Docker for R
Docker is a popular platform for packaging, deploying, and running applications. These applications run in containers. Crucially, this container can be used on any system: a developer’s laptop, systems on premises, or in the cloud. Containerization is a technology that’s been around for a long time, but it’s gained a new lease of life with Docker. Applications are packaged as images that contain everything needed to run them: code, libraries, and configuration.
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- Introduction to Docker and containerisation: A brief introduction to application containerisation and the available tooling including Docker
- Using, managing, and configuring containers: Using pre-built images to use create environments or services without installing extra software
- Building your own image: Customising existing images such as RStudio, Shiny Server, ShinyProxy and building news image from popular base Operating System images such as Debian, Ubuntu, and Alpine
- Managing images, automated builds, deploying in the cloud: Integrating with DockerHub, GitHub, AWS, and other services to provide a full cloud-based environment. Why we use these additional services and how to maintain an automated pipeline
- Advanced service management: Getting deeper into multi-service stacks with Docker compose. For example, setting up a full Shiny and Plumber API
- Active service monitoring: Additional tools for managing running containers, collecting logs, monitoring disk usage
- Scaling containers: An introduction to scaling via Docker swarm and kubernetes
On successful completion of the course, delegates will be able to leverage popular DockerHub images and publish their own. They will be able to configure a service stack to include a Shiny Server and an API. Delegates will gain an understanding of the deployment process and also gain some exposure to more advanced monitoring solutions for cloud applications.
The course will be a mixture of lecture style session together with practical exercises. Practical exercises will give delegates the opportunity to build and run their own Docker containers for examples including the RStudio IDE, Shiny Server, and Plumber.
This course requires basic familiarity with R and basic Shiny programming. Familiarity with Linux & git will be helpful, but not required.