Using R for Longitudinal Data Analysis
Date:
16/02/2026 - 17/02/2026
Organised by:
University College London
Presenter:
Dr Liam Wright
Level:
Intermediate (some prior knowledge)
Contact:
Any questions about the course content can be directed to the teacher, Liam Wright (liam.wright@ucl.ac.uk)
Location:
View in Google Maps (WC1E 6BT)
Venue:
University College London
Gower Street
London
(Precise location details will be emailed to registered participants)
Description:
This is an in-person two-day course for quantitative researchers wanting to perform longitudinal data analyses with the programming language R. The course will comprise six sessions delivered as guided walkthroughs. Together these will provide the necessary programming skills for producing full longitudinal analyses reproducibly from beginning to middle and end (exploring raw data, performing analyses, and presenting results in publication-ready tables and figures).
The course will be built around a single real-world analysis to demonstrate the full quantitative research pipeline in R. Attendees will leave with an appreciation of the power of R, including how R can be used to perform many analyses in an efficient way (e.g., performing Outcome-Wide and Specification Curve Analyses or other ‘many-model’ approaches).
The six sessions are:
- An Introduction to the tidyverse
- Opening and Exploring Raw Datasets
- Data Wrangling
- Generating Descriptive Statistics
- Performing Regression Analyses
- Presenting Results in Tables and Plots
The course is suited to PhD students and above. Attendees are expected to have some knowledge of R (at least the level in this document: https://osf.io/94rpq), as well as knowledge of longitudinal data analysis methods - this course is about implementing methods, rather than teaching the methods [e.g., mixed effects models] themselves).
Cost:
£200 (PhD Students) or £400 (Otherwise)
Website and registration:
Region:
Greater London
Keywords:
Quantitative Data Handling and Data Analysis, Descriptive Statistics, Regression Methods, Longitudinal Data Analysis, R, Creating graphs and charts
Related publications and presentations from our eprints archive:
Quantitative Data Handling and Data Analysis
Descriptive Statistics
Regression Methods
Longitudinal Data Analysis
R
Creating graphs and charts