Skip to content

Latest commit

 

History

History
24 lines (17 loc) · 3.25 KB

RAP.md

File metadata and controls

24 lines (17 loc) · 3.25 KB

"It's difficult to wear such high Louboutins and also freestyle rap."

-- Blake Lively

No, no, no - not that kind of rap 🙈...

Reproducible Analytical Pipelines

A Reproducible Analytical Pipeline (RAP) carries out all the steps of the production process of a statistical publication in one open source software program, from data extraction to report production. It combines best practice in academia and data science in order to improve the quality, auditability and speed of production, as well as ensure knowledge transfer in organisations with high turnover in staff. Although a Reproducible Analytical Pipeline may not be appropriate for every publication, implementing just one or two techniques such as version control or peer review has the potential to improve quality and make efficiency savings. These concepts can also be applied to any piece of analytical work, not just publications.

A good place to start is this short paper which explains what RAPs are, how to assess whether your work is suitable to RAP and details several levels of RAP which can be selected depending on a number of factors, such as the skill in your team or the available IT infrastructure. The original creators of RAP, Matt Upson and his colleague Matthew Gregory, have produced the RAP Companion, which provides a wealth of information about their work and the creation of RAP, and this free online course on Reproducible Analytical Pipelines (RAP) using R. Finally, there is now a Government Statistical Service RAP website which hosts useful resources and articles by colleagues across the public sector.

If you are interested in creating a RAP, or implenting some of the best practices involved in RAP into your own work, then please see the Transforming Publishing team's toolkit which contains templates and guides to help you do this.

Further reading and resources on RAP