diff --git a/missing-values.Rmd b/missing-values.Rmd
index a70bf74..2167641 100644
--- a/missing-values.Rmd
+++ b/missing-values.Rmd
@@ -6,27 +6,21 @@ status("drafting")
 
 ## Introduction
 
-```{r}
+A value can be missing in one of two possible ways.
+It can be **explicitly** missing, i.e. flagged with `NA`, or it can be **implicitly**, missing i.e. simply not present in the data.
+
+This chapter will explore cases where implicit and explicit missing values can become explict,
+
+### Prerequisites
+
+```{r setup, message = FALSE}
 library(tidyverse)
+library(nycflights13)
 ```
 
-Missing topics:
+## Motivation
 
--   Missing values generated from matching data frames (i.e. `left_join()` and `anti_join()`
-
--   Last observation carried forward and `tidy::fill()`
-
--   `coalesce()` and `na_if()`
-
-## Explicit vs implicit missing values {#missing-values-tidy}
-
-Changing the representation of a dataset brings up an important subtlety of missing values.
-Surprisingly, a value can be missing in one of two possible ways:
-
--   **Explicitly**, i.e. flagged with `NA`.
--   **Implicitly**, i.e. simply not present in the data.
-
-Let's illustrate this idea with a very simple data set:
+Let's illustrate this idea with a very simple data set.
 
 ```{r}
 stocks <- tibble(
@@ -44,6 +38,47 @@ There are two missing values in this dataset:
 
 One way to think about the difference is with this Zen-like koan: An explicit missing value is the presence of an absence; an implicit missing value is the absence of a presence.
 
+## Complete and joins
+
+If a dataset has a regular structure, you can make implicit missing values implicit with `complete()`:
+
+```{r}
+stocks |>
+  complete(year, qtr)
+```
+
+If you know that the range isn't correct, you can:
+
+```{r}
+stocks |>
+  complete(year = 2015:2017, qtr)
+```
+
+`complete()` takes a set of columns, and finds all unique combinations.
+It then ensures the original dataset contains all those values, filling in explicit `NA`s where necessary.
+
+```{r}
+stocks |> 
+  expand(year, qtr) |> 
+  left_join(stocks)
+```
+
+Other times missing values might be defined by another dataset.
+
+```{r}
+flights |> 
+  distinct(faa = dest) |> 
+  anti_join(airports)
+
+flights |> 
+  distinct(tailnum) |> 
+  anti_join(planes)
+```
+
+## Pivotting {#missing-values-tidy}
+
+Changing the representation of a dataset brings up an important subtlety of missing values.
+
 The way that a dataset is represented can make implicit values explicit.
 For example, we can make the implicit missing value explicit by putting years in the columns:
 
@@ -65,15 +100,7 @@ stocks |>
   )
 ```
 
-Another important tool for making missing values explicit in tidy data is `complete()`:
-
-```{r}
-stocks |>
-  complete(year, qtr)
-```
-
-`complete()` takes a set of columns, and finds all unique combinations.
-It then ensures the original dataset contains all those values, filling in explicit `NA`s where necessary.
+## Last observation carried forward
 
 There's one other important tool that you should know for working with missing values.
 Sometimes when a data source has primarily been used for data entry, missing values indicate that the previous value should be carried forward:
@@ -96,41 +123,8 @@ treatment |>
   fill(person)
 ```
 
-`group_by` + `.drop = FALSE`
+## Factors
 
-### Exercises
+-   factors: `group_by` + `.drop = FALSE`
 
-1.  Compare and contrast the `fill` arguments to `pivot_wider()` and `complete()`.
-
-2.  What does the direction argument to `fill()` do?
-
-## dplyr verbs
-
-`filter()` only includes rows where the condition is `TRUE`; it excludes both `FALSE` and `NA` values.
-If you want to preserve missing values, ask for them explicitly:
-
-```{r}
-df <- tibble(x = c(1, NA, 3))
-filter(df, x > 1)
-filter(df, is.na(x) | x > 1)
-```
-
-Missing values are always sorted at the end:
-
-```{r}
-df <- tibble(x = c(5, 2, NA))
-arrange(df, x)
-arrange(df, desc(x))
-```
-
-Explain the warning here
-
-```{r, eval = FALSE}
-flights |> 
-  group_by(dest) |> 
-  summarise(max_delay = max(arr_delay, na.rm = TRUE))
-```
-
-## Exercises
-
-1.  Why is `NA ^ 0` not missing? Why is `NA | TRUE` not missing? Why is `FALSE & NA` not missing? Can you figure out the general rule? (`NA * 0` is a tricky counterexample!)
+## 
diff --git a/numbers.Rmd b/numbers.Rmd
index 384d5e5..db9ee29 100644
--- a/numbers.Rmd
+++ b/numbers.Rmd
@@ -344,6 +344,10 @@ slide_vec(x, sum, .before = 2, .after = 2, .complete = TRUE)
 
 These are often used with numbers, but can be applied to most other column types.
 
+### Missing values
+
+`coalesce()`
+
 ### Ranks
 
 dplyr provides a number of ranking functions, but you should start with `dplyr::min_rank()`.