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.