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arrow

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 analyzing large, multi-file datasets (open_dataset()), working with individual 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")

Conda users on Linux and macOS can install arrow from conda-forge with

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

Installing a released version of the arrow package requires no additional system dependencies. For macOS and Windows, CRAN hosts binary packages that contain the Arrow C++ library. On Linux, source package installation will also build necessary C++ dependencies. For a faster, more complete installation, set the environment variable NOT_CRAN=true. See vignette("install", package = "arrow") for details.

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()

to retry installation with dependencies.

Note that install_arrow() is available as a standalone script, so you can access it for convenience without first installing the package:

source("https://raw.githubusercontent.com/apache/arrow/master/r/R/install-arrow.R")
install_arrow()

Installing a development version

Development versions of the package (binary and source) are built daily and hosted at https://dl.bintray.com/ursalabs/arrow-r/. To install from there:

install.packages("arrow", repos = "https://dl.bintray.com/ursalabs/arrow-r")

Or

install_arrow(nightly = TRUE)

These daily package builds are not official Apache releases and are not recommended for production use. They may be useful for testing bug fixes and new features under active development.

Developing

Windows and macOS users who wish to contribute to the R package and don’t need to alter the Arrow C++ library may be able to obtain a recent version of the library without building from source. 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 nightly bintray repository, and then set the RWINLIB_LOCAL environment variable to point to that zip file before installing the arrow R package. Version numbers in that repository correspond to dates, and you will likely want the most recent.

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 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(c("devtools", "roxygen2", "pkgdown", "covr")); devtools::install_dev_deps()'
R CMD INSTALL .

If you need to set any compilation flags while building the Rcpp extensions, you can use the ARROW_R_CXXFLAGS environment variable. For example, if you are using perf to profile the R extensions, you may need to set

export ARROW_R_CXXFLAGS=-fno-omit-frame-pointer

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 R_LD_LIBRARY_PATH to wherever Arrow C++ was put in make install, e.g. export R_LD_LIBRARY_PATH=/usr/local/lib, and retry installing the R package.

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.

For any other build/configuration challenges, see the C++ developer guide and vignette("install", package = "arrow").

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 additional dependencies:

remotes::install_github("nealrichardson/decor")
install.packages("glue")

We use Google C++ style in our C++ code. Check for style errors with

./lint.sh

Fix any style issues before committing with

./lint.sh --fix

The lint script requires Python 3 and clang-format-8. If the command isn’t found, you can explicitly provide the path to it like CLANG_FORMAT=$(which clang-format-8) ./lint.sh. On macOS, you can get this by installing LLVM via Homebrew and running the script as CLANG_FORMAT=$(brew --prefix llvm@8)/bin/clang-format ./lint.sh

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
pkgdown::build_site() # To preview the documentation website
devtools::check() # All package checks; see also below
covr::package_coverage() # See test coverage statistics

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 .
R CMD check arrow_*.tar.gz --as-cran

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Version

Install

install.packages('arrow')

Monthly Downloads

335,737

Version

0.17.1

License

Apache License (>= 2.0)

Issues

Pull Requests

Stars

Forks

Maintainer

Neal Richardson

Last Published

May 19th, 2020

Functions in arrow (0.17.1)

FileSystem

FileSystem classes
ParquetWriterProperties

ParquetWriterProperties class
Partitioning

Define Partitioning for a Dataset
FileSelector

file selector
Schema

Schema class
arrow_available

Is the C++ Arrow library available?
Table

Table class
buffer

Buffer class
FixedWidthType

class arrow::FixedWidthType
FileInfo

FileSystem entry info
InputStream

InputStream classes
ParquetFileWriter

ParquetFileWriter class
Message

class arrow::Message
Field

Field class
FileFormat

Dataset file formats
DictionaryType

class DictionaryType
array

Arrow Arrays
arrow-package

arrow: Integration to 'Apache' 'Arrow'
ParquetFileReader

ParquetFileReader class
OutputStream

OutputStream classes
install_arrow

Install or upgrade the Arrow library
ParquetReaderProperties

ParquetReaderProperties class
enums

Arrow enums
MessageReader

class arrow::MessageReader
default_memory_pool

compression

Compressed stream classes
MemoryPool

class arrow::MemoryPool
make_readable_file

Handle a range of possible input sources
install_pyarrow

Install pyarrow for use with reticulate
read_feather

Read a Feather file
RecordBatchWriter

RecordBatchWriter classes
hive_partition

Construct Hive partitioning
Scanner

Scan the contents of a dataset
cpu_count

Manage the global CPU thread pool in libarrow
mmap_create

Create a new read/write memory mapped file of a given size
data-type

Apache Arrow data types
mmap_open

Open a memory mapped file
read_json_arrow

Read a JSON file
RecordBatch

RecordBatch class
map_batches

Apply a function to a stream of RecordBatches
read_delim_arrow

Read a CSV or other delimited file with Arrow
open_dataset

Open a multi-file dataset
dictionary

Create a dictionary type
read_ipc_stream

Read Arrow IPC stream format
dataset_factory

Create a DatasetFactory
RecordBatchReader

RecordBatchReader classes
write_feather

Write data in the Feather format
codec_is_available

Check whether a compression codec is available
write_ipc_stream

Write Arrow IPC stream format
write_parquet

Write Parquet file to disk
write_to_raw

Write Arrow data to a raw vector
read_message

Read a Message from a stream
cast_options

Cast options
read_schema

read a Schema from a stream
reexports

Objects exported from other packages
read_parquet

Read a Parquet file
read_record_batch

Read a RecordBatch from an encapsulated IPC message
type

infer the arrow Array type from an R vector
unify_schemas

Combine and harmonize schemas
CsvTableReader

Arrow CSV and JSON table reader classes
FeatherReader

FeatherReader class
Dataset

Multi-file datasets
CsvReadOptions

File reader options
DataType

class arrow::DataType
Expression

Arrow expressions
Codec

Compression Codec class
ArrayData

ArrayData class
ChunkedArray

ChunkedArray class