R

The following set of courses discuss R. The details for each of the courses can be found on their respective pages.

Introduction to R Self Study Notes

Self-study notes: Introduction to R

The course introduces students to using RStudio for writing scripts and executing R commands, emphasizing the importance of various data types and object structures for effective data storage. Participants will learn how to read, manipulate, and save data, visualize it through commonly used figures, and perform basic inferential statistical tests. Additionally, the course covers fundamental programming terminology and concepts, such as variables, functions, and for loops, which are transferable to other programming languages.

Introduction to Regression with R

Clickable Link to Self Study Notes

This course builds on prior experience with simple linear regression in R, extending these foundations to cover generalised linear models and regression techniques suited to different data types. Through practical examples, you will learn how to select, fit, and interpret appropriate regression models for continuous and binary outcomes using R.

Regression Analysis in R: Adapting to Varied Data Types

Clickable Link to Self Study Notes

This session extends simple linear regression to cover generalised linear models in R, enabling you to analyse both continuous and binary outcomes and work effectively with a wider range of predictor variables. As part of the Regression with R workshop series, it focuses on selecting appropriate models and interpreting coefficients to answer more complex research questions.

Mixed Effects Regression with R

Clickable Link to Self Study Notes

This session builds on prior regression knowledge to introduce mixed effects and multi-level models in R, enabling you to analyse more complex data structures and address a broader range of research questions. You will learn how to specify appropriate models—including interactions—extract and summarise results, and interpret outputs as part of a coherent series of Regression with R workshops.

Working With Data in R Self Study Notes

Self-study notes: Working With Data In R

The course introduces the principles of the ‘tidy’ data format, a widely recognized convention for structuring data, and teaches how to read tabular data from files into dataframes. Students will learn to manipulate datasets by creating new variables, filtering, summarizing, sorting, joining multiple datasets, and reshaping data, as well as working with strings and dates when time permits. Additionally, the course provides hands-on experience with R notebooks for conducting and documenting data analysis effectively.