Introduction to Spatial Analysis in R

Day 1:

• Introduction to the R computer programme, vocabulary and format of different datatypes
• Using the “dplyr” and “ggplot2” packages to create numerical and visual summaries of structured data sets

Day 2:

• Introduction to reading and visualising spatial data including interactive maps using “mapview”, “tmap”, and “sf” packages.
• Demonstration of basic and some advanced spatial manipulations such as buffering, spatial joins, and distance calculations and interactive visualisation of spatial data

Day 3:

• Revision of Generalised Linear Models and their extension as Generalised Linear Mixed Models and Generalised Additive Models
• Discrete space spatial models with Markov random field smoothers

Day 4:

• Continuous space spatial models with Gaussian process based smoothers
• Reproducible reporting with R Markdown

The course is taught as a series of hands-on computer practicals using public health relevant examples from humanitarian crises. Background theory is presented by a lead tutor then students work independently or in small teams to solve a series of exercises with help available from in classroom teaching assistants. No prior experience with R is necessary for the course, but some basic knowledge and interest in epidemiological data analysis is highly desirable.