Training the Trainers Programme 2023
| Date | Course Leader | Course Name |
|---|---|---|
| Thursday 23 March | Dr. Craig Alexander | An introduction to Data Visualisation in R |
| Wednesday 5 April | Dr. Craig Alexander | Cluster Analysis in R |
| Wednesday 12 April | Dr. Vinny Davies | Aproaches for Model Selection |
| Wednesday 19 April | Dr. Thees Spreckelsen | Answering questions with count data: Introduction to models and R |
| Wednesday 26 April | Dr. Eilidh Jack | Spatial Modelling in R |
An Introduction to R Programming
Dr. Craig Alexander
School of Mathematics and Statistics
In this short course, we will introduce elementary graphical methods used for data visualisation. This module will focus on tools within R which allow users to create powerful graphics to aid exploration of datasets. This will begin with the basic plot function but will progress to more advanced graphics using the ggplot2 package. We will cover a variety of example datasets and data structures, including spatial data.
Please note this course will be delivered online via Zoom.
The course will be structured as follows:
- A short introductory session and summary lecture material, live on Zoom at 1pm.
- A set of exercises which participants can work through to explore the module. Participants are expected to work through these between both live sessions, in advance of the final discussion forum. Solutions will be shared after the course.
- An online forum, live on Zoom at 4pm, where participants can ask questions on the material with a short review.
For the session, we will use the statistical computing environment R and user interface RStudio. Participants should install both software’s before the session. The software and download instructions can be found at:
R - https://cran.r-project.org/
Rstudio - https://www.rstudio.com/products/rstudio/download/
Time – 1pm-2pm, 4pm-5pm
Eventbrite link - https://www.eventbrite.co.uk/e/an-introduction-to-data-visualisation-with-r-tickets-556646854727
Clustering Analysis in R
Dr. Craig Alexander
School of Mathematics and Statistics
Cluster analysis is a set of exploratory unsupervised learning methods that seek to discover a priori unknown group structure in quantitative data. This short course will introduce the participants to the basic idea of cluster analysis and a detailed tutorial on 2 classical and widely-used methods of cluster analysis: hierarchical agglomerative clustering and k-means. Although there will be some technical details, equations will be kept to a minimum and the focus will be building an intuition about the methods. Examples will be demonstrated in R and there will be short lab sessions for the course members to try out the methods in R themselves. The goal is for participants to end the course with an understanding of clustering, an awareness of when it is appropriate to use and when not, an ability to run and interpret a cluster analysis in R, issues and caveats to looks out for, and links to further methods of interest.
Please note that a working knowledge of the R statistical software language (at least the ability to open and manipulate data files, run summaries and plot data) will be assumed. This will not be a course introducing the basics of R.
Time: 1pm – 4pm
Eventbrite link - https://www.eventbrite.co.uk/e/cluster-analysis-in-r-tickets-558034274537
Approaches for Model Selection
Dr. Vinny Davies
School of Mathematics and Statistics
Model selection is one of the fundamental tasks of scientific enquiry, though can vary by the level of abstraction. For example, we may be interested in selecting the best hyperparameters for a selected method, which must be selected before model fitting. Conversely, model parameters are parameters which arise because of the fit.
In this short course, we will explore different approaches for hyperparameter tuning and model selection, with a practical application to several case studies.
This course will require a working knowledge of the statistical programming language R.
Time: 1pm – 3pm
Eventbrite Link - https://www.eventbrite.co.uk/e/approaches-for-model-selection-tickets-559602555307
Spatial Modelling in R
Dr. Eilidh Jack
School of Mathematics and Statistics
Spatial data are common in many fields like environmental monitoring and public health, however communicating spatial data is difficult. In this course your will learn how statistics is used to model, interpret and visualise spatial data.
This course will be useful for those who work with spatial data across a variety of fields. This course will suit anyone who is looking to learn about modelling and visualising their spatial data as well as those who would like to refresh what they have learned before. No prior knowledge of spatial statistics is required.
Course aim
After this course you are able to:
Load spatial datasets into R and produce exploratory visualisations and summaries.
Identify spatial trends and autocorrelation.
Interpret R output and produce high quality visualisations of results.
This course will take place online via Zoom
Time: 1pm-3pm
Eventbrite Link - https://www.eventbrite.co.uk/e/spatial-modelling-in-r-tickets-559449176547
Answering questions with count data: Introduction to models and R
How many times is Joe Biden mentioned in relation to democracy?
How many counts of theft are related to changing numbers of police officers?
These questions are the focus of count data models. This brief introduction will give an overview how to model counts, potential issues involved and how to implement the most common models in R.
Participants should be familiar with R.
This event will take place via Zoom from 2pm - 4pm
Eventbrite link - https://www.eventbrite.co.uk/e/answering-questions-with-count-data-introduction-to-models-and-r-tickets-595734346467