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Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks
Author
Title
Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks / Michael Dorman.
ISBN
9781783984374
1783984376
1783984368
9781783984367
Publication
Birmingham, England : Packt Publishing, 2014.
Copyright Notice Date
©2014
Physical Description
1 online resource (364 pages) : color illustrations.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
Annotation 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 softwarea working installation of R is all that is necessary to begin.
Variant and related titles
O'Reilly Safari. OCLC KB.
Other formats
Print version: Dorman, Michael. Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks. Birmingham, England : Packt Publishing, ©2014 Community experience distilled.
Format
Books / Online
Language
English
Added to Catalog
January 14, 2020
Series
Community Experience Distilled
Bibliography
Includes bibliographical references and index.
Contents
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 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 operationsA short sample session; Summary; Chapter 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 indicesSubsetting 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 graphicsSaving graphical output; The main graphical systems in R; Summary; Chapter 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 functionsApplying 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; Chapter 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
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