Apache Arrow R Package On CRAN

Published 08 Aug 2019
By Neal Richardson (npr)

We are very excited to announce that the arrow R package is now available on CRAN.

Apache Arrow is a cross-language development platform for in-memory data that specifies a standardized columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. The arrow package provides an R interface to the Arrow C++ library, including support for working with Parquet and Feather files, as well as lower-level access to Arrow memory and messages.

You can install the package from CRAN with


On macOS and Windows, installing a binary package from CRAN will generally handle Arrow’s C++ dependencies for you. However, the macOS CRAN binaries are unfortunately incomplete for this version, so to install 0.14.1, you’ll first need to use Homebrew to get the Arrow C++ library (brew install apache-arrow), and then from R you can install.packages("arrow", type = "source").

Windows binaries are not yet available on CRAN but should be published soon.

On Linux, you’ll need to first install the C++ library. See the Arrow project installation page to find pre-compiled binary packages for some common Linux distributions, including Debian, Ubuntu, and CentOS. You’ll need to install libparquet-dev on Debian and Ubuntu, or parquet-devel on CentOS. This will also automatically install the Arrow C++ library as a dependency. Other Linux distributions must install the C++ library from source.

If you install the arrow R package from source and the C++ library is not found, the R package functions will notify you that Arrow is not available. Call


for version- and platform-specific guidance on installing the Arrow C++ library.

Parquet files

This package introduces basic read and write support for the Apache Parquet columnar data file format. Prior to its availability, options for accessing Parquet data in R were limited; the most common recommendation was to use Apache Spark. The arrow package greatly simplifies this access and lets you go from a Parquet file to a data.frame and back easily, without having to set up a database.

df <- read_parquet("path/to/file.parquet")

This function, along with the other readers in the package, takes an optional col_select argument, inspired by the vroom package. This argument lets you use the “tidyselect” helper functions, as you can do in dplyr::select(), to specify that you only want to keep certain columns. By narrowing your selection at read time, you can load a data.frame with less memory overhead.

For example, suppose you had written the iris dataset to Parquet. You could read a data.frame with only the columns c("Sepal.Length", "Sepal.Width") by doing

df <- read_parquet("iris.parquet", col_select = starts_with("Sepal"))

Just as you can read, you can write Parquet files:

write_parquet(df, "path/to/different_file.parquet")

Note that this read and write support for Parquet files in R is in its early stages of development. The Python Arrow library (pyarrow) still has much richer support for Parquet files, including working with multi-file datasets. We intend to reach feature equivalency between the R and Python packages in the future.

Feather files

This package also includes a faster and more robust implementation of the Feather file format, providing read_feather() and write_feather(). Feather was one of the initial applications of Apache Arrow for Python and R, providing an efficient, common file format language-agnostic data frame storage, along with implementations in R and Python.

As Arrow progressed, development of Feather moved to the apache/arrow project, and for the last two years, the Python implementation of Feather has just been a wrapper around pyarrow. This meant that as Arrow progressed and bugs were fixed, the Python version of Feather got the improvements but sadly R did not.

With the arrow package, the R implementation of Feather catches up and now depends on the same underlying C++ library as the Python version does. This should result in more reliable and consistent behavior across the two languages, as well as improved performance.

We encourage all R users of feather to switch to using arrow::read_feather() and arrow::write_feather().

Note that both Feather and Parquet are columnar data formats that allow sharing data frames across R, Pandas, and other tools. When should you use Feather and when should you use Parquet? Parquet balances space-efficiency with deserialization costs, making it an ideal choice for remote storage systems like HDFS or Amazon S3. Feather is designed for fast local reads, particularly with solid-state drives, and is not intended for use with remote storage systems. Feather files can be memory-mapped and accessed as Arrow columnar data in-memory without any deserialization while Parquet files always must be decompressed and decoded. See the Arrow project FAQ for more.

Other capabilities

In addition to these readers and writers, the arrow package has wrappers for other readers in the C++ library; see ?read_csv_arrow and ?read_json_arrow. These readers are being developed to optimize for the memory layout of the Arrow columnar format and are not intended as a direct replacement for existing R CSV readers (base::read.csv, readr::read_csv, data.table::fread) that return an R data.frame.

It also provides many lower-level bindings to the C++ library, which enable you to access and manipulate Arrow objects. You can use these to build connectors to other applications and services that use Arrow. One example is Spark: the sparklyr package has support for using Arrow to move data to and from Spark, yielding significant performance gains.


In addition to the work on wiring the R package up to the Arrow Parquet C++ library, a lot of effort went into building and packaging Arrow for R users, ensuring its ease of installation across platforms. We’d like to thank the support of Jeroen Ooms, Javier Luraschi, JJ Allaire, Davis Vaughan, the CRAN team, and many others in the Apache Arrow community for helping us get to this point.