Last updated 6/2021
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.09 GB | Duration: 7h 0m
Course Tutorial to make your work reproducible using Docker Containers
What you’ll learn
Use Docker Containers to run R Scripts in a reproducible way
Create customized R Studio in a Docker Container[portable, automated updates]
Build personal Docker Images originated from verified publishers
Save Docker Images locally or using Docker Hub online repository
Share result of your work to your colleagues
Save and document your work with Version Control
Practical use of Version Control during development process
Run containers using Shell/Bat scripts
Use Auto-builds to update Docker images
Develop R packages
Develop Shiny Application with golem framework
Requirements
GitHub account
Mac or Windows PC[can also be applicable for Linux]
Basic knowledge of R programming language is preferred but not necessary
Willing to learn and use R Statistical Software
Basic knowledge of command line is preferred but not necessary
Description
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?Added value of this course is to provide a quick overview of functionality and to provide valuable methods and templates to build on. Focus of this course is to make a learning journey as easy as possible – simply watch these videos and reuse provided code!Just Start using Docker Containers with your Data Science tools by reproducing this course!
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Quick Win – Run R-Studio IDE in a Docker Container
Lecture 3 Quick Win – Run R program in a Docker Container
Lecture 4 Quick Win – Run R Shiny Application in a Docker Container
Section 2: Install Docker, Preparations, etc
Lecture 5 Introduction to this section
Lecture 6 Create an Account for DockerHub
Lecture 7 Docker Desktop for Mac
Lecture 8 Docker Desktop Settings
Lecture 9 Docker Desktop for Windows
Lecture 10 Docker for Linux
Lecture 11 Github Desktop
Section 3: Build a personal Docker Image for R-Studio IDE
Lecture 12 Motivation of this section
Lecture 13 Create a Folder for our project
Lecture 14 Put things under Version Control[Git]
Lecture 15 Build the image
Lecture 16 Taking care about Documentation (update file Readme)
Lecture 17 List all images
Lecture 18 Run the container
Lecture 19 Mapping computer folders to container
Lecture 20 Update readme file
Lecture 21 Create Executable File to run Container… make it easy
Lecture 22 Save image to the Docker Hub
Lecture 23 Saving image locally
Lecture 24 Deleting the image from your Computer
Lecture 25 Restore image from the local archive file
Lecture 26 Check running container from another terminal
Lecture 27 Install R Package in running RStudio and save image
Lecture 28 Push Changes to Docker Hub
Lecture 29 Save a new version of the image using Tags
Lecture 30 Setup Automated Build of the image
Lecture 31 Verify Automated Build
Lecture 32 Add a badge to the README file[nice to have]
Lecture 33 Practical use of R-Studio in Docker Container
Lecture 34 Summary of this chapter
Section 4: Build a personal Docker Image with R Statistical Software
Lecture 35 Motivation of this section
Lecture 36 Let’s again start with a Version control!
Lecture 37 Auto-building an image on Docker Hub
Lecture 38 Why to build own image (security)?
Lecture 39 Pull our personalized image
Lecture 40 Test our container!
Lecture 41 Summary of this chapter – ready for reproducible research
Lecture 42 Blueprint: Managing Docker Images
Lecture 43 Deleting un-used containers/images
Section 5: Customized image to make our work Reproducible
Lecture 44 Motivation of this section
Lecture 45 Blueprint for organizing Reproducible Research on Docker Containers
Lecture 46 Create our research document!
Lecture 47 Adding R Markdown to the Docker Image
Lecture 48 Test the container
Lecture 49 Push image (repetition)
Lecture 50 Publish our repository
Lecture 51 Share results: trying image on another machine
Section 6: Customized image to run R Scripts
Lecture 52 Motivation of this section
Lecture 53 Review Dockerfile
Lecture 54 Build and Push the image
Lecture 55 Test our container
Lecture 56 Publish our work in GitHub repository
Lecture 57 Summary of this section
Section 7: Docker Networks – publishing and consuming API using different Containers
Lecture 58 Introduction to multicontainer applications
Lecture 59 Note on Docker Compose
Lecture 60 Case Study: Application to verify hardware components
Lecture 61 Create Plumber API
Lecture 62 Add Plumber API into the image
Lecture 63 Create Docker Network
Lecture 64 Test connectivity between running containers
Lecture 65 Prepare to Test Multi Container Application
Lecture 66 Test Multi Container Application
Section 8: Shiny App in the Docker Container
Lecture 67 Motivation of this section
Lecture 68 Quick Win – rocker/shiny
Lecture 69 Rocker/shiny starting our Shiny Server in Docker Container
Lecture 70 Mapping: Shiny App <> Shiny Server <> Docker container
Lecture 71 Placing Shiny App into Docker Container
Lecture 72 More professional development of ShinyApps in Containers
Section 9: P1 Setup Project: Develop Shiny App as an R package in Docker Container
Lecture 73 Motivation of this section
Lecture 74 Create new Project
Lecture 75 Adding R package description
Lecture 76 Set Options to the package
Lecture 77 Add Version Control
Lecture 78 Building the package, finish step 1
Section 10: P2 golem explained: Develop Shiny App as an R package in Docker Container
Lecture 79 Investigation tactic: Let’s see developed example. Step 1: Clone others work!
Lecture 80 Step2: How to run Shiny App built with Golem framework?
Lecture 81 Step 3: Reverse engineer Golem Framework!
Section 11: P3 Dive in Version Control: Develop ShinyApp as an R package in Docker Container
Lecture 82 Deep dive in Version Control
Lecture 83 Nothing works – what to do?
Lecture 84 Back in history in a separate branch
Lecture 85 Revert single changes: commit frequently!
Lecture 86 How to delete branches?
Section 12: P4 Business Logic: Develop ShinyApp as an R package in Docker Container
Lecture 87 Adding Business Logic
Lecture 88 Develop User Interface Part 1
Lecture 89 Develop User Interface Part 2
Lecture 90 Develop Server logic Part 1
Lecture 91 Develop Server logic Part 2
Section 13: P5 Make it as a Package: Develop ShinyApp as an R package in Docker Container
Lecture 92 Detecting errors during R package checks
Lecture 93 Adding function dependencies with golem framework
Lecture 94 Adding tests
Lecture 95 Adding golem recommended tests
Lecture 96 Debugging failed tests
Section 14: P6 Setup Continuous Integ.: Develop ShinyApp as an R package in Docker Container
Lecture 97 Note about Travis CI
Lecture 98 Setup Travis CI P1
Lecture 99 Setup Travis CI P2
Lecture 100 Making Pull Request and make use of CI travis tests
Section 15: P7 Deploy Image: Develop ShinyApp as an R package in Docker Container
Lecture 101 Checking R package with R Hub
Lecture 102 Create Dockerfile using golem framework
Lecture 103 Build docker image
Lecture 104 Run the container with Shiny App as an R package!
Lecture 105 Stop Docker Container, push to Docker Hub
Lecture 106 Setup Autobuild of Docker Image
Lecture 107 Let’s try to use docker-compose to launch this app!
Section 16: P8 CI in Action: Develop ShinyApp as an R package in Docker Container
Lecture 108 Introducing Continuous Integration
Lecture 109 Introduce the ‘Ops’ task
Lecture 110 ‘Dev’ starts to work: Create Branch
Lecture 111 Side task: get rid of .DS_Store
Lecture 112 Making changes to ‘business logic’
Lecture 113 Commit changes to git
Lecture 114 Make Pull request
Lecture 115 Conclude Pull request
Lecture 116 Review DevOps process
Lecture 117 Docker Compose Pull Service
Section 17: Summary
Lecture 118 Summary of the course
Lecture 119 Useful Materials Blogs, Best practices, etc
Lecture 120 Bonus Lecture
Data Scientists willing to use Docker in their toolset,Anyone willing to deploy R script on Docker Container,Anyone willing to use R-Studio on Docker Container,Anyone curious about Docker for Data Science
Homepage
https://www.udemy.com/course/docker-containers-data-science-reproducible-research/
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