Visualising & Analysing Biomedical Datasets with R

Code: MED650, Offered By: Research Study Skills

Open To - Staff / Students University Wide

Semester Taught - Academic Year

Full Description

This unit will teach students how to visualise biomedical data using histograms, scattergrams, line plots, and more sophisticated methods including heatmaps, and how to turn these into publication-quality images. Students will learn how to interpret data and how to apply appropriate statistical tests, including all standard tests used in biomedical studies. The module will use the open source (free) software R which runs on all platforms and is fast becoming the standard statistical software in science. The module will be highly interactive with a detailed course manual (effectively a custom textbook on R in biomedical science) for directed self-study.

Researcher Development Framework Categories:

A1) Knowledge base
A2) Cognitive abilities
A3) Creativity

Teaching Methods

The course is taught online by a detailed 180 page PDF manual and 24 online video podcasts giving step by step instruction on how to setup and use R.


The exercises in the course manual will provide continuous formative assessment for the students. 

Aims Objectives

To understand and be able to use biomedical data to produce graphical visualisation and appropriate statistical tests within a common standard statistical software environment (R).

Learning Outcomes

Be able to plot histograms, scatterplots, box & whisker plots, heatmaps etc. and turn these into publication quality PDF files. Be able to design a data format and prepare data appropriately in a spreadsheet/database. Be able to load data from spreadsheets into statistical analysis software. Be able to describe the distributions and interactions of data items by interpreting visualisations such as histograms and scatterplots. Be able to select and perform appropriate statistical tests on biomedical data. Be able to write appropriate text description of the statistical tests and results that could be included in the Methods and Results section of a thesis or research paper. Be able to carry out elementary programming in R to automate data processing and visualisation (optional).

Module Dates

This module is now run via online learning in MOLE. 

Other Information

This module is a distance learning course which is run online via MOLE.  

PLEASE NOTE - There is no actual start date for this module as  this course is run via online learning in MOLE.

Please express your interest by registering on the module, and you will be contacted in due course on how to access the content in MOLE. 

Staff Contact

Prof Simon Cross  


Mon, Aug 31st 2020, 09:00

Venue: NA - Online Module, Room: NA - Online Module

Tutor: Prof Simon Cross

21 Places Available
Closing Date: August 31st, 2020

If none of the above sessions are suitable, you can add an expression of interest to your basket and you will be notified when any new sessions become available.

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