Spatial Data Analysis in R
Date:
20/05/2025 - 21/05/2025
Organised by:
Royal Statistical Society
Presenter:
Jumping River Tutor
Level:
Intermediate (some prior knowledge)
Contact:
Description:
Level: Intermediate (I)
As spatial data sets get larger, more sophisticated software needs to be harnessed for their analysis. R is now a widely used open source software platform for working with spatial data thanks to its powerful analysis and visualisation packages. The focus of this course is providing participants with the understanding needed to apply R’s powerful suite of geographical tools to their own problems.
Topics Covered
- Introducing R as a GIS
- The structure of spatial objects in R
- Loading and interrogating spatial data
- Visualisaing spatial datasets with tmap
- Data manipulation with spatial data using dplyr
- Spatial joins
- Coordinate reference systems (CRS)
- Interactive maps with leaflet
Learning Outcomes
By the end of the course, delegate will:
Have an understanding of different types of spatial data.
Know how to plot static maps using {tmap}, including adding multiple layers and colouring by variables.
Understand how to manipulate spatial data using {dplyr} and {sf} functions.
Gain knowledge of coordinate reference systems (CRS) and know how to define a CRS.
Understand and be able to create interactive maps using {leaflet}.
Understand how to create initial maps and add objects containing different types of data.
Be able to customise their interactive maps with colours, labels and legends.
Target Audience
Participants with spatial data problems who are not making use of R and are falling behind in the ever changing world of data science.
Assumed Knowledge
A basic understanding of the R software is assumed.
For this online course, participants are not required to have R installed on their own laptops. A virtual environment, which can be accessed through a web browser, will be used to run R and view course materials.
Cost:
Prices from £427.20 to £592.80 (inc.VAT)
Website and registration:
Region:
Greater London
Keywords:
Quantitative Data Handling and Data Analysis, R, Data manipulation, dplyr, Spatial joins, CRS
Related publications and presentations from our eprints archive:
Quantitative Data Handling and Data Analysis
R