This repo contains information to setup a dockerized instance with R, Rstudio, Shiny, Radiant, Python, and JupyterLab
To use the docker images you first need to install Docker
- For Mac: https://docs.docker.com/docker-for-mac/
- For Windows: https://docs.docker.com/docker-for-windows/
- For Linux: https://docs.docker.com/engine/installation/
After installing Docker, check that it is running by typing docker --version
in a terminal. This should return something like the below:
docker --version
Docker version 18.03.1-ce, build 9ee9f40
The full rsm-msba
setup uses Docker Compose so also check this is available by typing docker-compose --version
in a terminal. This should return something like the below:
docker-compose --version
docker-compose version 1.21.1, build 5a3f1a3
On windows please install Git Bash:
http://www.techoism.com/how-to-install-git-bash-on-windows/
For detailed install instructions on Windows see install/rsm-msba-windows.md
For detailed install instructions on macOS see install/rsm-msba-macos.md
To jump straight in and run the main application run the command below on macOS:
docker run --rm -p 8080:80 -p 8787:8787 -p 8989:8888 -v ~:/home/rstudio vnijs/rsm-msba
For Windows run the command below:
docker run --rm -p 8080:80 -p 8787:8787 -p 8989:8888 -v c:/Users/$USERNAME:/home/rstudio vnijs/rsm-msba
Perhaps even easier, you can start the rsm-msba
container on macOS using launch-mac.command
and on Windows using launch-windows.sh
. To get these files download the repo https://github.com/radiant-rstats/docker or clone the repo using git clone https://github.com/radiant-rstats/docker.git
is you have git installed. To run the script on Windows you will need Git Bash installed
as referenced above.
Another alternative approach is to use docker-compose
and the command below after cloning the repo:
docker-compose -f ./rsm-msba/docker-rsm-msba.yml up
Note: For Windows you may need to change the path in the volumes:
section to c:/Users/$USERNAME
For more information about running the radiant
application see radiant/README.md
For more information about running the rsm-msba
application see rsm-msba/README.md
You probably don't want to run this image by itself. It is used in the radiant
and rsm-msba
application (see below). To build a new container based on r-bionic
add the following at the top of your Dockerfile
FROM vnijs:docker-bionic
To build r-bionic yourself use:
docker build -t $USER/r-bionic ./r-bionic
Push to docker hub:
sudo docker login
docker push $USER/r-bionic
The second image builds on r-bionic
and adds radiant and required R-packages. To build a new container based on radiant
add the following at the top of your Dockerfile
FROM vnijs:radiant
To build radiant yourself use:
docker build -t $USER/radiant ./radiant
Push to docker hub:
sudo docker login
docker push $USER/radiant
Add the following to .Rprofile in your home directory
options(radiant.ace_vim.keys = FALSE)
options(radiant.maxRequestSize = -1)
# options(radiant.maxRequestSize = 10 * 1024^2)
options(radiant.report = TRUE)
# options(radiant.ace_theme = "cobalt")
options(radiant.ace_theme = "tomorrow")
# options(radiant.ace_showInvisibles = TRUE)
The third image builds on the radiant image and adds python and Jupyter. To build a new container based on rsm-msba
add the following at the top of your Dockerfile
FROM vnijs:rsm-msba
To build rsm-msba yourself use:
docker build -t $USER/rsm-msba ./rsm-msba
Push to docker hub:
sudo docker login
docker push $USER/rsm-msba
The rsm-msba directory also contains a docker-compose file that pulls in a postgres image and database admin tool adminer. To run the full application use the command below.
docker-compose -f ./rsm-msba/docker-rsm-msba.yml up
If you want to install an R-package, e.g., fortune
, in a way that persists when using the container again, use the command below. This will install the package and create a personal directory for future package installs. You will only need to add the lib = Sys.getenv("R_LIBS_USER")
argument once to generate the personal directory.
install.packages("fortunes", lib = Sys.getenv("R_LIBS_USER"))
If you want to install a python package, e.g., redis
, in a way that persists when using the container again, use the command below from the Jupyter (or Rstudio) terminal. This will install the package and create a personal directory for future package installs.
pip3 install -U "redis"
To stop (all) running containers use:
docker kill $(docker ps -q)
If the build fails for some reason you can access the container through the bash shell using to investigate what went wrong:
docker run -t -i $USER/rsm-msba /bin/bash
To remove an existing image use:
docker rmi --force $USER/rsm-msba
To remove stop all running containers, remove unused images, and errand docker processes use the dclean.sh
script
./dclean.sh
Check the disk space used by docker images
docker ps -s
docker system df
On mac you can use the commands below to push your custom image to docker hub:
sudo docker login
docker push $USER/rsm-msba
Add the following to .Rprofile in your home directory
options(radiant.ace_vim.keys = FALSE)
options(radiant.maxRequestSize = -1)
# options(radiant.maxRequestSize = 10 * 1024^2)
options(radiant.report = TRUE)
# options(radiant.ace_theme = "cobalt")
options(radiant.ace_theme = "tomorrow")
# options(radiant.ace_showInvisibles = TRUE)
Shiny and Shiny Server are registered trademarks of RStudio, Inc. The use of the trademarked terms Shiny and Shiny Server and the distribution of the Shiny Server through the images hosted on hub.docker.com has been granted by explicit permission of RStudio. Please review RStudio's trademark use policy and address inquiries about further distribution or other questions to [email protected].
Jupyter is distributed under the BSD 3-Clause license (Copyright (c) 2017, Project Jupyter Contributors)
Thanks to Ajar Vashisth for helping me get started with Docker and Docker Compose