R is a statistical programming language that you access through Rstudio. We use R in a broad range of subjects at UNE. This site compiles some information that will be useful at different stages of your R journey.
If you are new to R, start with the foundational topics. If you are in STAT100 or QMER100, you can stick to the foundational topics and basic working with data. If you are confident or enrolled in a second or third year subject, you’ll want to explore some of the Intermediate/Advanced topics.
Getting R and Rstudio – includes both the standard instructions for downloading & installing R and Rstudio, and cloud-based options for if you have trouble.
Navigating Rstudio – an introduction to the Rstudio interface, how to get set up with a project, and how to run your code.
Variables and types of R objects – understanding variables and different types of data is fundamental to knowing how your code is working and for identifying problems.
Common problems and troubleshooting – everyone who uses R encounters problems, here are some solutions to common problems as well as tips on how to solve problems and ask for help more generally.
R packages – many of the most powerful tools you will use in R come from packages. Here you will learn how to install them, load them, and find information about them.
Getting and summarising data – to analyse your data, you must first load it into R’s memory, and understand how it is organised.
Base data visualisation – the best way to understand the patterns in your data is to produce useful plots. You can find examples of the most common base plots here.
Core statistical programming – R is a programming language, and understanding how to use it as such gives you a lot of flexibility when performing simulations or analysing your data.
Common probability distributions – Probability underpins most statistical analysis, and there are a collection of probability distributions we see most often.
Understanding the tidyverse – As you use R for more advanced operations, you may encounter an ecosystem called the tidyverse. Here we briefly summarise what that means.
Data visualisation with ggplot2 – Producing professional quality visualisations is critical if you are to analyse data professionally. The tools provided in the ggplot2 package are the most efficient way to do this.
Linear models refresher – Linear models are the foundation of much of modern applied statistics, and are assumed background in most advanced subjects.