useR!2019 tutorial on Docker for Data Science

Contents

See abstract on the useR!2019 website

Preparation for the tutorial

It is absolutely mandatory to install the required software prior to the tutorial.

Software to install

By all means:

• Docker
• Docker Compose

Optionally:

• Kubectl
• Minikube

Note: The Docker daemon needs to be configured to accept connections over TCP, see https://www.shinyproxy.io/getting-started/#docker-startup-options This will be explained in the tutorial, but it does not harm to already configure it.

Docker images to pull

docker pull hello-world
docker pull openanalytics/r-base
docker pull openanalytics/shinyproxy
docker pull openanalytics/shinyproxy-snapshot
docker pull openanalytics/shinyproxy-demo
docker pull openanalytics/shinyproxy-dash-demo
docker pull openanalytics/shinyproxy-rstudio-ide-demo
docker pull openanalytics/scheduled-reporting-demo
docker pull openanalytics/scheduled-reporting-demo-report
docker pull openanalytics/rdepot-repo
docker pull openanalytics/rdepot-app
docker pull openanalytics/rtq-client
docker pull redis
docker pull apache/zeppelin:0.8.1

Git repositories to clone

• https://github.com/openanalytics/r-base
• https://github.com/openanalytics/shinyproxy-template
• https://github.com/openanalytics/shinyproxy-demo
• https://github.com/openanalytics/shinyproxy-config-examples
• https://github.com/openanalytics/shinyproxy-dash-demo
• https://github.com/openanalytics/shinyproxy-zeppelin-notebook-demo
• https://github.com/openanalytics/shinyproxy-rstudio-ide-demo
• https://github.com/openanalytics/scheduled-reporting-demo
• https://github.com/openanalytics/rdepot-demo
• https://github.com/openanalytics/rtq-docker

See you in Toulouse!