The following steps represent a minimal workflow for using Python with RStudio Connect via the reticulate package, whether you are using the RStudio IDE on your local machine or RStudio Workbench (previously RStudio Server Pro).
Step 1) Install a base version of Python
If you are working on a server with RStudio Workbench (previously RStudio Server Pro), your administrator can install a system-wide version of Python, or you can install Python in your home directory from Python.org or Anaconda.
Be sure to start a new terminal session to ensure your newly installed Python is active.
Also, ensure that your installation of Python has the
virtualenv package installed by running:
pip install virtualenv
Step 2) Create a Python environment in your project
It is recommended that you use one virtual environment per project, similar to how packrat is used to manage R packages within a project.
Navigate into your RStudio project directory by using the following command:
Create a new virtual environment in a folder called
my_env within your project directory using the following command:
Step 3) Activate your Python environment
You can activate the
virtualenv in your project using the following command in a terminal:
You can verify that you have activated the correct version of Python using the following command in a terminal:
Step 4) Install Python packages in your environment
You can install Python packages such as
matplotlib, and other packages in your Python
virtualenv by using
pip install using the following command in a terminal:
pip install numpy pandas matplotlib
Step 5) Install and configure
reticulate to use your Python version
reticulate package using the following command in your R console:
reticulate to point to the Python executable in your
virtualenv, create a file in your project directory called
.Rprofile with the following contents:
Sys.setenv(RETICULATE_PYTHON = "my_env/bin/python")
You'll need to restart your R session for the setting to take effect. You can verify that
reticulate is configured for the correct version of Python using the following command in your R console:
Step 6) Publish a project to RStudio Connect
You can then develop Shiny apps, R Markdown, and Plumber APIs with Python/R in the RStudio IDE and RStudio 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 RStudio 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 RStudio Connect.