thisisnic commented on code in PR #40148:
URL: https://github.com/apache/arrow/pull/40148#discussion_r1525628735


##########
r/README.md:
##########
@@ -1,114 +1,96 @@
 # arrow <img 
src="https://arrow.apache.org/img/arrow-logo_hex_black-txt_white-bg.png"; 
align="right" alt="" width="120" />
 
+<!-- badges: start -->
+
 
[![cran](https://www.r-pkg.org/badges/version-last-release/arrow)](https://cran.r-project.org/package=arrow)
 
[![CI](https://github.com/apache/arrow/workflows/R/badge.svg?event=push)](https://github.com/apache/arrow/actions?query=workflow%3AR+branch%3Amain+event%3Apush)
 
[![conda-forge](https://img.shields.io/conda/vn/conda-forge/r-arrow.svg)](https://anaconda.org/conda-forge/r-arrow)
 
-[Apache Arrow](https://arrow.apache.org/) is a cross-language
-development platform for in-memory and larger-than-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 R 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?
-
-The arrow package provides functionality for a wide range of data analysis
-tasks. It allows users to read and write data in a variety formats:
-
--   Read and write Parquet files, an efficient and widely used columnar format
--   Read and write Arrow (formerly known as Feather) files, a format optimized 
for speed and
-    interoperability
--   Read and write CSV files with excellent speed and efficiency
--   Read and write multi-file and larger-than-memory datasets
--   Read JSON files
+<!-- badges: end -->
 
-It provides data analysis tools for both in-memory and larger-than-memory data 
sets
+## Overview
 
--   Analyze and process larger-than-memory datasets
--   Manipulate and analyze Arrow data with dplyr verbs
+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.
 
-It provides access to remote filesystems and servers
-
--   Read and write files in Amazon S3 and Google Cloud Storage buckets
--   Connect to Arrow Flight servers to transport large datasets over networks  
-    
-Additional features include:
-
--   Zero-copy data sharing between R and Python
--   Fine control over column types to work seamlessly
-    with databases and data warehouses
--   Support for compression codecs including Snappy, gzip, Brotli,
-    Zstandard, LZ4, LZO, and bzip2
--   Access and manipulate Arrow objects through low-level bindings
-    to the C++ library
--   Toolkit for building connectors to other applications
-    and services that use Arrow
+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](article/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.

Review Comment:
   ```suggestion
   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.
   ```



-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: [email protected]

For queries about this service, please contact Infrastructure at:
[email protected]

Reply via email to