Docker Containers for Data Science and Reproducible Research

Get excited. This course is designed to jump-start using Docker Containers for Data Science and Reproducible Research by reproducing several practical examples. Course will help to setup Docker Environment on any machine equipped with Docker Engine (Mac, Windows, Linux).

Course will proceed with all steps to create custom and distributed development environment [RStudio] in a container. Forget about manual update of your Development Environment. Work as usual, add or develop the research document into your Container, test it and distribute in an image.

Result will be reproducible independently on the R version, perhaps after several years. Same about running R programs in the container. We will demonstrate this capability including testing the container on completely different machines (Mac, Windows, Linux)Summary of ideas we will cover in this course:Reproduce and share work on a different infrastructureBe able to repeat the work after several yearsUse R-Studio in an isolated environmentTips to personalize work with Docker including usage of Automated BuildsWhat is covered by this course.

This course will provide several use cases on using Docker Containers for Data Science:Preparing your computer for using DockerWorking pipeline to develop docker imageBuilding Docker image to work with R-Studio in Interactive modeBuilding Docker images to run R programsUsing Docker network to communicate between containersBuilding ShinyServer in Docker containerWalk-though example of developing Shiny App as an R Package and deploying in Docker Container using golem frameworkMore relevant materials may be added to this course in the future (e. g. continous integration and deployment, docker-compose)Why to take this course and not other.