diff --git a/logicals.Rmd b/logicals.Rmd
index 7a2bd7e..cdf1af4 100644
--- a/logicals.Rmd
+++ b/logicals.Rmd
@@ -12,7 +12,8 @@ You'll find logical vectors directly in data relatively rarely, but despite that
 
 We'll begin with the most common way of creating logical vectors: numeric comparisons.
 Then we'll talk about using Boolean algebra to combine different logical vectors, and some useful summaries for logical vectors.
-We'll finish off with some other tool for making conditional changes
+We'll finish off with some other tool for making conditional changes.
+Along the way, you'll also learn a little more about working with missing values, `NA`.
 
 ### Prerequisites
 
@@ -269,6 +270,10 @@ Similar reasoning applies with `NA & FALSE`.
 ### Exercises
 
 1.  Find all flights where `arr_delay` is missing but `dep_delay` is not. Find all flights where neither `arr_time` nor `sched_arr_time` are missing, but `arr_delay` is.
+2.  How many flights have a missing `dep_time`? What other variables are missing? What might these rows represent?
+3.  How could you use `arrange()` to sort all missing values to the start? (Hint: use `!is.na()`).
+4.  Come up with another approach that will give you the same output as `not_cancelled |> count(dest)` and `not_cancelled |> count(tailnum, wt = distance)` (without using `count()`).
+5.  Look at the number of cancelled flights per day. Is there a pattern? Is the proportion of cancelled flights related to the average delay?
 
 ## Summaries
 
@@ -416,3 +421,4 @@ df |> filter(cumall(!(balance < 0)))
 ### 
 
 ## 
+
diff --git a/missing-values.Rmd b/missing-values.Rmd
index a74fc95..a70bf74 100644
--- a/missing-values.Rmd
+++ b/missing-values.Rmd
@@ -18,31 +18,6 @@ Missing topics:
 
 -   `coalesce()` and `na_if()`
 
-## Basics
-
-### Missing values {#missing-values-filter}
-
-If you want to determine if a value is missing, use `is.na()`:
-
-```{r}
-is.na(x)
-```
-
-### Exercises
-
-1.  How many flights have a missing `dep_time`?
-    What other variables are missing?
-    What might these rows represent?
-
-2.  How could you use `arrange()` to sort all missing values to the start?
-    (Hint: use `!is.na()`).
-
-3.  Come up with another approach that will give you the same output as `not_cancelled |> count(dest)` and `not_cancelled |> count(tailnum, wt = distance)` (without using `count()`).
-
-4.  Look at the number of cancelled flights per day.
-    Is there a pattern?
-    Is the proportion of cancelled flights related to the average delay?
-
 ## Explicit vs implicit missing values {#missing-values-tidy}
 
 Changing the representation of a dataset brings up an important subtlety of missing values.