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Reproducible Analytical Pipelines (RAP) using R

via Udemy


Automating the production of statistical reports using DataOps principles.

What you'll learn:
  • Use DevOps to improve the production time and quality of your statistical reports.
  • Build a reproducible analytical pipeline enshrining business knowledge in an R package.
  • Automate the production of a periodic report.

At the end of my course, students will be able to identify suitable Reproducible Analytical Pipelines(RAP) opportunities in their organisation. From their chosen report they will derive the minimal tidy data set required to produce all the figures, tables and statistics therein. They will confidently use basic git functionality for version control, providing an audit trail of their progress. They will collaborate on Github using a standard workflow relying on pull requests for peer review; ensuring quality assurance throughout the project. They will build an R package, providing a single corpus to enshrine and encapsulate the business knowledge. The package will have all the hallmarks of reproducibility and quality assurance through the students’ prudent application of Open Source software development tools and principles including: functional programming, unit testing, continuous integration and dependency management. The outcome will be a software package that facilitates an improved production time of the statistical report while improving the quality of the statistics. This will free up the student's time to do more interesting things.

DISCLAIMER:The views and opinions expressed in this courseare those of the authorand do notreflect the official policy or position of GDS or the UK Government.

Taught by

Matthew Gregory


4.6 rating at Udemy based on 124 ratings

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