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.