diff --git a/iteration.Rmd b/iteration.Rmd
index 322b491..c689a60 100644
--- a/iteration.Rmd
+++ b/iteration.Rmd
@@ -102,7 +102,7 @@ Then we'll move on some variations of the for loop that help you solve other pro
 
     1.  Compute the mean of every column in `mtcars`.
     2.  Determine the type of each column in `nycflights13::flights`.
-    3.  Compute the number of unique values in each column of `iris`.
+    3.  Compute the number of unique values in each column of `palmerpenguins::penguins`.
     4.  Generate 10 random normals from distributions with means of -10, 0, 10, and 100.
 
     Think about the output, sequence, and body **before** you start writing the loop.
@@ -346,14 +346,14 @@ However, it is good to know they exist so that you're prepared for problems wher
     What if the names are not unique?
 
 3.  Write a function that prints the mean of each numeric column in a data frame, along with its name.
-    For example, `show_mean(iris)` would print:
+    For example, `show_mean(mpg)` would print:
 
     ```{r, eval = FALSE}
-    show_mean(iris)
-    #> Sepal.Length: 5.84
-    #> Sepal.Width:  3.06
-    #> Petal.Length: 3.76
-    #> Petal.Width:  1.20
+    show_mean(mpg)
+    #> displ:   3.47
+    #> year: 2004
+    #> cyl:     5.89
+    #> cty:    16.86
     ```
 
     (Extra challenge: what function did I use to make sure that the numbers lined up nicely, even though the variable names had different lengths?)
@@ -636,7 +636,7 @@ I focus on purrr functions here because they have more consistent names and argu
 
     1.  Compute the mean of every column in `mtcars`.
     2.  Determine the type of each column in `nycflights13::flights`.
-    3.  Compute the number of unique values in each column of `iris`.
+    3.  Compute the number of unique values in each column of `palmerpenguins::penguins`.
     4.  Generate 10 random normals from distributions with means of -10, 0, 10, and 100.
 
 2.  How can you create a single vector that for each column in a data frame indicates whether or not it's a factor?
@@ -909,11 +909,11 @@ A number of functions work with **predicate** functions that return either a sin
 `keep()` and `discard()` keep elements of the input where the predicate is `TRUE` or `FALSE` respectively:
 
 ```{r}
-iris %>% 
+gss_cat %>% 
   keep(is.factor) %>% 
   str()
 
-iris %>% 
+gss_cat %>% 
   discard(is.factor) %>% 
   str()
 ```
diff --git a/tibble.Rmd b/tibble.Rmd
index 5c90546..ce41ede 100644
--- a/tibble.Rmd
+++ b/tibble.Rmd
@@ -26,7 +26,7 @@ Most other R packages use regular data frames, so you might want to coerce a dat
 You can do that with `as_tibble()`:
 
 ```{r}
-as_tibble(iris)
+as_tibble(mtcars)
 ```
 
 You can create a new tibble from individual vectors with `tibble()`.