The UCLH learning-datascience repository contains training materials introducing R for the manipulation and visualisation of patient data. It assumes no knowledge of R, but is structured so that those who know some R can also benefit. It starts with a two day course and provides materials to take things further.
These resources are part of a UCLH fellowship program developing the capacity of clinicians to use R and other modern data tools to manipulate hospital generated data. In the longer term the aim is to help clinicians develop modern, reproducible data workflows – ‘good enough’ scientific computing - to make their work more efficient. We will be working to improve the interfaces between these practices and the hospital data systems. Here is a 10 minute talk from the 2021 NHSR-community conference outlining the fellowship program.
The initial two day course was delivered online by Andy South.
If working through these resources on your own work through the instructions & links here.
- Become familiar with R and RStudio
- Troubleshoot inevitable issues and find solutions with Google
- Be able to read data into R
- Learn about good data practice
- Be able to work with R objects, particularly vectors and dataframes
- Understand what are R functions and packages
- Know how to use RStudio projects
- Manipulate data with dplyr
- Visualise data with ggplot2
- Brief exposure to rmarkdown for reproducible reports and shiny for web applications
- Get a feeling for the potential of R and motivation to learn more
A series of modular mini-examples demonstrating coding solutions to common issues for learners starting to deal with patient data.
Practical tips including where best to access RStudio store your data etc.
Outlining the local implementation of the five safes framework for data security at UCLH.
A dictionary giving brief explanation & links for data science terms & acronyms in UCLH and beyond.
Summary of updates.
This course is licensed under a Creative Commons share-alike licence. Creative Commons
The course has been adapted from the ClinicianCoders and Data Science for Doctors courses.