This core module aims to teach students the principles of data carpentry, data summarisation and data visualisation. The importance of replicability and how to achieve this will be emphasised. Students will be taught to apply the principles using R in the R studio environment. The module also revises and applies some epidemiological and statistical concepts covered earlier in the course.
Module Learning Outcomes:
By the end of the module, students should be able to:
- Understand the rationale for replicable data analysis
- Clean a data file and manipulate the data using the key ‘tidyverse’ functions filter(), mutate(), select(), arrange(), group_by()
- Learn principles of data summarisation and visualisation
- Effectively summarise data/results using a table
- Effectively summarise data in a graph using ggplot()
R and R Studio installed on computers.
Basic skills in mathematics
Understand the principles of direct standardisation of incidence rates.
Lectures with embedded practical exercises used to develop basic data handling and analysis skills using the ‘tidyverse’ suite of packages from R implemented in R Studio.
Take-home assignment involving importing and manipulation of multiple data files in order to generate summary figures and tables.
Module Length: 6 days over 3 weeks