diff --git a/data-transform.qmd b/data-transform.qmd
index 897ef31..9cc4d7d 100644
--- a/data-transform.qmd
+++ b/data-transform.qmd
@@ -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.