diff --git a/quarto.qmd b/quarto.qmd
index c35ef6f..260ca9b 100644
--- a/quarto.qmd
+++ b/quarto.qmd
@@ -601,28 +601,25 @@ On subsequent runs, knitr will check to see if the code has changed, and if it h
 The caching system must be used with care, because by default it is based on the code only, not its dependencies.
 For example, here the `processed_data` chunk depends on the `raw-data` chunk:
 
-```         
-`r chunk`{r}
+```{r}
 #| label: raw-data
 
 rawdata <- readr::read_csv("a_very_large_file.csv")
-`r chunk`
+```
 
-`r chunk`{r}
+```{r}
 #| label: processed_data
 #| cache: true
 
 processed_data <- rawdata |> 
   filter(!is.na(import_var)) |> 
   mutate(new_variable = complicated_transformation(x, y, z))
-`r chunk`
 ```
 
 Caching the `processed_data` chunk means that it will get re-run if the dplyr pipeline is changed, but it won't get rerun if the `read_csv()` call changes.
 You can avoid that problem with the `dependson` chunk option:
 
-```         
-`r chunk`{r}
+```{r}
 #| label: processed-data
 #| cache: true
 #| dependson: "raw-data"
@@ -630,7 +627,6 @@ You can avoid that problem with the `dependson` chunk option:
 processed_data <- rawdata |> 
   filter(!is.na(import_var)) |> 
   mutate(new_variable = complicated_transformation(x, y, z))
-`r chunk`
 ```
 
 `dependson` should contain a character vector of *every* chunk that the cached chunk depends on.
@@ -642,13 +638,11 @@ This is an arbitrary R expression that will invalidate the cache whenever it cha
 A good function to use is `file.info()`: it returns a bunch of information about the file including when it was last modified.
 Then you can write:
 
-```         
-`r chunk`{r}
+```{r}
 #| label: raw-data
 #| cache.extra: file.info("a_very_large_file.csv")
 
 rawdata <- readr::read_csv("a_very_large_file.csv")
-`r chunk`
 ```
 
 We've followed the advice of [David Robinson](https://twitter.com/drob/status/738786604731490304) to name these chunks: each chunk is named after the primary object that it creates.