creating-maps.bib

@Book{brunsdon_introduction_2015,
  title = {An introduction to {R} for spatial analysis \& mapping},
  isbn = {1-4462-7294-X},
  publisher = {Sage},
  author = {Chris Brunsdon and Lex Comber},
  year = {2015},
}

@Book{lamigueiro_displaying_2014,
  title = {Displaying {Time} {Series}, {Spatial}, and {Space}-{Time} {Data} with {R}},
  isbn = {978-1-4665-6520-3},
  abstract = {Code and Methods for Creating High-Quality Data Graphics  A data graphic is not only a static image, but it also tells a story about the data. It activates cognitive processes that are able to detect patterns and discover information not readily available with the raw data. This is particularly true for time series, spatial, and space-time datasets.  Focusing on the exploration of data with visual methods, Displaying Time Series, Spatial, and Space-Time Data with R presents methods and R code for producing high-quality graphics of time series, spatial, and space-time data. Practical examples using real-world datasets help you understand how to apply the methods and code.  The book illustrates how to display a dataset starting with an easy and direct approach and progressively adding improvements that involve more complexity. Each of the book’s three parts is devoted to different types of data. In each part, the chapters are grouped according to the various visualization methods or data characteristics.  Web ResourceAlong with the main graphics from the text, the author’s website offers access to the datasets used in the examples as well as the full R code. This combination of freely available code and data enables you to practice with the methods and modify the code to suit your own needs.},
  language = {en},
  publisher = {CRC Press},
  author = {Oscar Perpinan Lamigueiro},
  month = {apr},
  year = {2014},
  keywords = {Mathematics / General, Mathematics / Probability \& Statistics / General, Science / Earth Sciences / Geology, Science / Life Sciences / Biology},
}

@Book{colin_gillespie_robin_lovelace_efficient_2016,
  title = {Efficient {R} {Programming}},
  isbn = {978-1-4919-5078-4},
  url = {http://shop.oreilly.com/product/0636920047995.do},
  abstract = {Become a more productive programmer with Efficient R Programming. Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace give practical advice on a range of topics—from optimizing set-up of RStudio to...},
  urldate = {2016-05-31TZ},
  author = {{Colin Gillespie, Robin Lovelace}},
  year = {2016},
}

@Article{pebesma_spacetime:_2012,
  title = {spacetime: {Spatio}-temporal data in r},
  volume = {51},
  number = {7},
  journal = {Journal of Statistical Software},
  author = {Edzer Pebesma},
  year = {2012},
  pages = {1--30},
}

@Book{wickham_advanced_2014,
  title = {Advanced {R}},
  url = {http://www.crcpress.com/product/isbn/9781466586963 http://adv-r.had.co.nz http://www.crcpress.com/product/isbn/9781466586963 http://adv-r.had.co.nz},
  publisher = {CRC Press},
  author = {Hadley Wickham},
  year = {2014},
}

@Book{bivand_applied_2013,
  title = {Applied spatial data analysis with {R}},
  volume = {747248717},
  publisher = {Springer},
  author = {Roger S Bivand and Edzer J Pebesma and Virgilio G{\a'o}mez-Rubio},
  year = {2013},
}

@InCollection{cheshire_spatial_2015,
  title = {Spatial data visualisation with {R}},
  url = {https://github.com/geocomPP/sdv},
  booktitle = {Geocomputation},
  publisher = {SAGE Publications},
  author = {James Cheshire and Robin Lovelace},
  editor = {Chris Brunsdon and Alex Singleton},
  year = {2015},
  pages = {1--14},
}

@Article{torfs_very_2014,
  title = {A (very) short introduction to {R}},
  url = {http://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf},
  journal = {Comprehensive R Archive Network},
  author = {Paul Torfs and Claudia Brauer},
  year = {2014},
}

@Book{dorman_learning_2014,
  title = {Learning {R} for {Geospatial} {Analysis}},
  publisher = {Packt Publishing Ltd},
  author = {Michael Dorman},
  year = {2014},
}

@Article{lovelace_introduction_2014,
  title = {Introduction to visualising spatial data in {R}},
  url = {https://github.com/Robinlovelace/Creating-maps-in-R},
  abstract = {This tutorial is an introduction to spatial data in R and map making with R's `base' graphics and the popular graphics package ggplot2. It assumes no prior knowledge of spatial data analysis but prior understanding of the R command line would be beneficial. For people new to R, we recommend working through an `Introduction to R' type tutorial, such as {"}A (very) short introduction to R{"} (Torfs and Brauer, 2012) or the more geographically inclined {"}Short introduction to R{"} (Harris, 2012). Building on such background material, the following set of exercises is concerned with specific functions for spatial data and visualisation. It is divided into five parts: *Introduction, which provides a guide to R's syntax and preparing for the tutorial *Spatial data in R, which describes basic spatial functions in R *Manipulating spatial data, which includes changing projection, clipping and spatial joins *Map making with ggplot2, a recent graphics package for producing beautiful maps quickly *Taking spatial analysis in R further, a compilation of resources for furthering your skills An up-to-date version of this tutorial is maintained at https://github.com/Robinlovelace/Creating-maps-in-R and the entire tutorial, including the input data can be downloaded as a zip file, as described below. The entire tutorialwas written in RMarkdown, which allows R code to run as the document compiles. Thus all the examples are entirely reproducible. Suggested improvements welcome - please fork, improve and push this document to its original home to ensure its longevity. The tutorial was developed for a series of Short Courses put on by the National Centre for Research Methods, via the TALISMAN node (see geotalisman.org).},
  number = {03},
  journal = {Comprehensive R Archive Network},
  author = {Robin Lovelace and James Cheshire},
  year = {2014},
}