less Active Hours, no authentication, single instance, etc.) Persistent storage system sounds exciting. They are also constantly investing in the development of back-end components.
For example, the Starter plan (Up to 1GB RAM) will cost you $9 per month with a limited number of Active Hours. The service is also not competitively priced if you only look at the computing power for your cash. Although the app would normally be in the sleep mode if no one is using it, the wakening/sleeping cycle may take a few minutes so even someone briefly logs on and off the app, it is likely to cost 0.1-0.5 Active Hours. So three clients use the app for 1 hour each would lead to 3 Active Hours. However, it can be quite pricey as their pricing plans are based on Active Hours, which are the number of hours users are active on the apps. If you are using RStudio IDE and signed up for a shinyapps.io account, it is just a few clicks away to deploy your apps. RStudio fully manages all the back-end DevOps so no need to set up the environment, R server or worrying about scaling. Shinyapps.io is an RStudio service that provides an easy solution to publish Shiny apps. In this post, I will provide a quick review of different deployment strategies and guide you through one of my favourite methods using ShinyProxy with Docker. Important features to consider are customisation of the UI, authentication, access control, security, performance and cost-efficiency. For example, building a client-facing model prediction app would have its unique requirements. There are many ways of deploying Shiny apps and they all have their advantages and drawbacks, thus need to be decided on a case-by-case basis.
Although it is relatively easy to build a Shiny app and make it run on our local machines, deploying the app on the cloud for production could be a daunting task. Built by RStudio, this package is highly integrated with the RStudio IDE, making it the primary choice for production. R Shiny is a powerful tool for building data products, from data visualisations to predictive models.
INSTALL R STUDIO ON AWS WINDOWS PRO
Shiny Server or Shiny Server Pro on own premise or cloud.
Depending on what you are looking for and your experience with Docker technology and ShinyProxy, you may want to check my other tutorials:įor learning the framework and testing containerised Shiny apps locally: This postįor securely deploying Shiny apps on Clouds (single node, the docker-compose way): Securing and Monitoring ShinyProxy Deployment of R Shiny Appsįor deploying secure, scalable, production-grade Shiny apps with Docker Swarm: Effectively Deploying and Scaling Shiny Apps with ShinyProxy, Traefik and Docker Swarm