Managing an R Package’s Python Dependencies

If you’re writing an R package that uses reticulate as an interface to a Python session, you likely also need one or more Python packages installed on the user’s machine for your package to work properly. In addition, you’d likely prefer to spare users as much as possible from details around how Python + reticulate are configured. This vignette documents a few approaches for accomplishing these goals.

Creating a “Pit of Success”

Overall, the goal of an R package author using reticulate is to create a default experience that works reliably and doesn’t require users to intervene or to have a sophisticated understanding of Python installation management. At the same time, it should also be easy to adjust the default behavior. There are two key questions to keep in mind:

Packages like tensorflow approach this task by providing a helper function, tensorflow::install_tensorflow(), and documenting that users can call this function to prepare the environment. For example:

# use tensorflow

As a best practice, an R package’s Python dependencies should default to installing in an isolated virtual environment specifically designated for the R package. This minimizes the risk of inadvertently disrupting another Python installation on the user’s system.

As an example, install_tensorflow() takes an argument envname with a default value of "r-tensorflow". This default value ensures that install_tensorflow() will install into an environment named "r-tensorflow", optionally creating it as needed.

The counterpart to the default behavior of install_tensorflow() is the work that happens in tensorflow::.onLoad(), where the R package expresses a preference, on behalf of the user, to use the r-tensorflow environment if it exists. Inside the package, these two parts work together to create a “pit of success”:

install_tensorflow <- function(..., envname = "r-tensorflow") {
  reticulate::py_install("tensorflow", envname = envname, ...)

.onLoad <- function(...) {
  use_virtualenv("r-tensorflow", required = FALSE)

The R package:

With this setup, the default experience is for the user to call install_tensorflow() once (creating a “r-tensorflow” environment). Subsequently, calls to library(tensorflow) will cause reticulate to use the r-tensorflow environment, and for everything to “just work”. The risk of disrupting another Python environment, or of this one being disrupting, is minimal, since the environment is designated for the R package. At the same time, if the environment is disrupted at some time later (perhaps because something with conflicting Python dependencies was manually installed), the user can easily revert to a working state by calling install_tensorflow().

Python environments can occasionally get into a broken state when conflicting package versions are installed, and the most reliable way to get back to a working state is to delete the environment and start over with a fresh one. For this reason, install_tensorflow() removes any pre-existing “r-tensorflow” Python environments first. Deleting a Python environment however is not something to be done lightly, so the default is to only delete the default “r-tensorflow” environment. Here is an example of the helper install_tensorflow() with the “reset” behavior.

#' @importFrom reticulate py_install virtualenv_exists virtualenv_remove
install_tensorflow <-
           envname = "r-tensorflow",
           new_env = identical(envname, "r-tensorflow")) {

  if(new_env && virtualenv_exists(envname))

  py_install(packages = "tensorflow", envname = envname, ...)

Managing Multiple Package Dependencies

One drawback of the isolated-package-environments approach is that if multiple R packages using reticulate are in use, then those packages won’t all be able to use their preferred Python environment in the same R session (since there can only be one active Python environment at a time within an R session). To resolve this, users will have to take a slightly more active role in managing their Python environments. However, this can be as simple as supplying a unique environment name.

The most straightforward approach is for users to create a dedicated Python environment for a specific project. For example, a user can create a virtual environment in the project directory, like this:

envname <- "./venv"
tensorflow::install_tensorflow(envname = envname)
pysparklyr::install_pyspark(envname = envname)

As described in the Order of Python Discovery guide, reticulate will automatically discover and use a Python virtual environment in the current working directory like this. Alternatively, if the environment exists outside the project directory, the user could then place an .Renviron or .Rprofile file in the project directory, ensuring that reticulate will use always use the Python environment configured for that project. For example, an .Renviron file in the project directory could contain:


Or an .Rprofile file in the project directory could contain:

Sys.setenv("RETICULATE_PYTHON_ENV" = "~/my/project/venv")

This approach minimizes the risk that an existing, already working, Python environment will accidentally be broken by installing packages, due to inadvertently upgrading or downgrading other Python packages already installed in the environment.

Another approach is for users to install your R packages’ Python dependencies into another Python environment that is already on the search path. For example, users can opt-in to installing into the default r-reticulate venv:

tensorflow::install_tensorflow(envname = "r-reticulate")

Or they can install one package’s dependencies into another package’s default environment. For example, installing spark into the default "r-tensorflow" environment:

tensorflow::install_tensorflow() # creates an "r-tensorflow" env
pysparklyr::install_pyspark(envname = "r-tensorflow")

This approach—exporting an installation helper function that defaults to a particular environment, and a hint in .onLoad() to use that environment—is one way to create a “pit of success”. It encourages a default workflow that is robust and reliable, especially for users not yet familiar with the mechanics of Python installation management. At the same time, an installation helper function empowers users to manage Python environments through simply providing an environment name. It makes it easy to combine dependencies of multiple R packages, and, should anything go wrong due to conflicting Python dependencies, it also provides a straightforward way to revert to a working state at any time, by calling the helper function without arguments.

Automatic Configuration

An alternative approach to the one described above is to do automatic configuration. It’s possible for client packages to declare their Python dependencies in such a way that they are automatically installed in the currently activated Python environment. This is a maximally convenient approach; when it works it can feel a little bit magical, but it is also potentially dangerous and can result in frustration if something goes wrong. You can opt in to this behavior as a package author through your packages DESCRIPTION file, with the use of the Config/reticulate field.

With automatic configuration, reticulate envisions a world wherein different R packages wrapping Python packages can live together in the same Python environment / R session. This approach only works when the Python packages being wrapped don’t have conflicting dependencies.

You must be a judge of the Python dependencies your R package requires–if automatically bootstrapping an installation of the Python package into the user’s active Python environment, whatever it may contain, is a safe action to perform by default. For example, this is most likely a safe action for a Python package like requests, but perhaps not a safe choice for a frequently updated package with many dependencies, like torch or tensorflow (e.g., it’s not uncommon for torch and tensorflow to have conflicting version requirements for dependencies like numpy or cuda). Keep in mind that, unlike CRAN, PyPI does not perform any compatibility or consistency checks across the package repository.

Using Config/reticulate

As a package author, you can opt in to automatic configuration like this. For example, if we had a package rscipy that acted as an interface to the SciPy Python package, we might use the following DESCRIPTION file:

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 ... >


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, though it’s still recommended to do so).

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 discovered.

  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 work as any other R package would. In particular, users are otherwise spared from details about 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 following 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.


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. Python 2 will not be supported.

Tools for breaking these rules are not yet implemented, but will be provided as the need arises.


Declared Python package dependencies should have the following format:

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)