Apache Arrow is a cross-language development platform for in-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.

The arrow package exposes an interface to the Arrow C++ library to access many of its features in R. This includes support for working with Parquet (read_parquet(), write_parquet()) and Feather (read_feather(), write_feather()) files, as well as lower-level access to Arrow memory and messages.

## Installation

Install the latest release of arrow from CRAN with

install.packages("arrow")

On macOS and Windows, installing a binary package from CRAN will handle Arrow’s C++ dependencies for you. 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 package from source and the C++ library is not found, the R package functions will notify you that Arrow is not available. Call

arrow::install_arrow()

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

When installing from source, if the R and C++ library versions do not match, installation may fail. If you’ve previously installed the libraries and want to upgrade the R package, you’ll need to update the Arrow C++ library first.

## Example

library(arrow)
#>
#> Attaching package: 'arrow'
#> The following object is masked from 'package:utils':
#>
#>     timestamp
#> The following objects are masked from 'package:base':
#>
#>     array, table
set.seed(24)

tab <- arrow::table(x = 1:10, y = rnorm(10))
tab$schema #> arrow::Schema #> x: int32 #> y: double tab #> arrow::Table as.data.frame(tab) #> x y #> 1 1 -0.545880758 #> 2 2 0.536585304 #> 3 3 0.419623149 #> 4 4 -0.583627199 #> 5 5 0.847460017 #> 6 6 0.266021979 #> 7 7 0.444585270 #> 8 8 -0.466495124 #> 9 9 -0.848370044 #> 10 10 0.002311942 ## Installing a development version To use the development version of the R package, you’ll need to install it from source, which requires the additional C++ library setup. On macOS, you may install the C++ library using Homebrew: # For the released version: brew install apache-arrow # Or for a development version, you can try: brew install apache-arrow --HEAD On Windows, you can download a .zip file with the arrow dependencies from the rwinlib project, and then set the RWINLIB_LOCAL environment variable to point to that zip file before installing the arrow R package. That project contains released versions of the C++ library; for a development version, Windows users may be able to find a binary by going to the Apache Arrow project’s Appveyor, selecting an R job from a recent build, and downloading the build\arrow-*.zip file from the “Artifacts” tab. Linux users can get a released version of the library from our PPAs, as described above. If you need a development version of the C++ library, you will likely need to build it from source. See “Development” below. Once you have the C++ library, you can install the R package from GitHub using the remotes package. From within an R session, # install.packages("remotes") # Or install "devtools", which includes remotes remotes::install_github("apache/arrow/r") or if you prefer to stay at the command line, R -e 'remotes::install_github("apache/arrow/r")' You can specify a particular commit, branch, or release to install by including a ref argument to install_github(). This is particularly useful to match the R package version to the C++ library version you’ve installed. ## Developing If you need to alter both the Arrow C++ library and the R package code, or if you can’t get a binary version of the latest C++ library elsewhere, you’ll need to build it from source too. First, install the C++ library. See the C++ developer guide for details. Note that after any change to the C++ library, you must reinstall it and run make clean or git clean -fdx . to remove any cached object code in the r/src/ directory before reinstalling the R package. This is only necessary if you make changes to the C++ library source; you do not need to manually purge object files if you are only editing R or Rcpp code inside r/. Once you’ve built the C++ library, you can install the R package and its dependencies, along with additional dev dependencies, from the git checkout: cd ../../r R -e 'install.packages("devtools"); devtools::install_dev_deps()' R CMD INSTALL . If the package fails to install/load with an error like this: ** testing if installed package can be loaded from temporary location Error: package or namespace load failed for 'arrow' in dyn.load(file, DLLpath = DLLpath, ...): unable to load shared object '/Users/you/R/00LOCK-r/00new/arrow/libs/arrow.so': dlopen(/Users/you/R/00LOCK-r/00new/arrow/libs/arrow.so, 6): Library not loaded: @rpath/libarrow.14.dylib try setting the environment variable LD_LIBRARY_PATH (or DYLD_LIBRARY_PATH on macOS) to wherever Arrow C++ was put in make install, e.g. export LD_LIBRARY_PATH=/usr/local/lib, and retry installing the R package. For any other build/configuration challenges, see the C++ developer guide. ### Editing Rcpp code The arrow package uses some customized tools on top of Rcpp to prepare its C++ code in src/. If you change C++ code in the R package, you will need to set the ARROW_R_DEV environment variable to TRUE (optionally, add it to your~/.Renviron file to persist across sessions) so that the data-raw/codegen.R file is used for code generation. The codegen.R script has these dependencies: remotes::install_github("romainfrancois/decor") install.packages(c("dplyr", "purrr", "glue")) ### Useful functions Within an R session, these can help with package development: devtools::load_all() # Load the dev package devtools::test(filter="^regexp$") # Run the test suite, optionally filtering file names
devtools::document() # Update roxygen documentation
devtools::check() # All package checks; see also below
Any of those can be run from the command line by wrapping them in R -e '\$COMMAND'. There’s also a Makefile to help with some common tasks from the command line (make test, make doc, make clean, etc.)
R CMD build --keep-empty-dirs .
R CMD check arrow_*.tar.gz --as-cran --no-manual