The arrow package provides reticulate methods for passing data between R and Python in the same process. This document provides a brief overview.

Why you might want to use pyarrow?

• To use some Python functionality that is not yet implemented in R, for example, the concat_arrays function.
• To transfer Python objects into R, for example, a Pandas dataframe into an R Arrow Array.

## Installing

To use arrow in Python, at a minimum you’ll need the pyarrow library. To install it in a virtualenv,

library(reticulate)
virtualenv_create("arrow-env")
install_pyarrow("arrow-env")

If you want to install a development version of pyarrow, add nightly = TRUE:

install_pyarrow("arrow-env", nightly = TRUE)

A virtualenv or a virtual environment is a specific Python installation created for one project or purpose. It is a good practice to use specific environments in Python so that updating a package doesn’t impact packages in other projects.

install_pyarrow() also works with conda environments (conda_create() instead of virtualenv_create()).

For more on installing and configuring Python, see the reticulate docs.

## Using

To start, load arrow and reticulate, and then import pyarrow.

library(arrow)
library(reticulate)
use_virtualenv("arrow-env")
pa <- import("pyarrow")

The arrow R package include support for sharing Arrow Array and RecordBatch objects in-process between R and Python. For example, let’s create an Array in pyarrow.

a <- pa$array(c(1, 2, 3)) a ## Array ## <double> ## [ ## 1, ## 2, ## 3 ## ] a is now an Array object in your R session, even though you created it in Python. You can apply R methods on it: a[a > 1] ## Array ## <double> ## [ ## 2, ## 3 ## ] You can send data both ways. One reason you might want to use pyarrow in R is to take advantage of functionality that is better supported in Python than in R. For example, pyarrow has a concat_arrays() function, but as of 0.17, this function is not implemented in the arrow R package. You can use reticulate to use it efficiently. b <- Array$create(c(5, 6, 7, 8, 9))
a_and_b <- pa$concat_arrays(list(a, b)) a_and_b ## Array ## <double> ## [ ## 1, ## 2, ## 3, ## 5, ## 6, ## 7, ## 8, ## 9 ## ] Now you have a single Array in R. ## How this works “Send”, however, isn’t the correct word. Internally, we’re passing pointers to the data between the R and Python interpreters running together in the same process, without copying anything. Nothing is being sent: we’re sharing and accessing the same internal Arrow memory buffers. ## Arrow object types For more information about Arrow object types see the “Internals” section of the “arrow” vignette: vignette("arrow", package = "arrow") ## Troubleshooting If you get an error like Error in py_get_attr_impl(x, name, silent) : AttributeError: 'pyarrow.lib.DoubleArray' object has no attribute '_export_to_c' it means that the version of pyarrow you’re using is too old. Support for passing data to and from R is included in versions 0.17 and greater. Check your pyarrow version like this: pa$__version__

## [1] "0.16.0"

Note that your pyarrow and arrow versions don’t need themselves to match: they just need to be 0.17 or greater.