Mapping in R

Mapping in R

taught by Chris Brunsdon

 
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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

Homework

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

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.
Level:
Intermediate
Prerequisite:
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:
Options for Credit and Recognition:

Specialization:
Specializations are an easy way for you to demonstrate mastery of a specific skill in statistics and analytics. This course is part of the Spatial Analytics Specialization which gives a deep dive into analyzing location and geospatial data.

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.

Software:
You must have a copy of R for the course. Click Here for information on obtaining a free copy.
Instructor(s):

Dates:

May 03, 2019 to May 31, 2019 November 22, 2019 to December 20, 2019 May 01, 2020 to May 29, 2020 November 20, 2020 to December 18, 2020

Mapping in R

Instructor(s):

Dates:
May 03, 2019 to May 31, 2019 November 22, 2019 to December 20, 2019 May 01, 2020 to May 29, 2020 November 20, 2020 to December 18, 2020

Course Fee: $589

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

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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