Module Aims:

This core module aims to equip students to competently handle data.  We will be using the R language and environment for statistical computing and graphics. We will teach:

1. Why reproducible research is important, and how to make your own analysis reproducible
2. The basics of coding in R
3. How to import data into R
4. How to clean and manipulate data
5. How to summarise data
6. How to identify and rectify errors in analysis
7. Data visualisation

We will practise some concepts learnt earlier in term e.g. trial design, age standardisation, routine data, chance, bias, confounding, measures of risk.

 

Module Learning Outcomes:

By the end of the module, students should be able to:

1. Explain why reproducible research is important and conduct their own research in a reproducible way
2. Import, input, clean, check, summarise, and manipulate raw data
3. Prepare data for analysis
4. Understand how to present data using appropriate visualisation

Pre-requisites:

R and R Studio installed on computers.
Basic skills in mathematics
Understand the principles of direct standardisation of incidence rates.

 

Teaching Strategy:

The material is work-book based, allowing students to work at the pace that suits them and whole-group discussion will be minimal.  For some sessions notes will be provided in advance.  All sessions will focus on developing practical skills.

Assessment:

Take-home assignment involving importing and manipulation of multiple data files in order to generate summary figures and tables, and associated interpretation.

Module Length: Ten 3-hour sessions