---
title: "Using reticulate in an R Package"
output:
  rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Using reticulate in an R Package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(eval = FALSE)
```

## Delay Loading Python Modules

If you write an R package that wraps one or more Python packages, it's likely that you'll be importing Python modules within the `.onLoad` method of your package so that you can have convenient access to them within the rest of the package source code.

When you do this, you should use the `delay_load` flag to the `import()` function, for example:

``` r
# global reference to scipy (will be initialized in .onLoad)
scipy <- NULL

.onLoad <- function(libname, pkgname) {
  # use superassignment to update global reference to scipy
  scipy <<- reticulate::import("scipy", delay_load = TRUE)
}
```

Using the `delay_load` flag has two important benefits:

1.  It allows you to successfully load your package even when Python / Python packages are not installed on the target system (this is particularly important when testing on CRAN build machines).

2.  It allows users to specify a desired location for Python before interacting with your package. For example:

    ``` r
    library(mypackage)
    reticulate::use_virtualenv("~/pythonenvs/userenv")
    # call functions from mypackage
    ```

Without the `delay_load`, Python would be loaded immediately and the user's call to `use_virtualenv` would have no effect.

## Installing Python Dependencies

Your R package likely depends on the installation of one or more Python packages. As a convenience to your users, you may want to provide a high-level R function to allow users to install these Python packages. By default, the Python packages should be installed in an isolated virtual environment, but it's beneficial if users could easily configure multiple R packages to depend on a common Python environment (so that they can be easily used together).

The `py_install()` function provides a high-level interface for installing one or more Python packages. The packages will by default be installed within the currently active virtual environment or conda environment, (which is, by default, an environent named "r-reticulate"). For example:

``` r
library(reticulate)
py_install("scipy")
```

You can document the use of this function along with your package, or alternatively you can provide a wrapper function for `py_install()` that defaults to installing in a Python environment created specifically for your R package. For example:

```{r, eval = FALSE}
install_scipy <- function(envname = "r-scipy", method = "auto", ...) {
  reticulate::py_install("scipy", envname = envname, 
                         method = method, ...)
}
```

For a fuller discussion of how to use reticulate in an R package, include alternative approaches, and how to create a "pit of success" for R users with regards to managing Python installations, see the guide on [Python Dependencies](python_dependencies.html)

## Checking and Testing on CRAN

If you use **reticulate** in another R package you need to account for the fact that when your package is submitted to CRAN, the CRAN test servers may not have Python, NumPy, or whatever other Python modules you are wrapping in your package. If you don't do this then your package may fail to load and/or pass its tests when run on CRAN.

There are two things you should do to ensure your package is well behaved on CRAN:

1.  Use the `delay_load` option (as described above) to ensure that the module (and Python) is loaded only on its first use. For example:

    ```{r}
    # python 'scipy' module I want to use in my package
    scipy <- NULL

    .onLoad <- function(libname, pkgname) {
      # delay load foo module (will only be loaded when accessed via $)
      scipy <<- import("scipy", delay_load = TRUE)
    }
    ```

2.  When writing tests, check to see if your module is available and if it isn't then skip the test. For example, if you are using the **testthat** package, you might do this:

    ```{r}
    # helper function to skip tests if we don't have the 'foo' module
    skip_if_no_scipy <- function() {
      have_scipy <- py_module_available("scipy")
      if (!have_scipy)
        skip("scipy not available for testing")
    }

    # then call this function from all of your tests
    test_that("Things work as expected", {
      skip_if_no_scipy()
      # test code here...
    })
    ```

## Implementing S3 Methods

Python objects exposed by **reticulate** carry their Python classes into R, so it's possible to write S3 methods to customize e.g. the `str` or `print` behavior for a given class (note that it's not typically necessary that you do this since the default `str` and `print` methods call `PyObject_Str`, which typically provides an acceptable default behavior).

If you do decide to implement custom S3 methods for a Python class it's important to keep in mind that when an R session ends the connection to Python objects is lost, so when the .RData saved from one R session is restored in a subsequent R session the Python objects are effectively lost (technically they become `NULL` R `externalptr` objects).

By default when you attempt to interact with a Python object from a previous session (a `NULL` R `externalptr`) an error is thrown. If you want to do something more customized in your S3 method you can use the `py_is_null_xptr()` function. For example:

```{r eval=FALSE}
method.MyModule.MyPythonClass <- function(x, y, ...) {
  if (py_is_null_xptr(x))
    # whatever is appropriate
  else
    # interact with the object
}
```

Note that this check isn't required, as by default an R error will occur. If it's desirable to avoid this error for any reason then you can use `py_is_null_xptr()` to do so.

The **reticulate** package exports a `py_str` generic method which is called from the `str` method only after doing appropriate validation (if the object is NULL then `<pointer: 0x0>` is returned). You can implement the `py_str` method as follows:

```{r eval=FALSE}
#' @importFrom reticulate py_str
#' @export
py_str.MyModule.MyPythonClass <- function(object, ...) {
  # interact with the object to generate the string
}
```

The `print` and `summary` methods for Python objects both call the `str` method by default, so if you implement `py_str()` you will automatically inherit implementations for those methods.

### Converting between R and Python

**reticulate** provides the generics `r_to_py()` for converting R objects into Python objects, and `py_to_r()` for converting Python objects back into R objects. Package authors can provide methods for these generics to convert Python and R objects otherwise not handled by **reticulate**.

**reticulate** provides conversion operators for some of the most commonly used Python objects, including:

-   Built-in Python objects (lists, dictionaries, numbers, strings, tuples)
-   NumPy arrays,
-   Pandas objects (`Index`, `Series`, `DataFrame`),
-   Python `datetime` objects.

If you see that **reticulate** is missing support for conversion of one or more objects from these packages, please [let us know](https://github.com/rstudio/reticulate/issues) and we'll try to implement the missing converter. For Python packages not in this set, you can provide conversion operators in your own extension package.

### Writing your own `r_to_py()` methods

`r_to_py()` accepts a `convert` argument, which controls how objects generated from the created Python object are converted. To illustrate, consider the difference between these two cases:

```{r}
library(reticulate)

# [convert = TRUE] => convert Python objects to R when appropriate
sys <- import("sys", convert = TRUE)
class(sys$path)
# [1] "character"

# [convert = FALSE] => always return Python objects
sys <- import("sys", convert = FALSE)
class(sys$path)
# [1] "python.builtin.list" "python.builtin.object"
```

This is accomplished through the use of a `convert` flag, which is set on the Python object wrappers used by `reticulate`. Therefore, if you're writing a method `r_to_py.foo()` for an object of class `foo`, you should take care to preserve the `convert` flag on the generated object. This is typically done by:

1.  Passing `convert` along to the appropriate lower-level `r_to_py()` method;

2.  Explicitly setting the `convert` attribute on the returned Python object.

As an example of the second:

```{r eval=FALSE}
# suppose 'make_python_object()' creates a Python object
# from R objects of class 'my_r_object'.
r_to_py.my_r_object <- function(x, convert) {
  object <- make_python_object(x)
  assign("convert", convert, envir = object)
  object
}
```

## Using Github Actions

[Github Actions](https://github.com/features/actions) are commonly used for continuous integration and testing of R packages. Making it work with **reticulate** is pretty simple - all you need to do is ensure that there is a valid Python installation on the runner, and that reticulate knows to use it. You can do this all with shell commands, or you can use functions in reticulate to do this. Here is an example sequence of `steps` demonstrating how you can do this with reticulate functions:

``` yaml

      - uses: r-lib/actions/setup-r@v2
        with:
          r-version: release

      - uses: r-lib/actions/setup-r-dependencies@v2
        with:
          extra-packages: rcmdcheck reticulate

      - uses: actions/setup-python@v4
        with:
          python-version: "3.x"

      - name: setup r-reticulate venv
        shell: Rscript {0}
        run: |

          path_to_python <- reticulate::virtualenv_create(
            envname = "r-reticulate",
            python = Sys.which("python"), # placed on PATH by the setup-python action
            packages = c(
              "numpy", "any-other-python-packages-you-want-go-here"
            )
          )

          writeLines(sprintf("RETICULATE_PYTHON=%s", path_to_python),
                     Sys.getenv("GITHUB_ENV"))

      - uses: r-lib/actions/check-r-package@v2
```
