Learning R for Geospatial Analysis电子书

Learning R for Geospatial Analysis

Table of Contents

Learning R for Geospatial Analysis

Credits

About the Author

About the Reviewers

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Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code and data

Downloading the color images of this book

Errata

Piracy

Questions

1. The R Environment

Installing R and using the command line

Downloading R

Installing R

Using R as a calculator

Coding with R beyond the command line

Approaches to editing R code

Installation of RStudio

Using RStudio

Evaluating expressions

Using arithmetic and logical operators

Using functions

Dealing with warning and error messages

Getting help

Exploring the basic object types in R

Everything is an object

Storing data in data structures

Calling functions to perform operations

A short sample session

Summary

2. Working with Vectors and Time Series

Vectors – the basic data structures in R

Different types of vectors

Using the assignment operator to save an object

Removing objects from memory

Summarizing vector properties

Element-by-element operations on vectors

The recycling principle

Using functions with several parameters

Supplying more than one argument in a function call

Creating default vectors

Creating repetitive vectors

Substrings

Creating subsets of vectors

Subsetting with numeric vectors of indices

Subsetting with logical vectors

Dealing with missing values

Missing values and their effect on data

Detecting missing values in vectors

Performing calculations on vectors with missing values

Writing new functions

Defining our own functions

Setting default values for the arguments

Working with dates and time series

Specialized time series classes in R

Reading climatic data from a CSV file

Converting character values to dates

Examining our time series

Creating subsets based on dates

Introducing graphical functions

Displaying vectors using base graphics

Saving graphical output

The main graphical systems in R

Summary

3. Working with Tables

Using the data.frame class to represent tabular data

Creating a table from separate vectors

Creating a table from a CSV file

Examining the structure of a data.frame object

Subsetting data.frame objects

Calculating new data fields

Writing a data.frame object to a CSV file

Controlling code execution

Conditioning execution with conditional statements

Repeatedly executing code sections with loops

Automated calculations using the apply family of functions

Applying a function on separate parts of a vector

Applying a function on rows or columns of a table

Inference from tables by joining, reshaping, and aggregating

Using contributed packages

Shifting between long and wide formats using melt and dcast

Aggregating with ddply

Joining tables with join

Summary

4. Working with Rasters

Using the matrix and array classes

Representing two-dimensional data with a matrix

Representing more than two dimensions with an array

Data structures for rasters in the raster package

Creating single band rasters

Creating multiband rasters

Writing raster files

Exploring a raster's properties

Subsetting rasters

Accessing raster values as a vector

Accessing raster values with the matrix notation

Subsets involving more than one layer

Transforming a raster into a matrix or an array

Overlay and reclassification of rasters

Raster algebra and overlay operations

Reclassifying raster values

Summary

5. Working with Points, Lines, and Polygons

Data structures for vector layers in R

Points

Lines

Polygons

Exploring vector layer properties and subsetting

Examining vector layer properties

Accessing the attribute table of vector layers

Subsetting vector layers

Geometrical calculations on vector layers

Reprojecting vector layers

Working with the geometrical properties of vector layers

Spatial relations between vector layers

Querying relations between vector layers

Creating new geometries

Calculating distances between geometries

Joining geometries with tabular data

Summary

6. Modifying Rasters and Analyzing Raster Time Series

Changing the spatial extent or resolution of rasters

Merging rasters

Cropping and trimming

Aggregating and disaggregating

Raster resampling and reprojection

Raster resampling

Raster reprojection

Filtering and clumping

Topography-related calculations with elevation data

Slope and aspect calculation

Hillshade

Aggregating spatio-temporal raster data

The time dimension

Spatial dimensions

Summary

7. Combining Vector and Raster Datasets

Creating rasters from vector layers

Rasterizing vector layers

Masking values in a raster

Creating vector layers from a raster

Raster-to-points conversion

Raster-to-contours conversion

Raster-to-polygons conversion

Extracting raster values based on vector layers

Extracting by points

Extracting by polygons

Summary

8. Spatial Interpolation of Point Data

Spatially interpolating point data

Nearest-neighbor interpolation

IDW interpolation

Interpolation using Ordinary Kriging

Using covariates in Universal Kriging interpolation

Mapping the annual temperature in Spain

Summary

9. Advanced Visualization of Spatial Data

Plotting with ggplot2 and ggmap

An overview of ggplot2

Plotting nonspatial data

Saving the ggplot2 plots

Plotting spatial data

Adding static maps from the Web

Making 3D plots with lattice

Summary

A. External Datasets Used in Examples

B. Cited References

Index