You can use Python with RStudio Workbench (previously RStudio Server Pro) to develop R applications that call Python code using the
reticulate package. You can use Python with RStudio Connect to publish Jupyter Notebooks as well as R applications that call Python code.
The best approach for using Python with RStudio Workbench and RStudio Connect is to install one or more versions of Python on the server that are independent of the system-managed version of Python.
Configuring Python with RStudio Workbench
Refer to the documentation at https://docs.rstudio.com/rsp/integration/python/ to install and configure Python with RStudio Workbench.
Configuring Python with RStudio Connect
Refer to the documentation at https://docs.rstudio.com/rsc/integration/python/ to install and configure Python with RStudio Connect.
RStudio Connect uses a service account (default
rstudio-connect) to automatically create virtual environments in Python for each application at deployment time. As a result, it is not typically necessary to manually install Python packages or create virtual environments on the RStudio Connect server.
For more information on end-user best practices and working with custom, per-project Python environments, refer to the support articles on Installing and Configuring Python with RStudio and Best Practices for using Python with RStudio Connect.