Teaching

Experience teaching R and reproducible data analysis.

I am a Posit (formerly RStudio) Certified Tidyverse Instructor with over 5 years experience teaching reproducible data analysis in R to a variety of audiences. Typically these courses centre around the analysis of environmental data, specifically long term monitoring data.

I have delivered training to learners from organisations such as:

• UK Department for the Environment, Food and Rural Afairs (Defra)

• UK Health Security Agency (UKHSA)

• UK Environment Agency (EA)

• The Clean Air Society of Australia and New Zealand (CASANZ)

• Port Talbot Local Authority

• UK Centre for Ecology & Hydrology (UKCEH)

• The Wolfson Atmospheric Chemistry Laboratories (WACL)

Approach

I am a proponent of live coding in teaching, meaning that learners can see RStudio used in an authentic way. Authentic examples showing real-world applications of R are important to ensure that the content is relevant to learner’s interests. Teaching is supported by extensive, reproducible learning materials written in quarto (formerly rmarkdown). Where possible on longer courses, case studies are written up using data provided by learners to get them started with their own data analysis projects.

Figure 1 shows some example course materials produced for a recent course on using R for air quality modelling. An advanced lesson, it outlines a method to use readr and purrr to rapidly pull large amounts of kilometre grid-square modelled air quality data from the “UK AIR” website and then plot it using ggplot2. All of the content is reproducible, allowing learners to explore it further at a later date.

Figure 1: An example of reproducible course materials, commonly produced when teaching R.