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Mapping in R



May 01, 2015 to May 29, 2015 November 20, 2015 to December 18, 2015 May 06, 2016 to June 03, 2016 November 18, 2016 to December 16, 2016 May 05, 2017 to June 02, 2017 November 17, 2017 to December 15, 2017 May 04, 2018 to June 01, 2018 November 16, 2018 to December 14, 2018

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Mapping in R

taught by Prof. Chris Brunsdon

Aim of Course:

In this online course, “Mapping in R,” you will be provided with a very flexible environment for statistical analysis, including a number of facilities to manipulate, visualise and analyse spatial information. Here, the emphasis is on visualising spatial data. A key advantage of mapping in R is that it is very easy to integrate the results of data analysis with the map creation process.

The key aims of the course are to:

  • Appraise the use of R as a tool for creating maps, identifying its strengths and weaknesses
  • Explain how spatial data may be written/read and visualised in R
  • Consider how publication quality maps may be produced in R, based on the GISTools package
  • Provide an overview of a number of diverse methods for visually representing geographical data in R

Should you take this course, or "Spatial analysis techniques in R?"

"Mapping in R" concentrates on the use of R for the display of spatial data, especially area lattice data (e.g. states, counties, provinces), including handling different map projections, incorporating data acquired over the internet, and producing publication quality maps. Much of the work involves the use of the instructor’s own GISTools package. If your interest is more on the statistical analysis of the three major types of spatial objects (point events, spatial lattices or continuous surfaces), "Spatial analysis techniques in R" might be the better choice. There is virtually no overlap between these courses.

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

WEEK 1: Spatial Objects in R

  • Types of Spatial Objects
  • Getting shapefiles into R
  • Attributes
  • Map projections and rgdal

WEEK 2: Map Components in R

  • Legends
  • Scales, North Arrows, Labels
  • Colouring schemes
  • Can a red/green colorblind person read your map?

WEEK 3: Mapping Data From the Internet

  • The RgoogleMaps Library
  • The Google coordinate system
  • Sources of geographical data and APIs
  • A live map in a function

WEEK 4: Further Topics

  • Other approaches to mapping: ggmap
  • Incorporating maps in 3d graphics
  • Basic GIS operations via rgeos
  • Basic map topology with spdep


In this course the homework is a mixture of some simple exercises and consists of guided mapping and visualisation problems using R.

In addition to assigned readings, this course also has supplemental readings available online, and practice exercises

Mapping in R


May 01, 2015 to May 29, 2015 November 20, 2015 to December 18, 2015 May 06, 2016 to June 03, 2016 November 18, 2016 to December 16, 2016 May 05, 2017 to June 02, 2017 November 17, 2017 to December 15, 2017 May 04, 2018 to June 01, 2018 November 16, 2018 to December 14, 2018

Course Fee: $629

Do you meet course prerequisites? What about book & software? (Click here to learn more)

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)

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Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

Mapping in R

taught by Prof. Chris Brunsdon

Who Should Take This Course:

Those who have a need to incorporate data with a spatial location into the R environment, appraise the use of packages in R to visualize such data (as opposed to use of a GIS) and to produce publication quality maps of these data, including mashing them onto public domain products such as Google Maps.



These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

If you are new to R and doubtful about your ability to learn R quickly enough to follow along in the course, we recommend first taking one of the introductory R courses.

Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement: about 15 hours per week, at times of  your choosing.

Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

All required materials will be provided.

Note:  If you're unfamiliar with cartographic concepts, a good and affordable introduction is given in Designing Better Maps: A Guide for GIS Users by Cynthia A. Brewer (ESRI Press, 2005), but it is not required in the course, nor is it referenced in the materials.


You must have a copy of R for the course. Click Here for information on obtaining a free copy.

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