If you’re using renv
with an R project that also depends
on some Python packages (say, through the reticulate package),
then you may find renv
’s Python integration useful.
Python integration can be activated on a project-by-project basis.
Use renv::use_python()
to tell renv
to create
and use a project-local Python environment with your project. If the
reticulate
package is installed and active, then
renv
will use the same version of Python that
reticulate
normally would when generating the virtual
environment. Alternatively, you can set the
RETICULATE_PYTHON
environment variable to instruct
renv
to use a different version of Python.
If you’d rather tell renv
to use an existing Python
virtual environment, you can do so by passing the path of that virtual
environment instead – use
renv::use_python(python = "/path/to/python")
and
renv
will record and use that Python interpreter with your
project. This can also be used with pre-existing virtual environments
and Conda environments.
Once Python integration is active, renv
will attempt to
manage the state of your Python virtual environment when
snapshot()
/ restore()
is called. With this,
projects that use renv
and Python can ensure that Python
dependencies are tracked in addition to R package dependencies. Note
that future restores will require both renv.lock
(for R
package dependencies) and requirements.txt
(for Python
package dependencies).
When using virtual environments, the following extensions are provided:
renv::snapshot()
calls
pip freeze > requirements.txt
to save the set of
installed Python packages;
renv::restore()
calls
pip install -r requirements.txt
to install the
previously-recorded set of Python packages.
When using Conda environments, the following extensions are provided:
renv::snapshot()
calls
conda env export > environment.yml
to save the set of
installed Python packages;
renv::restore()
calls
conda env [create/update] --file environment.yml
to install
the previously-recorded set of Python packages.