thisisnic commented on a change in pull request #10765:
URL: https://github.com/apache/arrow/pull/10765#discussion_r679253544
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File path: r/vignettes/dataset.Rmd
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@@ -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:
How about in this example here @nealrichardson ? I was thinking `data
frame` as we're referring to both `data.frame` and `tibble::tibble` objects,
but then they're both of or inherit from class `data.frame ` so I wasn't sure
what makes the most sense.
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