You can read more about VS Code support for virtual environments here. Note that this applies only to venv not Conda environments (which have a separate mechanism for binding to the current VS Code session). If you create a virtual environment in the env/ directory as described above, Visual Studio Code should automatically discover that environment when you load a workspace from the environment’s parent directory. If you need to install Python packages, simply use pip or conda within the terminal as described above. Just be sure to create an RStudio project within the same directory where you created your env and things will work as expected with no additional configuration. RStudio will automatically activate any venv or condaenv that it finds within a project directory. If you need to install R packages, use install.packages if you need to install Python packages, simply use pip or conda within the terminal as described above. If you are using renv, RStudio will automatically do the right thing in terms of binding Quarto to the R and/or Python packages in your project-local environments. If you are using Quarto within RStudio it is strongly recommended that you use the current release of RStudio from (the documentation below assumes you are using this build). The workflow is similar if you are using conda environments.

For example: ShellĪll of the Python packages installed within the env will be available in your Jupyter notebook session. To use Jupyter or JupyterLab within a Python virtual environment you just need to activate the environment and then launch the Jupyter front end. To create a new environment in the directory env: See this article on managing project specific environments with Conda for additional details. This section will cover the basics of creating and using conda environments with Quarto projects. Python3 -m pip install -r requirements.txt Then, install packages from requirements.txt: Platform To reproduce the environment on another machine you create an empty environment, activate it, and then pip install using requirements.txt:įirst, follow the instructions above for creating and activating a virtual environment for your platform/shell. You should generally check the requirements.txt file into version control.

To do this use the pip freeze command: Platform To make your environment reproducible, you need to create a requirements.txt file that enumerates all of the packages in use. This differs slightly depending on which platform / shell you are using: Shell To use the environment you need to activate it.

To create a new Python 3 virtual environment in the directory env: Platform See the full documentation on using virtual environments with Python for additional details. Here we’ll provide a brief run through of creating a venv for a Quarto project. We’ll also cover using virtual environments with JupyterLab, RStudio, and VS Code. In these examples we’ll assume that you are already within a project directory that contains Quarto documents (so the environment will be created as a sub-directory of the project). Renv (package for managing R environments)īelow we’ll provide some example workflows for using these tools with Quarto. There are several popular flavors of virtual environment, we will cover the following ones here: This both helps you to faithfully reproduce your environment (e.g. if you are collaborating with a colleague or deploying to a server) as well as isolate the use of packages so that upgrading a package in one project doesn’t break other projects. Virtual environments provide a project-specific version of installed packages.

Anaconda virtual environment