diff --git a/regexps.qmd b/regexps.qmd
index bd2583c..64f1353 100644
--- a/regexps.qmd
+++ b/regexps.qmd
@@ -22,6 +22,11 @@ We'll finish up with a survey of other places in stringr, the tidyverse, and bas
 
 ### Prerequisites
 
+::: callout-important
+This chapter relies on features only found in stringr 1.5.0 and tidyr 1.3.0 which are still in development.
+If you want to live life on the edge you can get the dev versions with `devtools::install_github(c("tidyverse/stringr", "tidyverse/tidyr"))`.
+:::
+
 In this chapter, we'll use regular expression functions from stringr and tidyr, both core members of the tidyverse, as well as data from the babynames package.
 
 ```{r}
diff --git a/strings.qmd b/strings.qmd
index 3079870..0b861a4 100644
--- a/strings.qmd
+++ b/strings.qmd
@@ -18,6 +18,11 @@ The chapter finishes up with functions that work with individual letters and a b
 
 ### Prerequisites
 
+::: callout-important
+This chapter relies on features only found in stringr 1.5.0 and tidyr 1.3.0 which are still in development.
+If you want to live life on the edge you can get the dev versions with `devtools::install_github(c("tidyverse/stringr", "tidyverse/tidyr"))`.
+:::
+
 In this chapter, we'll use functions from the stringr package which is part of the core tidyverse.
 We'll also use the babynames data since it provides some fun strings to manipulate.
 
@@ -173,10 +178,11 @@ df |> mutate(greeting = str_c("Hi ", name, "!"))
 If you want missing values to display in some other way, use `coalesce()` either inside or outside of `str_c()`:
 
 ```{r}
-df |> mutate(
-  greeting1 = str_c("Hi ", coalesce(name, "you"), "!"),
-  greeting2 = coalesce(str_c("Hi ", name, "!"), "Hi!")
-)
+df |> 
+  mutate(
+    greeting1 = str_c("Hi ", coalesce(name, "you"), "!"),
+    greeting2 = coalesce(str_c("Hi ", name, "!"), "Hi!")
+  )
 ```
 
 ### `str_glue()` {#sec-glue}
@@ -305,7 +311,11 @@ They are more complicated that their `by` equivalents because you need to name t
 ```{r}
 df3 <- tibble(x = c("a,1,2022", "b,2,2011", "e,5,2015"))
 df3 |> 
-  separate_wider_delim(x, delim = ",", names = c("letter", "number", "year"))
+  separate_wider_delim(
+    x,
+    delim = ",",
+    names = c("letter", "number", "year")
+  )
 ```
 
 If a specific value is not useful you can use `NA` to omit it from the results:
@@ -313,7 +323,11 @@ If a specific value is not useful you can use `NA` to omit it from the results:
 ```{r}
 df3 <- tibble(x = c("a,1,2022", "b,2,2011", "e,5,2015"))
 df3 |> 
-  separate_wider_delim(x, delim = ",", names = c("letter", NA, "year"))
+  separate_wider_delim(
+    x,
+    delim = ",",
+    names = c("letter", NA, "year")
+  )
 ```
 
 Alternatively, you can provide `names_sep` and `separate_wider_delim()` will use that separator to name automatically:
@@ -330,7 +344,10 @@ You can omit values from the output by not naming them:
 ```{r}
 df4 <- tibble(x = c("202215TX", "202122LA", "202325CA")) 
 df4 |> 
-  separate_wider_position(x, c(year = 4, age = 2, state = 2))
+  separate_wider_position(
+    x,
+    c(year = 4, age = 2, state = 2)
+  )
 ```
 
 ### Case studies