nealrichardson commented on a change in pull request #10765:
URL: https://github.com/apache/arrow/pull/10765#discussion_r679285857
##########
File path: r/vignettes/dataset.Rmd
##########
@@ -313,27 +330,29 @@ instead of a file path, or simply concatenate them like
`big_dataset <- c(ds1, d
As you can see, querying a large dataset can be made quite fast by storage in
an
efficient binary columnar format like Parquet or Feather and partitioning
based on
-columns commonly used for filtering. However, we don't always get our data
delivered
-to us that way. Sometimes we start with one giant CSV. Our first step in
analyzing data
+columns commonly used for filtering. However, data isn't always stored that
way.
+Sometimes you might start with one giant CSV. The first step in analyzing data
is cleaning is up and reshaping it into a more usable form.
-The `write_dataset()` function allows you to take a Dataset or other tabular
data object---an Arrow `Table` or `RecordBatch`, or an R `data.frame`---and
write it to a different file format, partitioned into multiple files.
+The `write_dataset()` function allows you to take a Dataset or another tabular
+data object - an Arrow Table or RecordBatch, or an R data frame - and write
Review comment:
I know 😞 I think I'd go with "data frame" so as to (in theory) avoid
the "you said data.frame but it's a tibble" though my reasoning for picking one
or the other is more about what's in my head when I'm writing, and you're
right, the distinction is not perfect in the real world, nor may the reader
share my distinction.
--
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]
