How does the integration with RStudio Workbench / RStudio Server Pro and Jupyter Notebooks work?
You can configure RStudio Workbench (previously RStudio Server Pro) to spawn sessions with the RStudio IDE, Jupyter Notebooks, or JupyterLab. This allows data scientists to develop R and Python applications and code using standard editors for R (RStudio) and Python (Jupyter Notebooks and JupyterLab).
Can I use Jupyter Notebooks with Kubernetes or Slurm?
Yes, if you have a Kubernetes or Slurm cluster, you can configure RStudio Workbench and Launcher to spawn sessions with the RStudio IDE, Jupyter Notebooks, or JupyterLab. Refer to the documentation on configuring RStudio Workbench with Kubernetes or Slurm for more information.
Do I need to use Launcher with Kubernetes or Slurm to use Jupyter Notebooks?
No, the use of those resource managers with RStudio Workbench and Launcher is optional. You can configure RStudio Workbench with Jupyter Notebooks on a single server. Refer to the documentation on Configuring RStudio Workbench with Jupyter Notebooks on a Single Server.
Can I publish Jupyter Notebooks from RStudio Workbench / RStudio Server Pro?
Yes, you can publish Jupyter Notebooks to RStudio Connect using the rsconnect-jupyter notebook extension.
Can I schedule and email Jupyter Notebooks?
Yes, you can schedule and email notebooks that have been published to RStudio Connect.
Can I use JupyterLab with RStudio Workbench / RStudio Server Pro?
Yes, you can selectively enable Jupyter Notebooks and/or JupyterLab with RStudio Server Pro or RStudio Workbench.
How do I install Python and Jupyter for use with RStudio Workbench / RStudio Server Pro?
We provide documentation for installing Python, common Python packages, Jupyter Notebooks, JupyterLab, and notebook extensions in the respective documentation for a single server, with Kubernetes, or with Slurm.
Can I install additional Python packages?
Yes, after you install Python, administrators can install additional Python packages in a centralized Python environment, or users can install additional Python packages in their home directories.
Can I use multiple versions of Python?
Yes, you can install multiple versions of Python and register them as additional kernels in Jupyter. Refer to the documentation on Using Multiple Python Versions and Environments with RStudio Workbench and Jupyter Notebooks for more information.
Can I install my own notebook extensions?
Yes, you can install and customize Jupyter Notebooks after installing the basic requirements.
Can I use version control with my notebooks?
Yes, you can use version control systems such as Git with Jupyter Notebooks via the Terminal or third-party notebook extensions.
Can I use Jupyter Notebooks with R code and the R kernel?
We recommend using the RStudio IDE when developing R code and applications, and the Python kernel in Jupyter when developing Python code and applications.
Can I use other notebook kernels with Jupyter?
Yes, you can install and customize Jupyter Notebooks with other third-party notebook kernels. Note that only notebooks that use the Python kernel can be published to RStudio Connect.
Can I use Jupyter Notebooks with R code and Shiny, R Markdown, or Plumber?
The best way to combine R and Python code in Shiny apps, R Markdown reports, and Plumber REST APIs is to use the reticulate package, which can then be published to RStudio Connect. Refer to the resources on Using Python with RStudio for more information.
Are other editors supported in RStudio Workbench / RStudio Server Pro?
As of the RStudio Server Pro 1.2.5 release, supported editors include the RStudio IDE (Server Pro and Workbench), Jupyter Notebooks, and JupyterLab.