June Training Update
This summer, we have public courses to take you all the way from the very basics of R, through to using R for statistical modelling, with some data wrangling and intermediate programming in between. Wherever you are on your R journey, take a look at our upcoming courses to see if we can help you on your way.
Introduction to R
Course Level: Foundation
Next course date: 26th June 2023
R is a versatile language for statistical computing and graphics. In this course you will learn the advantages of using R and how to get started. You will gain familiarity with the RStudio interface and learn the R basics. Also included is an introduction to the Tidyverse and how to use various packages for data storage, visualisation and manipulation. This course provides a great foundation to begin your R journey!
Data Wrangling in the Tidyverse
Course Level: Foundation
Next course date: 3rd July 2023
If you work with data, you probably spend a lot of time cleaning it and wrangling it into the correct shape. This course will show you how you can use R to efficiently clean and wrangle your data into a format that’s ready for analysis. You will learn about the Tidyverse, what tidy data really is, and how to practically achieve it with packages such as {dplyr}, {tidyr}, {lubridate} and {forcats}.
Programming with R
Course Level: Intermediate
Next course date: 10th July 2023
The benefit of using a programming language such as R is that we can automate repetitive tasks. This course covers the fundamental techniques such as functions, for loops and conditional expressions. By the end of this course, you will understand what these techniques are and when to use them. This is a one-day intensive course on R.
Statistical Modelling with R
Course Level: Intermediate
Next course date: 17th July 2023
From the very beginning, R was designed for statistical modelling. Out of the box, R makes standard statistical techniques easy. This course covers the fundamental modelling techniques. We begin the day by revising hypotheses tests, before moving onto ANOVA tables and regression analysis. The class ends by looking at more sophisticated methods such as clustering and principal components analysis (PCA).
