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, enabling access to many of its features in R. It provides low-level access to the Arrow C++ library API and higher-level access through a dplyr backend and familiar R functions.

## What can the arrow package do?

• Read and write Parquet files (read_parquet(), write_parquet()), an efficient and widely used columnar format
• Read and write Feather files (read_feather(), write_feather()), a format optimized for speed and interoperability
• Analyze, process, and write multi-file, larger-than-memory datasets (open_dataset(), write_dataset())
• Read large CSV and JSON files with excellent speed and efficiency (read_csv_arrow(), read_json_arrow())
• Manipulate and analyze Arrow data with dplyr verbs
• Read and write files in Amazon S3 buckets with no additional function calls
• Exercise fine control over column types for seamless interoperability with databases and data warehouse systems
• Use compression codecs including Snappy, gzip, Brotli, Zstandard, LZ4, LZO, and bzip2 for reading and writing data
• Enable zero-copy data sharing between R and Python
• Connect to Arrow Flight RPC servers to send and receive large datasets over networks
• Access and manipulate Arrow objects through low-level bindings to the C++ library
• Provide a toolkit for building connectors to other applications and services that use Arrow

## Installation

### Installing the latest release version

Install the latest release of arrow from CRAN with

install.packages("arrow")

Conda users 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.

### Installing a development version

Development versions of the package (binary and source) are built nightly and hosted at https://arrow-r-nightly.s3.amazonaws.com. To install from there:

install.packages("arrow", repos = "https://arrow-r-nightly.s3.amazonaws.com")

Conda users can install arrow nightly builds with

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

If you already have a version of arrow installed, you can switch to the latest nightly development version with

arrow::install_arrow(nightly = TRUE)

These nightly 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.

## Usage

Among the many applications of the arrow package, two of the most accessible are:

• High-performance reading and writing of data files with multiple file formats and compression codecs, including built-in support for cloud storage
• Analyzing and manipulating bigger-than-memory data with dplyr verbs

The sections below describe these two uses and illustrate them with basic examples. The sections below mention two Arrow data structures:

• Table: a tabular, column-oriented data structure capable of storing and processing large amounts of data more efficiently than R’s built-in data.frame and with SQL-like column data types that afford better interoperability with databases and data warehouse systems
• Dataset: a data structure functionally similar to Table but with the capability to work on larger-than-memory data partitioned across multiple files

### Reading and writing data files with arrow

The arrow package provides functions for reading single data files in several common formats. By default, calling any of these functions returns an R data.frame. To return an Arrow Table, set argument as_data_frame = FALSE.

• read_parquet(): read a file in Parquet format
• read_feather(): read a file in Feather format (the Apache Arrow IPC format)
• read_delim_arrow(): read a delimited text file (default delimiter is comma)
• read_csv_arrow(): read a comma-separated values (CSV) file
• read_tsv_arrow(): read a tab-separated values (TSV) file
• read_json_arrow(): read a JSON data file

For writing data to single files, the arrow package provides the functions write_parquet() and write_feather(). These can be used with R data.frame and Arrow Table objects.

For example, let’s write the Star Wars characters data that’s included in dplyr to a Parquet file, then read it back in. Parquet is a popular choice for storing analytic data; it is optimized for reduced file sizes and fast read performance, especially for column-based access patterns. Parquet is widely supported by many tools and platforms.

First load the arrow and dplyr packages:

library(arrow, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)

Then write the data.frame named starwars to a Parquet file at file_path:

file_path <- tempfile()
write_parquet(starwars, file_path)

Then read the Parquet file into an R data.frame named sw:

sw <- read_parquet(file_path)

R object attributes are preserved when writing data to Parquet or Feather files and when reading those files back into R. This enables round-trip writing and reading of sf::sf objects, R data.frames with with haven::labelled columns, and data.frames with other custom attributes.

For reading and writing larger files or sets of multiple files, arrow defines Dataset objects and provides the functions open_dataset() and write_dataset(), which enable analysis and processing of bigger-than-memory data, including the ability to partition data into smaller chunks without loading the full data into memory. For examples of these functions, see vignette("dataset", package = "arrow").

All these functions can read and write files in the local filesystem or in Amazon S3 (by passing S3 URIs beginning with s3://). For more details, see vignette("fs", package = "arrow")

### Using dplyr with arrow

The arrow package provides a dplyr backend enabling manipulation of Arrow tabular data with dplyr verbs. To use it, first load both packages arrow and dplyr. Then load data into an Arrow Table or Dataset object. For example, read the Parquet file written in the previous example into an Arrow Table named sw:

sw <- read_parquet(file_path, as_data_frame = FALSE)

Next, pipe on dplyr verbs:

result <- sw %>%
filter(homeworld == "Tatooine") %>%
rename(height_cm = height, mass_kg = mass) %>%
mutate(height_in = height_cm / 2.54, mass_lbs = mass_kg * 2.2046) %>%
arrange(desc(birth_year)) %>%
select(name, height_in, mass_lbs)

The arrow package uses lazy evaluation to delay computation until the result is required. This speeds up processing by enabling the Arrow C++ library to perform multiple computations in one operation. result is an object with class arrow_dplyr_query which represents all the computations to be performed:

result
#> Table (query)
#> name: string
#> height_in: expr
#> mass_lbs: expr
#>
#> * Filter: equal(homeworld, "Tatooine")
#> * Sorted by birth_year [desc]
#> See $.data for the source Arrow object To perform these computations and materialize the result, call compute() or collect(). compute() returns an Arrow Table, suitable for passing to other arrow or dplyr functions: result %>% compute() #> Table #> 10 rows x 3 columns #>$name <string>
#> $height_in <double> #>$mass_lbs <double>

collect() returns an R data.frame, suitable for viewing or passing to other R functions for analysis or visualization:

result %>% collect()
#> # A tibble: 10 x 3
#>    name               height_in mass_lbs
#>    <chr>                  <dbl>    <dbl>
#>  1 C-3PO                   65.7    165.
#>  2 Cliegg Lars             72.0     NA
#>  3 Shmi Skywalker          64.2     NA
#>  4 Owen Lars               70.1    265.
#>  5 Beru Whitesun lars      65.0    165.
#>  6 Darth Vader             79.5    300.
#>  7 Anakin Skywalker        74.0    185.
#>  8 Biggs Darklighter       72.0    185.
#>  9 Luke Skywalker          67.7    170.
#> 10 R5-D4                   38.2     70.5

The arrow package works with most single-table dplyr verbs except those that compute aggregates, such as summarise() and mutate() after group_by(). Inside dplyr verbs, Arrow offers support for many functions and operators, with common functions mapped to their base R and tidyverse equivalents. The changelog lists many of them. If there are additional functions you would like to see implemented, please file an issue as described in the Getting help section below.

For dplyr queries on Table objects, if the arrow package detects an unimplemented function within a dplyr verb, it automatically calls collect() to return the data as an R data.frame before processing that dplyr verb. For queries on Dataset objects (which can be larger than memory), it raises an error if the function is unimplemented; you need to explicitly tell it to collect().

Other applications of arrow are described in the following vignettes:

• vignette("python", package = "arrow"): use arrow and reticulate to pass data between R and Python
• vignette("flight", package = "arrow"): connect to Arrow Flight RPC servers to send and receive data
• vignette("arrow", package = "arrow"): access and manipulate Arrow objects through low-level bindings to the C++ library

## Getting help

If you encounter a bug, please file an issue with a minimal reproducible example on the Apache Jira issue tracker. Create an account or log in, then click Create to file an issue. Select the project Apache Arrow (ARROW), select the component R, and begin the issue summary with [R] followed by a space. For more information, see the Report bugs and propose features section of the Contributing to Apache Arrow page in the Arrow developer documentation.

We welcome questions, discussion, and contributions from users of the arrow package. For information about mailing lists and other venues for engaging with the Arrow developer and user communities, please see the Apache Arrow Community page.

All participation in the Apache Arrow project is governed by the Apache Software Foundation’s code of conduct.