Free R books for GIS and Spatial Data Analysis
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Contents
Free R books for GIS and Spatial Data Analysis
In this article cartogeek.com are going to publicize some of the main R books for GIS and Data Science, all of them access Free R books for GIS in PDF for download freely by users.
The R programming language is a powerful tool to perform statistical analysis which can also solve complexes spatial data analysis.
We could consider that R constitutes one of the main programming languages of the geospatial sector, present for decades, although its great popularity is rather recent.
The connection of GIS and Cartography, the Geostatistics and Data Science for geographic information ( o Spatial Data Science) is more present than ever, with an increasing number of specialists and application to all types of projects.
Use the R programming language for these purposes it is a great opportunity for multiple professionals in the geotechnological sector: analysts and data scientists, university researchers and scientists, technicians and GIS analysts, as well as sector specialists in urban and territorial planning, geomarketing and the study of populations or professionals in biology and environmental sciences, among many others.
Hopefully this listing of R books oriented to GIS and spatial data analysis It can serve to promote the use and knowledge of this programming language increasingly used by the community.
Free R books for GIS
Space Analysis with R: Use R as a Geographic Information System.
- Title: Spatial analysis with R. Use R as a Geographic Information System.
- Author: Jean-François Mas.
- Editorial: European Scientific Institute
- Year of the edition: 2018
This is one of the reference books on programming in R available at Spanish. Most technical books about the language R they are written in the English language. This, however, is a powerful and complete book translated entirely into Spanish.
As the author himself indicates, this book aims to introduce regular users of the GIS in the world of programming in R:
» This book is intended for users with basic knowledge of Geographic Information Systems ( SIG ) who wish to start managing and analyzing spatial data in R. Therefore, it does not require any prior knowledge of this program, but rather a basic knowledge of GIS. »
It stands out for not being excessively heavy and focuses a lot on the utilities of this language for Geographic Information Systems and the spatial data analysis both in raster and vector format.
He also dedicates chapters to cover aspects of spatial analysis and the Geostatistics, as well as the Remote sensing and the processing and classification of satellite images. Finally shows how to create map displays with R and to link R with QGIS.
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An Introduction to Spatial Data Analysis and Visualization in R
- Title: An Introduction to Spatial Data Analysis and Visualization in R
- Authors: Guy Landsley and James Chesire
- Editorial: Consumer Data Research Center
- Year of the edition: 2016
The book, written by two authors of the University College of London makes an introduction to R programming language and RStudio as a preferred IDE to work with scripts and view data in R.
Treats all essential points to familiarize yourself with the R language applied to spatial analysis and the spatial statistics.
In this sense, it covers aspects such as the exploration of spatial data, the search for patterns and spatial relationships between objects, regression and spatial interpolation.
In addition, it covers other essential points of R and RStudio such as the realization and configuration of graphics such as histograms, boxplots or box charts, bivariate charts and, of course, the realization of maps to approximate the use of R as if of a GIS it will be.
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Applied Spatial Data Analysis with R
- Title: Applied Spatial Data Analysis in R
- Authors: Roger S. Bivand, Edzer J. Pebesma and Virgilio Gómez-Rubio
- Editorial: Springer.
- Year of the edition: 2008.
Book Applied Spatial Data Analysis in R it is certainly similar to the previous one, although more extensive and complex. It focuses mainly on the data analysis with R in depth and detail.
It contains an extensive and detailed material, really interesting for users who want to delve into the spatial data analysis. Among the English-speaking books, this is probably one of the most interesting and comprehensive for the quality and length of the content.
However, it may not be the most suitable for users who do not have any experience and wish to start with the R programming language.
It places special emphasis on the following points:
- Spatial data interpolation
- Spatial point pattern analysis
- Spatial autocorrelation
- Applied geostatistics
- Space statistics
- Generation and adjustment of space models
In addition, it offers information and examples of data visualization, so much about graphics as maps, as well as methods of reading and writing spatial data, using cartographic projections and libraries to process and analyze geographic information.
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R for Data Science
- Title: R for Data Science
- Authors: Hadley Wickham and Garett Grolemund
- Editorial: O’Reilly
- Year of the edition: 2017
The structure of this book focuses especially on the different steps that are usually carried out when carrying out projects related to data analysis in R.
Thus, each chapter dedicated to each of the phases:
- Exploratory data analysis
- Reading, transformation and adaptation
- Function programming and iteration
- Conceptualization and model building
- Data-based information communication
In fact, the focus of the book is on the workflow or workflow of the Data Science, so it would actually serve as a reference for any programming language other than strictly R. However, all the examples and methods shown use the R programming language as a reference and the content adapts to it.
This book can be useful to any user who wants to familiarize themselves with this language to carry out projects related to the data science, without waiting for a great deepening in advanced topics such as Big data.
I would personally highlight the good examples and data analyzed, as well as the introduction to R Markdown to make sheets that combine code, text and objects such as graphs, maps, tables, etc., to communicate information and workflows efficiently.
It is interesting to note that it has exercises so that the reader can apply the concepts explained in the various chapters, illustrated with examples, about which questions are asked or the code is asked to adapt to obtain a certain result.
Link to digital content: https://r4ds.had.co.nz/
R programming for Data Science
- Title: R Programming for Data Science
- Author: Roger D. Peng
- Year of the edition: 2015
Another free book in English, highly consulted by all those users who want to start in R as a programming language for Data Science.
This book covers all the basics of the grammar of language R and the main packages to read, treat, analyze and visualize data of any kind.
Explains aspects of language such as data types, code control structures, creation and use of functions, iterations or loops, or the concept of scope in R.
This is a recommendation specially indicated for users who start from scratch to programming and want to get acquainted with it language R to start carrying out projects data analysis and visualization.
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The R Book
- Title: The R Book
- Author: Michael J. Crawley
- Editorial: Wiley
- Year of the edition: 2013
As its name suggests, it is « the » R. Book. It has neither more nor less than 1,060 pages (! ). So it’s about a manual to read and consult with all the tranquility of the world.
Of course, given its vast length, it covers in detail all aspects of the language R in depth and with a high quality of content.
The author, professor of botany and ecology of the Imperial College of London, has configured this great manual to cover practically all the details of the use of R for statistics and data analysis.
It could be used for students or professionals who wish to use it as a reference and reference book to answer questions and understand technical aspects of the language, although it may not be the most « friendly » to start R for data science.
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R Notes for Professionals
This book, in a friendlier format and outside the most formal circuit in the university world, tries to provide all the tools, concepts and working methods for professionals who use ( or want to learn ) R for their projects.
Of course, it is quite complete and covers practically all aspects of the R programming language to work freely.
Featible content of the book:
- Definitions and data types
- Reading and data manipulation
- Use of classes, functions and conditional structures
- Graphics data visualization
- Functional programming
- Regular expressions in R
- Using R Markdown
- Using Spark for Big Data
- Machine Learning
- Random Forest algorithms
- Natural language processing
Also dedicate chapters to spatial data analysis ( chapter 48 ), when treating raster geographic information ( chapters 71 and 95 ), cheat mapping ( chapter 85 ) or aspects such as clustering hierarchical ( chapter 107 ), among others.
It is undoubtedly a good reference manual to learn and deepen your knowledge of R. It allows the reader to delve into more advanced aspects of it aimed at Big data and Machine Learning, always supported by code, data and graph examples.
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Geocomputation with R
- Title: Geocomputation with R
- Authors: Robin Lovelace, Jakub Nowosad and Jannes Muenchow
- Editorial: CRC Press
- Editing year: 2019
To finish, we propose another of the reference books that focus on the use of R to treat and analyze spatial information.
It is a very complete book, very well documented with examples and offers great detail in the explanations.
It is not particularly extensive nor does it entertain itself in covering the most basic aspects of the language ( we have suggested other free books to start in R ), but it focuses very quickly on the treatment of spatial data.
It covers aspects such as:
- Geometric and spatial operations with R
- Geographical data projections
- Import and export of spatial data
- Map creation with R
- Space algorithms and functions
- Similarities and differences of R with a Geographic Information System
- Space statistics and models
Finally,Free R books for GIS it proposes various application examples, focusing on three areas of great interest where the language R can play a very important role in studies of data analysis: transportation, geomarketing and ecology or conservation.
Although there is a printed version of the book, it is also free and open to be consulted in its version online. Also, the version online It has two important advantages: it is more up-to-date than printed editions and the code and graphics are more easily accessible.
Free R books for GIS
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