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:
- Why reproducible research is important, and how to make your own analysis reproducible
- The basics of coding in R
- How to import data into R
- How to clean and manipulate data
- How to summarise data
- How to identify and rectify errors in analysis
- 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:
- Explain why reproducible research is important and conduct their own research in a reproducible way
- Import, input, clean, check, summarise, and manipulate raw data
- Prepare data for analysis
- 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