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### Rationale for this change
#35875#35082 and #32895 make a number of recommendations to update the the Readme
### What changes are included in this PR?
Rewording and reorganizing the Readme and sidebar.
### Are these changes tested?
n/a
### Are there any user-facing changes?
Yes
* Closes: #35875
Lead-authored-by: David Greiss <david.dgreiss@gmail.com>
Co-authored-by: Nic Crane <thisisnic@gmail.com>
Co-authored-by: Bryce Mecum <petridish@gmail.com>
Signed-off-by: Nic Crane <thisisnic@gmail.com>
[Apache Arrow](https://arrow.apache.org/) is a cross-language
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development platform for in-memory and larger-than-memory data. It specifies a standardized
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language-independent columnar memory format for flat and hierarchical
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data, organized for efficient analytic operations on modern hardware. It
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also provides computational libraries and zero-copy streaming, messaging,
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and interprocess communication.
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The arrow R package exposes an interface to the Arrow C++ library,
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enabling access to many of its features in R. It provides low-level
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access to the Arrow C++ library API and higher-level access through a
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`{dplyr}` backend and familiar R functions.
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## What can the arrow package do?
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The arrow package provides functionality for a wide range of data analysis
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tasks. It allows users to read and write data in a variety formats:
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<!-- badges: end -->
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- Read and write Parquet files, an efficient and widely used columnar format
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- Read and write Arrow (formerly known as Feather) files, a format optimized for speed and
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interoperability
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- Read and write CSV files with excellent speed and efficiency
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- Read and write multi-file and larger-than-memory datasets
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- Read JSON files
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## Overview
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It provides data analysis tools for both in-memory and larger-than-memory data sets
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- Analyze and process larger-than-memory datasets
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- Manipulate and analyze Arrow data with dplyr verbs
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It provides access to remote filesystems and servers
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- Read and write files in Amazon S3 and Google Cloud Storage buckets
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- Connect to Arrow Flight servers to transport large datasets over networks
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Additional features include:
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The R `{arrow}` package provides access to many of the features of the [Apache Arrow C++ library](https://arrow.apache.org/docs/cpp/index.html) for R users. The goal of arrow is to provide an Arrow C++ backend to `{dplyr}`, and access to the Arrow C++ library through familiar base R and tidyverse functions, or `{R6}` classes.
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- Zero-copy data sharing between R and Python
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- Fine control over column types to work seamlessly
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with databases and data warehouses
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- Support for compression codecs including Snappy, gzip, Brotli,
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Zstandard, LZ4, LZO, and bzip2
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- Access and manipulate Arrow objects through low-level bindings
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to the C++ library
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- Toolkit for building connectors to other applications
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and services that use Arrow
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To learn more about the Apache Arrow project, see the parent documentation of the [Arrow Project](https://arrow.apache.org/). The Arrow project provides functionality for a wide range of data analysis tasks to store, process and move data fast. See the [read/write article](articles/read_write.html) to learn about reading and writing data files, [data wrangling](articles/data_wrangling.html) to learn how to use dplyr syntax with arrow objects, and the [function documentation](reference/acero.html) for a full list of supported functions within dplyr queries.
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## Installation
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Most R users will probably want to install the latest release of arrow
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from CRAN:
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The latest release of arrow can be installed from CRAN. In most cases installing the latest release should work without requiring any additional system dependencies, especially if you are using
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Windows or macOS.
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```r
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```r
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install.packages("arrow")
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```
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Alternatively, if you are using conda you can install arrow from conda-forge:
In most cases installing the latest release should work without
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requiring any additional system dependencies, especially if you are using
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Window or a Mac. For those users, CRAN hosts binary packages that contain
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the Arrow C++ library upon which the arrow package relies, and no
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additional steps should be required.
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There are some special cases to note:
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- On macOS, the R you use with Arrow should match the architecture of the machine you are using. If you're using an ARM (aka M1, M2, etc.) processor use R compiled for arm64. If you're using an Intel based mac, use R compiled for x86. Using R and Arrow compiled for Intel based macs on an ARM based mac will result in segfaults and crashes.
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- On macOS, the R you use with Arrow should match the architecture of the machine you are using. If you're using an ARM (aka M1, M2, etc.) processor use R compiled for arm64. If you're using an Intel based mac, use R compiled for x86. Using R and Arrow compiled for Intel based macs on an ARM based mac will result in segfaults and crashes.
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- On Linux the installation process can sometimes be more involved because CRAN does not host binaries for Linux. For more information please see the [installation guide](articles/install.html).
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- If you are compiling arrow from source, please note that as of version 10.0.0, arrow requires C++17 to build. This has implications on Windows and CentOS 7. For Windows users it means you need to be running an R version of 4.0 or later. On CentOS 7, it means you need to install a newer compiler than the default system compiler gcc. See the [installation details article](https://arrow.apache.org/docs/r/articles/developers/install_details.html) for guidance.
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- Development versions of arrow are released nightly. For information on how to installl nighhtly builds please see the [installing nightly builds](articles/install_nightly.html) article.
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## What can the arrow package do?
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The Arrow C++ library is comprised of different parts, each of which serves a specific purpose. The arrow package provides binding to the C++ functionality for a wide range of data analysis
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tasks.
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It allows users to read and write data in a variety formats:
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- On Linux the installation process can sometimes be more involved because
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CRAN does not host binaries for Linux. For more information please see the [installation guide](https://arrow.apache.org/docs/r/articles/install.html).
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- Read and write Parquet files, an efficient and widely used columnar format
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- Read and write Arrow (formerly known as Feather) files, a format optimized for speed and
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interoperability
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- Read and write CSV files with excellent speed and efficiency
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- Read and write multi-file and larger-than-memory datasets
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- Read JSON files
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- If you are compiling arrow from source, please note that as of version
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10.0.0, arrow requires C++17 to build. This has implications on Windows and
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CentOS 7. For Windows users it means you need to be running an R version of
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4.0 or later. On CentOS 7, it means you need to install a newer compiler
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than the default system compiler gcc 4.8. See the [installation details article](https://arrow.apache.org/docs/r/articles/developers/install_details.html) for guidance. Note that
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this does not affect users who are installing a binary version of the package.
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It provides access to remote filesystems and servers:
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-Development versions of arrow are released nightly. Most users will not
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need to install nightly builds, but if you do please see the article on [installing nightly builds](https://arrow.apache.org/docs/r/articles/install_nightly.html) for more information.
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-Read and write files in Amazon S3 and Google Cloud Storage buckets
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- Connect to Arrow Flight servers to transport large datasets over networks
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## Arrow resources
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Additional features include:
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- Manipulate and analyze Arrow data with dplyr verbs
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- Zero-copy data sharing between R and Python
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- Fine control over column types to work seamlessly with databases and data warehouses
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- Toolkit for building connectors to other applications and services that use Arrow
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+
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## What is Apache Arrow?
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Apache Arrow is a cross-language development platform for in-memory and
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larger-than-memory data. It specifies a standardized language-independent
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+
columnar memory format for flat and hierarchical data, organized for efficient
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+
analytic operations on modern hardware. It also provides computational libraries
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+
and zero-copy streaming, messaging, and interprocess communication.
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+
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This package exposes an interface to the Arrow C++ library, enabling access to
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many of its features in R. It provides low-level access to the Arrow C++ library
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API and higher-level access through a dplyr backend and familiar R functions.
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In addition to the official [Arrow R package documentation](https://arrow.apache.org/docs/r/), the [Arrow for R cheatsheet](https://github.com/apache/arrow/blob/-/r/cheatsheet/arrow-cheatsheet.pdf), and the [Apache Arrow R Cookbook](https://arrow.apache.org/cookbook/r/index.html) are useful resources for getting started with arrow.
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## Arrow resources
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There are a few additional resources that you may find useful for getting started with arrow:
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- The official [Arrow R package documentation](https://arrow.apache.org/docs/r/)
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-[Arrow for R cheatsheet](https://github.com/apache/arrow/blob/-/r/cheatsheet/arrow-cheatsheet.pdf)
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-[Apache Arrow R Cookbook](https://arrow.apache.org/cookbook/r/index.html)
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- R for Data Science [Chapter on Arrow](https://r4ds.hadley.nz/arrow)
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