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Fixed typos (#1186) 

* Fixed typos

* Update data-transform.qmd

Co-authored-by: Mine Cetinkaya-Rundel <cetinkaya.mine@gmail.com>
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@ -91,7 +91,7 @@ The code starts with the `flights` dataset, then filters it, then groups it, the
We'll come back to the pipe and its alternatives in @sec-pipes.
dplyr's verbs are organised into four groups based on what they operate on: **rows**, **columns**, **groups**, or **tables**.
In the following sections you'll learn the most important verbs for rows, columns, and groups, then we'll come back to verb that work on tables in @sec-joins.
In the following sections you'll learn the most important verbs for rows, columns, and groups, then we'll come back to verbs that work on tables in @sec-joins.
Let's dive in!
## Rows
@ -238,7 +238,7 @@ Note that if you want to find the number of duplicates, or rows that weren't dup
5. Which flights traveled the farthest distance?
Which traveled the least distance?
6. Does it matter what order you used `filter()` and `arrange()` in if you're using both?
6. Does it matter what order you used `filter()` and `arrange()` if you're using both?
Why/why not?
Think about the results and how much work the functions would have to do.
@ -447,7 +447,7 @@ This means subsequent operations will now work "by month".
### `summarize()` {#sec-summarize}
The most important grouped operation is a summary, which each collapses each group to a single row.
The most important grouped operation is a summary, which collapses each group to a single row.
In dplyr, this is operation is performed by `summarize()`[^data-transform-3], as shown by the following example, which computes the average departure delay by month:
[^data-transform-3]: Or `summarise()`, if you prefer British English.