in R

Using functions with several parameters

Creating subsets of vectors

Dealing with missing values

Writing new functions

Working with dates and time series

Introducing graphical functions

Summary

3. Working with Tables

3. Working with Tables

Using the data.frame class to represent tabular data

Controlling code execution

Automated calculations using the apply family of functions

Inference from tables by joining, reshaping, and aggregating

Summary

4. Working with Rasters

4. Working with Rasters

Using the matrix and array classes

Data structures for rasters in the raster package

Subsetting rasters

Overlay and reclassification of rasters

Summary

5. Working with Points, Lines, and Polygons

5. Working with Points, Lines, and Polygons

Data structures for vector layers in R

Exploring vector layer properties and subsetting

Geometrical calculations on vector layers

Spatial relations between vector layers

Joining geometries with tabular data

Summary

6. Modifying Rasters and Analyzing Raster Time Series

6. Modifying Rasters and Analyzing Raster Time Series

Changing the spatial extent or resolution of rasters

Raster resampling and reprojection

Filtering and clumping

Topography-related calculations with elevation data

Aggregating spatio-temporal raster data

Summary

7. Combining Vector and Raster Datasets

7. Combining Vector and Raster Datasets

Creating rasters from vector layers

Creating vector layers from a raster

Extracting raster values based on vector layers

Summary

8. Spatial Interpolation of Point Data

8. Spatial Interpolation of Point Data

Spatially interpolating point data

Mapping the annual temperature in Spain

Summary

9. Advanced Visualization of Spatial Data

9. Advanced Visualization of Spatial Data

Plotting with ggplot2 and ggmap

Making 3D plots with lattice

Summary

A. External Datasets Used in Examples

A. External Datasets Used in Examples

B. Cited References

B. Cited References

Index

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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