diff --git a/data-tidy.Rmd b/data-tidy.Rmd
index 5fe9d2d..c481e2c 100644
--- a/data-tidy.Rmd
+++ b/data-tidy.Rmd
@@ -566,7 +566,7 @@ It's then up to you to figure out what's gone wrong with your data and either re
 
 While `pivot_wider()` is occasionally useful for making tidy data, its real strength is making **untidy** data.
 While that sounds like a bad thing, untidy isn't a pejorative term: there are many untidy data structures that are extremely useful.
-Tidy data is a great starting point for most analyses but it's not the only data format you'll even need.
+Tidy data is a great starting point for most analyses but it's not the only data format you'll ever need.
 
 The following sections will show a few examples of `pivot_wider()` making usefully untidy data for presenting data to other humans, for input to multivariate statistics algorithms, and for pragmatically solving data manipulation challenges.
 
@@ -622,7 +622,7 @@ col_year
 
 `pivot_wider()` produces a tibble where each row is labelled by the `country` variable.
 But most classic statistical algorithm don't want the identifier as an explicit variable; they want as a **row name**.
-We can turn the `year` variable into row names with `column_to_rowname()`:
+We can turn the `country` variable into row names with `column_to_rowname()`:
 
 ```{r}
 col_year <- col_year |>