Virtual useR! 2024

I presume that, as R developers, we’ve heard of or perhaps used shiny package to develop web applications, and that, we can attest to the fact that it has emerged as a popular framework for developing dashboards in R. These shiny dashboards provide a dynamic platform for data exploration, analysis, and dissemination, making them invaluable tools for researchers, analysts, and decision-makers. However, as the volume and complexity of data grow, maintaining and updating these dashboards manually can become a time-consuming and error-prone process. Automating updates to shiny dashboards deployed on shiny server offers a solution to this challenge, enabling developers to streamline the deployment pipeline, enhance efficiency, and ensure the timely dissemination of accurate information. This session will be a hands-on tutorial that will take you through the process of setting up the workflow that works in such a way that, a push event to the main branch of a GitHub repository triggers a webhook that in turn sends a payload to an API which also triggers a bash script to do a git pull from the same repository.

Tutorial Materials: https://github.com/oyogo/useR2024_tutorial_materials

The workshop will focus on different deployment designs of machine learning pipelines using R, open-source applications, and free-tier tools. We will use the US hourly demand for electricity data from the EIA API to demonstrate the deployment of a pipeline with GitHub Actions and Docker that fully automates the data refresh process and generates a forecast on a regular basis. This includes the use of open-source tools such as point-blank to monitor the health of the data and the model's success. Last but not least, we will use Quarto doc to set up the monitoring dashboard and deploy it on GitHub Pages.