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.

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):

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

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 our self-hosted libarrow binaries. You can do this 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, but results in a more fully-featured build than default.

Installing libarrow dependencies

When you install libarrow, 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).

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.

Dependencies for S3 support

The arrow package allows you to work with data in AWS S3 or in other cloud storage system that emulate S3. However, support for working with S3 is not enabled in the default build, and it has additional system requirements. 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. You also need the following system dependencies:

  • gcc >= 4.9 or clang >= 3.3; note that the default compiler on CentOS 7 is gcc 4.8.5, which is not sufficient
  • CURL: install libcurl-devel (rpm) or libcurl4-openssl-dev (deb)
  • OpenSSL >= 1.0.2: install openssl-devel (rpm) or libssl-dev (deb)

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

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, which is why we recommend method 1 above.

However, if you wish to fine-tune or customise your Linux installation, the instructions in this section explain how to do that.

If you wish to install libarrow from source instead of looking for pre-compiled binaries, you can set the LIBARROW_BINARY variable.


By default, this is set to TRUE, and so libarrow will only be built from source if this environment variable is set to FALSE or no compatible binary for your OS can be found.

When compiling libarrow from source, you have the power to really fine-tune which features to install. You can set the environment variable LIBARROW_MINIMAL to FALSE to enable a more full-featured build including S3 support and alternative memory allocators.


By default this variable is unset; if set to TRUE a trimmed-down version of arrow is installed with many features disabled.

Note that in this guide, you will have seen us mention the environment variable NOT_CRAN - this is a convenience variable, which when set to TRUE, automatically sets LIBARROW_MINIMAL to FALSE and LIBARROW_BINARY to TRUE.

Building libarrow from source requires more time and resources than installing a binary. We recommend that you set the environment variable ARROW_R_DEV to TRUE for more verbose output during the installation process if anything goes wrong.

Sys.setenv("ARROW_R_DEV" = TRUE)

Once you have set these variables, call install.packages() to install arrow using this configuration.

The section below discusses environment variables you can set before calling install.packages("arrow") to build from source and customise your configuration.

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_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 true
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 : If set to true, the script will try to download a binary C++ library built for your operating system. You may also set it to some other string, a related “distro-version” that has binaries built that work for your OS. See the distro map for compatible binaries and OSs. If no binary is found, installation will fall back to building C++ dependencies from source.
  • 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 = "")

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:

  • already have arrow installed and want to upgrade to a different version
  • want to install a development build
  • want to try to reinstall and fix issues with Linux C++ binaries

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:

  • Install the arrow package or run source("")
  • Run create_package_with_all_dependencies("my_arrow_pkg.tar.gz")
  • Copy the newly created my_arrow_pkg.tar.gz to the computer without internet access

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

  • Install the arrow package from the copied file
    • install.packages("my_arrow_pkg.tar.gz", dependencies = c("Depends", "Imports", "LinkingTo"))
    • This installation will build from source, so cmake must be available
  • Run arrow_info() to check installed capabilities
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, check the logs for a message that says *** Unable to identify current OS/version, or a message that says *** No C++ binaries found for an invalid OS. If you see either, please report an issue. You may also set the environment variable ARROW_R_DEV=TRUE for additional debug messages.

A workaround would be to set the environment variable LIBARROW_BINARY to a distribution-version that exists in the Ursa Labs repository. Setting LIBARROW_BINARY is also an option when there’s not an exact match for your OS but a similar version would work, such as if you’re on ubuntu-18.10 and there’s only a binary for ubuntu-18.04.

If that workaround works for you, and you believe that it should work for everyone else too, you may propose adding an entry to this lookup table. This table is checked during the installation process and tells the script to use binaries built on a different operating system/version because they’re known to work.

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

  • On CentOS, if you are using a more modern devtoolset, you may need to set the environment variables CC and CXX either in the shell or in R’s Makeconf. For CentOS 7 and above, both the Arrow system packages and the C++ binaries for R are built with the default system compilers. If you want to use either of these and you have a devtoolset installed, set CC=/usr/bin/gcc CXX=/usr/bin/g++ to use the system compilers instead of the devtoolset. Alternatively, if you want to build arrow with the newer devtoolset compilers, set both ARROW_USE_PKG_CONFIG and LIBARROW_BINARY to false so that you build the Arrow C++ from source using those compilers. Compiler mismatch between the arrow system libraries and the R package may cause R to segfault when arrow package functions are used. See discussions here and here.

  • If you have multiple versions of zstd installed on your system, installation by building libarrow from source may fail with an “undefined symbols” error. Workarounds include (1) setting LIBARROW_BINARY to use a C++ binary; (2) setting ARROW_WITH_ZSTD=OFF to build without zstd; or (3) uninstalling the conflicting zstd. See discussion here.


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.