diff --git a/missing-values.qmd b/missing-values.qmd
index 6bebc3e..365f7cb 100644
--- a/missing-values.qmd
+++ b/missing-values.qmd
@@ -176,7 +176,7 @@ In that case, you can do manually what `complete()` does for you: create a data
 ### Joins
 
 This brings us to another important way of revealing implicitly missing observations: joins.
-You'll learn more about joins in @sec-joins, but we wanted to quickly mention them to you here since you can often only know that values are missing from one dataset when you compare it another.
+You'll learn more about joins in @sec-joins, but we wanted to quickly mention them to you here since you can often only know that values are missing from one dataset when you compare it to another.
 
 `dplyr::anti_join(x, y)` is a particularly useful tool here because it selects only the rows in `x` that don't have a match in `y`.
 For example, we can use two `anti_join()`s to reveal that we're missing information for four airports and 722 planes mentioned in `flights`: