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We use Docker to build our respective integrated development environments (IDE) for working with dpl software.

RStudio containers are based on the Rocker R and RStudio builds. CyVerse rehosts the Rocker images in its data science workbench, the Discovery Environment.

CyVerse container builds are maintained on a GitHub Organization, and hosted on its private Harbor Registry.

JupyterLab containers are based on the Project Jupyter image stacks.


Run Docker for testing the code with Jupyter Lab or RStudio-Server.

Install the Docker Desktop for Windows or Mac OS X, or command line for Linux.

Pull pre-existing Docker images for RStudio-Server or JupyterLab:

docker pull jupyter/datascience-notebook:latest

To run RStudio-Server (authentication user: rstudio, password: set it yourself below):

$ git clone
$ cd dplPy
$ docker run -it --rm -p 8787:8787 -e PASSWORD=new_password -v $PWD:/home/rstudio/dplPy -e REDIRECT_URL=http://localhost:8787 rocker/geospatial:latest

Open your browser and navigate to http://localhost:8787

If you're running remotely, use the DNS of the virtual machine service with the :8787 or allow it to open the tab for you, e.g., with CodeSpaces or GitPod.

From the R Console:

> install.packages("dplR", dependencies=TRUE)

The Project Jupyter DataScience Notebook runs both Python and R:

$ git clone
$ cd dplPy
$ docker run -it --rm -p 8888:8888 -v $PWD:/home/jovyan/dplPy -e REDIRECT_URL=http://localhost:8888 jupyter/datascience-notebook:latest jupyter lab --no-browser --NotebookApp.token=''

Open your browser and navigate to http://localhost:8888

  • Note: that we're disabling the Notebook Token so you don't have to authenticate; remove --NotebookApp.token='' to re-enable.

Last update: 2023-12-04