Installing the Arrow Package on Linux


In most cases, install.packages("arrow") should just work. There are things you can do to make the installation faster (see below). If, for any reason, it doesn’t work, set the environment variable ARROW_R_DEV=true, retry, and share the logs with us.

The Apache Arrow project is implemented in multiple languages, and the R package depends on the Arrow C++ library (referred to from here on as libarrow). This means that when you install arrow, you need both the R and C++ versions. If you install arrow from CRAN on a machine running Windows or MacOS, when you call install.packages("arrow"), a precompiled binary containing both the R package and libarrow will be downloaded. However, CRAN does not host R package binaries for Linux, and so you must choose from one of the alternative approaches.

This vignette outlines the recommend approaches to installing arrow on Linux, starting from the simplest and least customisable to the most complex but with more flexbility to customise your installation.

The intended audience for this document is arrow R package users on Linux, and not Arrow developers. If you’re contributing to the Arrow project, see vignette("developing", package = "arrow") for resources to help you on set up your development environment. You can also find a more detailed discussion of the code run during the installation process in the developers’ installation docs

Having trouble installing arrow? See the “Troubleshooting” section below.

System dependencies

The arrow package is designed to work with very minimal system requirements, but there are a few things to note.


As of version 10.0.0, arrow requires a C++17 compiler to build. For gcc, this generally means version 7 or newer. Most contemporary Linux distributions have a new enough compiler; however, CentOS 7 is a notable exception, as it ships with gcc 4.8.

If you are on CentOS 7, to build arrow you will need to install a newer devtoolset, and you’ll need to update R’s Makevars to define the CXX17 variables. This script installs devtoolset-8 and configures R to be able to use C++17:

#!/usr/bin/env bash

yum install -y centos-release-scl
yum install -y devtoolset-8
# Optional: also install cloud storage dependencies, as described below
yum install -y libcurl-devel openssl-devel

source /opt/rh/devtoolset-8/enable

if [ ! `R CMD config CXX17` ]; then
  mkdir -p ~/.R
  echo "CC = $(which gcc) -fPIC" >> ~/.R/Makevars
  echo "CXX17 = $(which g++) -fPIC" >> ~/.R/Makevars
  echo "CXX17STD = -std=c++17" >> ~/.R/Makevars
  echo "CXX17FLAGS = ${CXX11FLAGS}" >> ~/.R/Makevars

Note that the C++17 compiler is only required at build time. You don’t need to enable the devtoolset every time you load the package. What’s more, if you install a binary package from RStudio Package Manager (see method 1a below), you do not need to set up any of this. Likewise, if you R CMD INSTALL --build arrow on a CentOS machine with the newer compilers, you can take the binary package it produces and install it on any other CentOS machine without those compilers.


Optional support for reading from cloud storage–AWS S3 and Google Cloud Storage (GCS)–requires additional system dependencies:

The prebuilt binaries come with S3 and GCS support enabled, so you will need to meet these system requirements in order to use them. If you’re building everything from source, the install script will check for the presence of these dependencies and turn off S3 and GCS support in the build if the prerequisites are not met–installation will succeed but without S3 or GCS functionality. If afterwards you install the missing system requirements, you’ll need to reinstall the package in order to enable S3 and GCS support.

Installing a release version (the easy way)

Method 1 - Installation with a precompiled libarrow binary

As mentioned above, on macOS and Windows, when you run install.packages("arrow"), and install arrow from CRAN, you get an R binary package that contains a precompiled version of libarrow, though CRAN does not host binary packages for Linux. This means that the default behaviour when you run install.packages() on Linux is to retrieve the source version of the R package that has to be compiled locally, including building libarrow from source. See method 2 below for details of this.

For a faster installation, we recommend that you instead use one of the methods below for installing arrow with a precompiled libarrow binary.

Method 1a - Binary R package containing libarrow binary via RSPM/conda

Graphic showing R and C++ logo inside the package icon

If you want a quicker installation process, and by default a more fully-featured build, you could install arrow from RStudio’s public package manager, which hosts binaries for both Windows and Linux.

For example, if you are using Ubuntu 20.04 (Focal):

  HTTPUserAgent =
      "R/%s R (%s)",
      paste(getRversion(), R.version["platform"], R.version["arch"], R.version["os"])

install.packages("arrow", repos = "")

Note that the User Agent header must be specified as in the example above. Please check the RStudio Package Manager: Admin Guide for more details.

For other Linux distributions, to get the relevant URL, you can visit the RSPM site, click on ‘binary’, and select your preferred distribution.

Similarly, if you use conda to manage your R environment, you can get the latest official release of the R package including libarrow via:

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

Method 1b - R source package with libarrow binary

Graphic showing R logo in folder icon, then a plus sign, then C++ logo inside the package icon

Another way of achieving faster installation with all key features enabled is to use static libarrow binaries we host. These are used automatically on many Linux distributions (x86_64 architecture only), according to the allowlist. If your distribution isn’t in the list, you can opt-in by setting the NOT_CRAN environment variable before you call install.packages():

Sys.setenv("NOT_CRAN" = TRUE)

This installs the source version of the R package, but during the installation process will check for compatible libarrow binaries that we host and use those if available. If no binary is available or can’t be found, then this option falls back onto method 2 below (full source build), but setting the environment variable results in a more fully-featured build than default.

The libarrow binaries include support for AWS S3 and GCS, so they require the libcurl and openssl libraries installed separately, as noted above. If you don’t have these installed, the libarrow binary won’t be used, and you will fall back to the full source build (with S3 and GCS support disabled).

Users on CentOS 7 will also need to install and configure a C++17 compiler. See “System dependencies” above.

Installing a release version (the less easy way)

Method 2 - Installing an R source package and building libarrow from source

Graphic showing R inside a folder icon, then a plus sign, then C++ logo inside a folder icon

Generally, compiling and installing R packages with C++ dependencies requires either installing system packages, which you may not have privileges to do, or building the C++ dependencies separately, which introduces all sorts of additional ways for things to go wrong.

The full source build of arrow, compiling both C++ and R bindings, does handle most of the dependency management for you, but it is much slower. However, if using binaries isn’t an option for you, or you wish to fine-tune or customize your Linux installation, the instructions in this section explain how to do that.

Handling libarrow dependencies

When you build libarrow from source, its dependencies will be automatically downloaded. The environment variable ARROW_DEPENDENCY_SOURCE controls whether the libarrow installation also downloads or installs all dependencies (when set to BUNDLED), uses only system-installed dependencies (when set to SYSTEM) or checks system-installed dependencies first and only installs dependencies which aren’t already present (when set to AUTO, the default).

These dependencies vary by platform; however, if you wish to install these yourself prior to libarrow installation, we recommend that you take a look at the docker file for whichever of our CI builds (the ones ending in “cpp” are for building Arrow’s C++ libaries, aka libarrow) corresponds most closely to your setup. This will contain the most up-to-date information about dependencies and minimum versions.

If downloading dependencies at build time is not an option, as when building on a system that is disconnected or behind a firewall, there are a few options. See “Offline builds” below.

Dependencies for S3 and GCS support

Support for working with data in S3 and GCS is not enabled in the default source build, and it has additional system requirements as described above. To enable it, set the environment variable LIBARROW_MINIMAL=false or NOT_CRAN=true to choose the full-featured build, or more selectively set ARROW_S3=ON and/or ARROW_GCS=ON.

When either feature is enabled, the install script will check for the presence of the required dependencies, and if the prerequisites are met, it will turn off S3 and GCS support–installation will succeed but without S3 or GCS functionality. If afterwards you install the missing system requirements, you’ll need to reinstall the package in order to enable S3 and GCS support.

Advanced configuration for building from source

In this section, we describe how to fine-tune your installation at a more granular level.

libarrow configuration

Some features are optional when you build Arrow from source - you can configure whether these components are built via the use of environment variables. The names of the environment variables which control these features and their default values are shown below.

Name Description Default Value
ARROW_S3 S3 support (if dependencies are met)* OFF
ARROW_GCS GCS support (if dependencies are met)* OFF
ARROW_JEMALLOC The jemalloc memory allocator ON
ARROW_MIMALLOC The mimalloc memory allocator ON
ARROW_JSON The JSON parsing library ON
ARROW_WITH_RE2 The RE2 regular expression library, used in some string compute functions ON
ARROW_WITH_UTF8PROC The UTF8Proc string library, used in many other string compute functions ON
ARROW_WITH_BROTLI Compression algorithm ON
ARROW_WITH_BZ2 Compression algorithm ON
ARROW_WITH_LZ4 Compression algorithm ON
ARROW_WITH_SNAPPY Compression algorithm ON
ARROW_WITH_ZLIB Compression algorithm ON
ARROW_WITH_ZSTD Compression algorithm ON

R package configuration

There are a number of other variables that affect the configure script and the bundled build script. All boolean variables are case-insensitive.

Name Description Default
LIBARROW_BUILD Allow building from source true
LIBARROW_BINARY Try to install libarrow binary instead of building from source (unset)
LIBARROW_MINIMAL Build with minimal features enabled (unset)
ARROW_R_DEV More verbose messaging and regenerates some code false
ARROW_USE_PKG_CONFIG Use pkg-config to search for libarrow install true
LIBARROW_DEBUG_DIR Directory to save source build logs (unset)
CMAKE Alternative CMake path (unset)

See below for more in-depth explanations of these environment variables.

  • LIBARROW_BINARY : By default on many distributions, or if explicitly set to true, the script will determine whether there is a prebuilt libarrow that will work with your system. You can set it to false to skip this option altogether, or you can specify a string “distro-version” that corresponds to a binary that is available, to override what this function may discover by default. Possible values are: “centos-7”, “ubuntu-18.04” (both with gcc 8, and openssl 1), “ubuntu-22.04” (openssl 3).
  • LIBARROW_BUILD : If set to false, the build script will not attempt to build the C++ from source. This means you will only get a working arrow R package if a prebuilt binary is found. Use this if you want to avoid compiling the C++ library, which may be slow and resource-intensive, and ensure that you only use a prebuilt binary.
  • LIBARROW_MINIMAL : If set to false, the build script will enable some optional features, including S3 support and additional alternative memory allocators. This will increase the source build time but results in a more fully functional library. If set to true turns off Parquet, Datasets, compression libraries, and other optional features. This is not commonly used but may be helpful if needing to compile on a platform that does not support these features, e.g. Solaris.
  • NOT_CRAN : If this variable is set to true, as the devtools package does, the build script will set LIBARROW_BINARY=true and LIBARROW_MINIMAL=false unless those environment variables are already set. This provides for a more complete and fast installation experience for users who already have NOT_CRAN=true as part of their workflow, without requiring additional environment variables to be set.
  • ARROW_R_DEV : If set to true, more verbose messaging will be printed in the build script. arrow::install_arrow(verbose = TRUE) sets this. This variable also is needed if you’re modifying C++ code in the package: see the developer guide vignette.
  • ARROW_USE_PKG_CONFIG: If set to false, the configure script won’t look for Arrow libraries on your system and instead will look to download/build them. Use this if you have a version mismatch between installed system libraries and the version of the R package you’re installing.
  • LIBARROW_DEBUG_DIR : If the C++ library building from source fails (cmake), there may be messages telling you to check some log file in the build directory. However, when the library is built during R package installation, that location is in a temp directory that is already deleted. To capture those logs, set this variable to an absolute (not relative) path and the log files will be copied there. The directory will be created if it does not exist.
  • CMAKE : When building the C++ library from source, you can specify a /path/to/cmake to use a different version than whatever is found on the $PATH.

Install the nightly build

Daily development builds, which are not official releases, can be installed from the Ursa Labs repository:

Sys.setenv(NOT_CRAN = TRUE)
install.packages("arrow", repos = c(arrow = "", getOption("repos")))

or for conda users via:

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

Install from git repo

You can also install the R package from a git checkout:

git clone
cd arrow/r

If you don’t already have libarrow on your system, when installing the R package from source, it will also download and build libarrow for you. See the section above on build environment variables for options for configuring the build source and enabled features.

Installation using install_arrow()

The previous instructions are useful for a fresh arrow installation, but arrow provides the function install_arrow(), which you can use if you:

install_arrow() provides some convenience wrappers around the various environment variables described below.

Although this function is part of the arrow package, it is also available as a standalone script, so you can access it for convenience without first installing the package:


Install the latest release


Install the nightly build

install_arrow(nightly = TRUE)

Install with more verbose output for debugging errors

install_arrow(verbose = TRUE)

install_arrow() does not require environment variables to be set in order to satisfy C++ dependencies.

Note that, unlike packages like tensorflow, blogdown, and others that require external dependencies, you do not need to run install_arrow() after a successful arrow installation.

Offline installation

The install-arrow.R file also includes the create_package_with_all_dependencies() function. Normally, when installing on a computer with internet access, the build process will download third-party dependencies as needed. This function provides a way to download them in advance.

Doing so may be useful when installing Arrow on a computer without internet access. Note that Arrow can be installed on a computer without internet access without doing this, but many useful features will be disabled, as they depend on third-party components. More precisely, arrow::arrow_info()$capabilities() will be FALSE for every capability. One approach to add more capabilities in an offline install is to prepare a package with pre-downloaded dependencies. The create_package_with_all_dependencies() function does this preparation.

If you’re using binary packages you shouldn’t need to follow these steps. You should download the appropriate binary from your package repository, transfer that to the offline computer, and install that. Any OS can create the source bundle, but it cannot be installed on Windows. (Instead, use a standard Windows binary package.)

Note if you’re using RStudio Package Manager on Linux: If you still want to make a source bundle with this function, make sure to set the first repo in options("repos") to be a mirror that contains source packages (that is: something other than the RSPM binary mirror URLs).

Step 1 - Using a computer with internet access, pre-download the dependencies:

Step 2 - On the computer without internet access, install the prepared package:

Alternative, hands-on approach

  • Download the dependency files (cpp/thirdparty/ may be helpful)
  • Copy the directory of dependencies to the offline computer
  • Create the environment variable ARROW_THIRDPARTY_DEPENDENCY_DIR on the offline computer, pointing to the copied directory.
  • Install the arrow package as usual.


The intent is that install.packages("arrow") will just work and handle all C++ dependencies, but depending on your system, you may have better results if you tune one of several parameters. Here are some known complications and ways to address them.

Package failed to build C++ dependencies

If you see a message like

------------------------- NOTE ---------------------------
There was an issue preparing the Arrow C++ libraries.

in the output when the package fails to install, that means that installation failed to retrieve or build the libarrow version compatible with the current version of the R package.

Please check the “Known installation issues” below to see if any apply, and if none apply, set the environment variable ARROW_R_DEV=TRUE for more verbose output and try installing again. Then, please report an issue and include the full installation output.

Using system libraries

If a system library or other installed Arrow is found but it doesn’t match the R package version (for example, you have libarrow 1.0.0 on your system and are installing R package 2.0.0), it is likely that the R bindings will fail to compile. Because the Apache Arrow project is under active development, it is essential that versions of libarrow and the R package matches. When install.packages("arrow") has to download libarrow, the install script ensures that you fetch the libarrow version that corresponds to your R package version. However, if you are using a version of libarrow already on your system, version match isn’t guaranteed.

To fix version mismatch, you can either update your libarrow system packages to match the R package version, or set the environment variable ARROW_USE_PKG_CONFIG=FALSE to tell the configure script not to look for system version of libarrow. (The latter is the default of install_arrow().) System libarrow versions are available corresponding to all CRAN releases but not for nightly or dev versions, so depending on the R package version you’re installing, system libarrow version may not be an option.

Note also that once you have a working R package installation based on system (shared) libraries, if you update your system libarrow installation, you’ll need to reinstall the R package to match its version. Similarly, if you’re using libarrow system libraries, running update.packages() after a new release of the arrow package will likely fail unless you first update the libarrow system packages.

Using prebuilt binaries

If the R package finds and downloads a prebuilt binary of libarrow, but then the arrow package can’t be loaded, perhaps with “undefined symbols” errors, please report an issue. This is likely a compiler mismatch and may be resolvable by setting some environment variables to instruct R to compile the packages to match libarrow.

A workaround would be to set the environment variable LIBARROW_BINARY=FALSE and retry installation: this value instructs the package to build libarrow from source instead of downloading the prebuilt binary. That should guarantee that the compiler settings match.

If a prebuilt libarrow binary wasn’t found for your operating system but you think it should have been, please report an issue and share the console output. You may also set the environment variable ARROW_R_DEV=TRUE for additional debug messages.

Building libarrow from source

If building libarrow from source fails, check the error message. (If you don’t see an error message, only the ----- NOTE -----, set the environment variable ARROW_R_DEV=TRUE to increase verbosity and retry installation.) The install script should work everywhere, so if libarrow fails to compile, please report an issue so that we can improve the script.

Known installation issues


As mentioned above, please report an issue if you encounter ways to improve this. If you find that your Linux distribution or version is not supported, we welcome the contribution of Docker images (hosted on Docker Hub) that we can use in our continuous integration. These Docker images should be minimal, containing only R and the dependencies it requires. (For reference, see the images that R-hub uses.)

You can test the arrow R package installation using the docker-compose setup included in the apache/arrow git repository. For example,

R_ORG=rhub R_IMAGE=ubuntu-gcc-release R_TAG=latest docker-compose build r
R_ORG=rhub R_IMAGE=ubuntu-gcc-release R_TAG=latest docker-compose run r

installs the arrow R package, including libarrow, on the rhub/ubuntu-gcc-release image.