diff --git a/functions.qmd b/functions.qmd
index 0f71598..54c589d 100644
--- a/functions.qmd
+++ b/functions.qmd
@@ -617,17 +617,7 @@ lin_check <- function(df, x, y) {
 }
 ```
 
-```{r}
-# https://twitter.com/sharoz/status/1574376332821204999
-
-# Facetting is fiddly - have to use special vars syntax.
-foo <- function(x) {
-  ggplot(mtcars) +
-    aes(x = mpg, y = disp) +
-    geom_point() +
-    facet_wrap(vars({{ x }}))
-}
-```
+Of course you might combine both dplyr and ggplot2:
 
 ```{r}
 sorted_bars <- function(df, var) {
@@ -639,18 +629,25 @@ sorted_bars <- function(df, var) {
 diamonds |> sorted_bars(cut)
 ```
 
-Of course you might combine both dplyr and ggplot2:
+Next we'll discuss two more complicated cases: facetting and automatic labelling.
+
+### Facetting
+
+Unfortunately facetting is a special challenge, mostly because it was implemented well before we understood what tidy evaluation was and how it should work.
+And unlike `aes()`, it wasn't straightforward to backport to tidy evalution, so you have to use a different syntax to usual.
+Instead of writing `~ x`, you write `vars(x)` and instead of `~ x + y` you write `vars(x, y)`.
+The only advantage of this syntax is that `vars()` is data masking so you can embrace within it.
 
 ```{r}
-bars <- function(df, condition, var) {
-  df |> 
-    filter({{ condition }}) |> 
-    ggplot(aes({{ var }})) + 
-    geom_bar() + 
-    scale_x_discrete(guide = guide_axis(angle = 45))
-}
+# https://twitter.com/sharoz/status/1574376332821204999
 
-diamonds |> bars(cut == "Good", clarity)
+# Facetting is fiddly - have to use special vars syntax.
+foo <- function(x) {
+  ggplot(mtcars) +
+    aes(x = mpg, y = disp) +
+    geom_point() +
+    facet_wrap(vars({{ x }}))
+}
 ```
 
 I've written these functions so that you can supply any data frame, but there are also advantages to hardcoding a data frame, if you're using it repeatedly:
@@ -668,6 +665,20 @@ density(species)
 density(island, sex)
 ```
 
+Also note that I hardcoded the `x` variable but allowed the fill to vary.
+
+```{r}
+bars <- function(df, condition, var) {
+  df |> 
+    filter({{ condition }}) |> 
+    ggplot(aes({{ var }})) + 
+    geom_bar() + 
+    scale_x_discrete(guide = guide_axis(angle = 45))
+}
+
+diamonds |> bars(cut == "Good", clarity)
+```
+
 ### Labelling
 
 It'd be nice to label this plot automatically.