The R {arrow} package provides access to many of the features of the Apache Arrow C++ library for R users. The goal of arrow is to provide an Arrow C++ backend to {dplyr}, and access to the Arrow C++ library through familiar base R and tidyverse functions, or {R6} classes.

To learn more about the Apache Arrow project, see the parent documentation of the Arrow Project. The Arrow project provides functionality for a wide range of data analysis tasks to store, process and move data fast. See the read/write article to learn about reading and writing data files, data wrangling to learn how to use dplyr syntax with arrow objects, and the function documentation for a full list of supported functions within dplyr queries.


The latest release of arrow can be installed from CRAN. In most cases installing the latest release should work without requiring any additional system dependencies, especially if you are using Windows or macOS.


Alternatively, if you are using conda you can install arrow from conda-forge:

conda install -c conda-forge --strict-channel-priority r-arrow

There are some special cases to note:

What can the arrow package do?

The Arrow C++ library is comprised of different parts, each of which serves a specific purpose. The arrow package provides binding to the C++ functionality for a wide range of data analysis tasks.

It allows users to read and write data in a variety formats:

It provides access to remote filesystems and servers:

Additional features include:

What is Apache Arrow?

Apache Arrow is a cross-language development platform for in-memory and larger-than-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. It also provides computational libraries and zero-copy streaming, messaging, and interprocess communication.

This package exposes an interface to the Arrow C++ library, enabling access to many of its features in R. It provides low-level access to the Arrow C++ library API and higher-level access through a dplyr backend and familiar R functions.

Arrow resources

There are a few additional resources that you may find useful for getting started with arrow:

Getting help

We welcome questions, discussion, and contributions from users of the arrow package. For information about mailing lists and other venues for engaging with the Arrow developer and user communities, please see the Apache Arrow Community page.

If you encounter a bug, please file an issue with a minimal reproducible example on GitHub issues. Log in to your GitHub account, click on New issue and select the type of issue you want to create. Add a meaningful title prefixed with [R] followed by a space, the issue summary and select component R from the dropdown list. For more information, see the Report bugs and propose features section of the Contributing to Apache Arrow page in the Arrow developer documentation.

Code of Conduct

Please note that all participation in the Apache Arrow project is governed by the Apache Software Foundation’s code of conduct.