Integrating Posit Workbench with Python#
These instructions describe how to use the
reticulate R package with Posit Workbench, formerly RStudio Workbench, to interface with Python.
Once you configure Python and
reticulate with Workbench, users will be able to develop mixed R and Python content with Shiny apps, R Markdown
reports, and Plumber APIs that call out to Python code using the
What is reticulate?#
The reticulate package provides a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:
- Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.
- Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).
- Flexible binding to different versions of Python including virtual environments and Conda environments.
Reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability. If you are an R developer that uses Python for some of your work or a member of data science team that uses both languages, reticulate can dramatically streamline your workflow!
The RStudio IDE uses the reticulate R package to interface with Python, and so the Python integration requires:
- Installation of Python 3.5, or newer
- The reticulate R package (1.20, or newer; as available from CRAN)
Step 1. Install Python for all users#
- First, follow the Install Python instructions on the server with Workbench in a central location for all users (e.g.,
- Once you have completed the steps to Install Python, continue to Step 2.
Step 2. Install
reticulate for all users#
reticulateR package for all users in the global R library.
For example, if R is installed in
/opt/R/3.9.14/, then you you can use the following command:Terminal
$ sudo /opt/R/3.9.14/bin/Rscript -e 'install.packages("reticulate")'
Step 3. Configure
reticulate with Python for all users#
RETICULATE_PYTHONenvironment variable for all Workbench users by putting the following line in the R session-specific profile script used by Workbench.
For example, if Python is installed in
/opt/python/3.9.14/, then you you can use the following configuration:File: /etc/rstudio/rsession-profile
Now, you are ready to develop Shiny apps, R Markdown, and Plumber APIs with Python/R in the RStudio IDE and Workbench using the
reticulate package per https://blog.rstudio.com/2018/10/09/rstudio-1-2-preview-reticulated-python/ and https://rstudio.github.io/reticulate/ and deploy the applications to Connect.
For more details on each step, refer to the concepts and best practices in the support article for Best Practices for Using Python with Connect.