RAP in Practice
Senior Statisticians Away Day
Alice Byers
Data Innovation Team, Data Division
22 November 2023
Why should I care about RAP?
- Less time spent producing routine analysis; more time for other work
What is RAP?
Examples of projects
Stages of analysis
Data extraction
Data cleaning
Data analysis
Modelling
Data visualisation
Reporting
RAP principles
In order to achieve the full benefits, at a minimum a RAP must:
Minimise manual steps
Be built using open-source software; e.g. R, Python
Be peer reviewed by colleagues
Be version controlled; e.g. Git
Be open to anyone; e.g. code published on GitHub
Follow good practice for quality assurance
Contain well-commented code and have documentation embedded
Where to start
- Following all 7 principles will help you to achieve the full benefits
- Following one or some of the principles will still bring benefits!
- Start small, review and iterate
RAP Support
We can help with:
Reviewing project requirements and mapping out a process.
Deciding where to start when transitioning a process to a RAP.
Understanding what software is available and how to make best use of it.
Following best practices for structuring scripts, files and folders for your RAP.
Using Git and GitHub, ensuring your RAP project is setup within a repository.
Applying best practices to your code, for example “do not repeat yourself” and following coding conventions from a style guide.
Reviewing your code.
