jonkeane commented on a change in pull request #158:
URL: https://github.com/apache/arrow-site/pull/158#discussion_r743203031
##########
File path: _posts/2021-11-01-r-6.0.0.md
##########
@@ -0,0 +1,183 @@
+---
+layout: post
+title: Apache Arrow R 6.0.0 Release
+date: "2021-11-01"
+author:
+categories: [release]
+---
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+We are happy to announce the recent release of version 6.0.0 of Arrow on CRAN,
+and in this blog post we highlight the main updates in this version. A big
+thanks goes to Dragos Moldovan-Grünfeld, Percy Camilo Triveño Aucahuasi,
+Dewey Dunnington, Matt Peterson, and Phillip Cloud, who, in this release, made
+their first contributions the to the R package.
+
+# Grouped aggregation
+
+Aggregations can now be made across groups using dplyr’s `group_by() %>%
summarise()` syntax. Arrow 5.0.0 allowed `summarise()` to aggregate across a
whole dataset, but 6.0.0 now allows you to aggregate across groups with
`group_by()` (a workflow we know people have been waiting for and asking
about!). These are usable both with in-memory Arrow tables as well as across
partitioned datasets. As usual, Arrow will read and process data in chunks and
in parallel when possible to produce results much faster than one could by
loading it all into memory then processing, and even better, allows for
operations that wouldn’t fit into memory on a single machine.
+
+The focus of this release has been on the initial implementation of this
functionality - for the next release, we’ll be looking to profile and optimize
to enhance performance.
Review comment:
```suggestion
The focus of this release has been on the initial implementation of this
functionality - for the next release, we’ll be looking to profile and optimize
to enhance performance. Connected with that, much of this functionality is
still very new and slightly experimental, (for example, it's not even wired up
in the pyarrow package yet!). We are excited to have people try this out, if
you run into any issues at all, please [let us
know](https://issues.apache.org/jira/browse/ARROW) so that we can improve these
for our next release.
```
--
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]
