tree: f95d15ffccaf234fb3885316abd3a26d7cefd92f [path history] [tgz]
  1. .Rbuildignore
  2. .gitignore
  3. DESCRIPTION
  4. Dockerfile
  5. Makefile
  6. NAMESPACE
  7. NEWS.md
  8. R/
  9. README.Rmd
  10. README.md
  11. _pkgdown.yml
  12. arrow.Rproj
  13. clang_format.sh
  14. cleanup
  15. configure
  16. configure.win
  17. cran-comments.md
  18. data-raw/
  19. inst/
  20. lint.sh
  21. man/
  22. src/
  23. tests/
  24. tools/
r/README.md

arrow

cran conda-forge

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 for a list of PPAs from which you can obtain it.

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.

Example

library(arrow)
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().

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, clone the repository and install a release build of the C++ library.

git clone https://github.com/apache/arrow.git
mkdir arrow/cpp/build && cd arrow/cpp/build
cmake .. -DARROW_PARQUET=ON -DARROW_BOOST_USE_SHARED:BOOL=Off -DARROW_INSTALL_NAME_RPATH=OFF
make install

This likely will require additional system libraries to be installed, the specifics of which are platform dependent. See the C++ developer guide for details.

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.

You’ll also need remotes::install_github("romainfrancois/decor").

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
rmarkdown::render("README.Rmd") # To rebuild README.md
pkgdown::build_site(run_dont_run=TRUE) # To preview the documentation website
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.)

Full package validation

R CMD build --keep-empty-dirs .
R CMD check arrow_*.tar.gz --as-cran --no-manual