diff --git a/data-tidy.qmd b/data-tidy.qmd
index c790473..86abd9d 100644
--- a/data-tidy.qmd
+++ b/data-tidy.qmd
@@ -569,9 +569,9 @@ df |> pivot_wider(
 Since you don't know how to work with this sort of data yet, you'll want to follow the hint in the warning to figure out where the problem is:
 
 ```{r}
-df %>%
-  group_by(id, name) %>%
-  summarize(n = n(), .groups = "drop") %>%
+df |> 
+  group_by(id, name) |> 
+  summarize(n = n(), .groups = "drop") |> 
   filter(n > 1L) 
 ```
 
diff --git a/functions.qmd b/functions.qmd
index a159cb3..23da97b 100644
--- a/functions.qmd
+++ b/functions.qmd
@@ -423,7 +423,7 @@ If you commonly perform the same set of summaries when doing initial data explor
 
 ```{r}
 summary6 <- function(data, var) {
-  data %>% summarise(
+  data |> summarise(
     min = min({{ var }}, na.rm = TRUE),
     mean = mean({{ var }}, na.rm = TRUE),
     median = median({{ var }}, na.rm = TRUE),
diff --git a/rectangling.qmd b/rectangling.qmd
index f2bf193..42d4ba4 100644
--- a/rectangling.qmd
+++ b/rectangling.qmd
@@ -698,12 +698,12 @@ If these case studies have whetted your appetite for more real-life rectangling,
     #| results: false
     tibble(json = got_chars) |> 
       unnest_wider(json) |> 
-      select(id, where(is.list)) %>% 
+      select(id, where(is.list)) |> 
       pivot_longer(
         where(is.list), 
         names_to = "name", 
         values_to = "value"
-      ) %>% 
+      ) |>  
       unnest_longer(value)
     ```