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title: "Managing an R Package's Python Dependencies"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Managing an R Package's Python Dependencies}
```{r, include = FALSE}
collapse = TRUE,
comment = "#>"
If you're writing an R package that uses `reticulate` as an interface to a
Python session, you likely also need to install one or more Python packages on
the user's machine for your package to function. In addition, you'd likely
prefer to insulate users from details around how Python + `reticulate` are
configured as much as possible. This vignette documents a few approaches for
accomplishing these goals.
## Manual Configuration
Previously, packages like [tensorflow](
accomplished this by providing helper functions (e.g.
`tensorflow::install_tensorflow()`), and documenting that users should call this
function to prepare the environment. For example:
# use tensorflow
The biggest downside with this approach is that it requires users to manually
download and install an appropriate version of Python. In addition, if the user
has _not_ downloaded an appropriate version of Python, then the version
discovered on the user's system may not conform with the requirements imposed by
the `tensorflow` package -- leading to more trouble.
Fixing this often requires instructing the user to install Python, and then use
`reticulate` APIs (e.g. `reticulate::use_python()` and other tools) to find and
use an appropriate Python version + environment. This is, understandably, more
cognitive overhead than you might want to impose on users of your package.
Another huge problem with manual configuration is that if different R packages
use different default Python environments, then those packages can't ever be
loaded in the same R session (since there can only be one active Python
environment at a time within an R session).
## Automatic Configuration
With newer versions of `reticulate`, it's possible for client packages to
declare their Python dependencies directly in the `DESCRIPTION` file, with the
use of the `Config/reticulate` field.
With automatic configuration, `reticulate` wants to encourage a world wherein
different R packages wrapping Python packages can live together in the same
Python environment / R session. In essence, we would like to minimize the number
of conflicts that could arise through different R packages having incompatible
Python dependencies.
### Using Config/reticulate
For example, if we had a package `rscipy` that acted as an interface to the
[SciPy]( Python package, we might use the following
Package: rscipy
Title: An R Interface to scipy
Version: 1.0.0
Description: Provides an R interface to the Python package scipy.
packages = list(
list(package = "scipy")
< ... other fields ... >
### Installation
With this, `reticulate` will take care of automatically configuring a Python
environment for the user when the `rscipy` package is loaded and used (i.e. it's
no longer necessary to provide the user with a special `install_tensorflow()`
type function).
Specifically, after the `rscipy` package is loaded, the following will occur:
1. Unless the user has explicitly instructed `reticulate` to use an existing
Python environment, `reticulate` will prompt the user to download and install
[Miniconda]( (if necessary).
2. After this, when the Python session is initialized by `reticulate`, all
declared dependencies of loaded packages in `Config/reticulate` will be
3. These dependencies will then be installed into an appropriate Conda
environment, as provided by the Miniconda installation.
In this case, the end user workflow will be exactly as with an R package that
has no Python dependencies:
# use the package
If the user has no compatible version of Python available on their system, they
will be prompted to install Miniconda. If they do have Python already, then the
required Python packages (in this case `scipy`) will be installed in the
standard shared environment for R sessions (typically a virtual environment, or
a Conda environment named "r-reticulate").
In effect, users have to pay a one-time, mostly-automated initialization cost in
order to use your package, and then things will then work as any other R package
would. In particular, users are otherwise insulated from details as to how
`reticulate` works.
### .onLoad Configuration
In some cases, a user may try to load your package after Python has already been
initialized. To ensure that `reticulate` can still configure the active Python
environment, you can include the code:
.onLoad <- function(libname, pkgname) {
This will instruct `reticulate` to immediately try to configure the active
Python environment, installing any required Python packages as necessary.
## Versions
The goal of these mechanisms is to allow easy interoperability between R
packages that have Python dependencies, as well as to minimize specialized
version/configuration steps for end-users. To that end, `reticulate` will (by
default) track an older version of Python than the current release, giving
Python packages time to adapt as is required. Python 2 will not be supported.
Tools for breaking these rules are not yet implemented, but will be provided as
the need arises.
## Format
Declared Python package dependencies should have the following format:
- **package**: The name of the Python package.
- **version**: The version of the package that should be installed. When left
unspecified, the latest-available version will be installed. This should only
be set in exceptional cases -- for example, if the most recently-released
version of a Python package breaks compatibility with your package (or other
Python packages) in a fundamental way. If multiple R packages request
different versions of a particular Python package, `reticulate` will signal a
- **pip**: Whether this package should be retrieved from the
[PyPI]( with `pip`, or (if `FALSE`) from the Anaconda
For example, we could change the `Config/reticulate` directive from above to specify
that `scipy [1.3.0]` be installed from PyPI (with `pip`):
packages = list(
list(package = "scipy", version = "1.3.0", pip = TRUE)