ONLINE COURSE – Visual Exploration, Analysis, and Presentation of Spatial Data using the ‘tmap’ Package (TMAP01) This course will delivered live
6 May 2025 - 9 May 2025
£450.00
Event Date
Tuesday, May 6th, 2025
Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UK local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
About this course
R statistical software is becoming increasingly popular for spatial analysis and visualization—and for good reason. It is reproducible, flexible, and supported by a vast ecosystem of R packages dedicated to spatial data. An essential part of working with spatial data is visualization, not only for communication but also for exploration and analysis. This in-depth course focuses on the R package tmap, one of the most widely used packages for spatial data visualization. The course covers all key steps, from reading spatial data to publishing high-resolution static maps or interactive maps that can be embedded in web articles and dashboards. Participants will work with essential spatial data packages in particular sf, terra, and stars. The course also addresses key methodological aspects of spatial data visualization, including map projections, selecting the most appropriate visualization method for a given task, and choosing color schemes that account for accessibility and cultural considerations. Innovative spatial visualization techniques are also explored, including cartograms, grid maps (also known as origin-destination maps), and glyph-based visualizations.
By the end of the course, participants will:
- Know how to use the core packages sf, terra, and stars to read and process spatial data in R, including joining data sources and performing geospatial data manipulations.
- Be able to use tmap for exploring, analysing, and presenting spatial data.
- Create various types of thematic maps.
- Understand the methodological advantages and limitations of different map types, enabling informed decisions based on data characteristics and target users.
- Recognize key considerations when selecting a suitable colour palette.
- Be able to fine-tune map layouts in tmap, including adding map components, customizing legends, and incorporating map insets.
- Know how to export maps in various static and interactive formats.
Intended Audiences
- Academics and postgraduate students working on projects related to spatial data.
- Applied researchers and analysts in public, private, or third-sector organizations who need the reproducibility, speed, and flexibility of a command-line language like R for spatial data analysis and visualisation.
- Data journalists looking to create engaging and informative maps for news articles, dashboards, and infographics.
- Current R users wanting to update their knowledge of spatial data packages, particularly tmap.
Venue
Delivered remotely
Course Details
Venue – Delivered remotely
Time zone – UK local time
Availability – 25 places
Duration – 4 days
Contact hours – Approx. 24 hours
ECT’s – Equal to 2 ECT’s
Language – English
Teaching Format
The course includes introductory lectures on general spatial data analysis and visualisation, as well as intermediate-level lectures and hands-on practicals on working with spatial data for exploration, analysis, and presentation. Datasets for computer practicals will be provided by the instructors, but participants are encouraged to bring their own data.
Assumed quantitative knowledge
A basic understanding of mathematical and statistical concepts.
Assumed computer background
Good familiarity with R, including the ability to import/export data, manipulate data.frames, and generate simple exploratory plots. Familiarity with the tidyverse packages, particularly dplyr and tidyr, is recommended. Experience with git is also recommended.
Equipment and software requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
COURSE PROGRAMME
Tuesday 6th
Day 1 – Classes from 12:00 – 20:00
Getting started
- Overview of the core R packages for spatial data analysis and visualisation.
- Overview of resources: online books, Stack Overflow, GitHub, etc.
- Methodology of spatial data visualisation.
- Creating common thematic map types using the R package tmap.
- Exploring colour palettes with the R package cols4all.
Wednesday 7th
Day 2 – Classes from 12:00 – 20:00
Visualisation of spatial vector data in R
- Introduction to map projections (coordinate reference systems).
- Working with vector data in R using the sf package.
- Reading and writing spatial vector data in various formats.
- Joining spatial and non-spatial data.
- Geospatial data manipulations.
- Visualisation of vector data with tmap.
- Using basemaps in tmap.
- Creating cartograms with the R package tmap.cartogram.
Thursday 8th
Day 3 – Classes from 12:00 – 20:00
Visualisation of spatial raster data in R
- Working with raster data in R using the terra package.
- Working with spatiotemporal data cubes in R using the stars package.
- Reading and writing spatial raster data in various formats.
- Downsampling, warping, and transforming raster data.
- Converting spatial vector data to raster data and vice versa.
- Visualisation of raster data with tmap.
Friday 9th
Day 4 – Classes from 12:00 – 20:00
Finalising and exporting maps
- Exporting static maps to various formats, including bitmap (JPG, PNG) and vector formats (SVG, PDF).
- Exporting interactive maps to HTML.
- Integrating tmap with the R package shiny for dashboards.
- Fine-tuning map layouts for high-quality publications.
- Extensibility of tmap
