diff --git a/missing-values.Rmd b/missing-values.Rmd
index 3dfee88..d841571 100644
--- a/missing-values.Rmd
+++ b/missing-values.Rmd
@@ -61,7 +61,7 @@ x <- c(1, 4, 5, 7, NA)
 coalesce(x, 0)
 ```
 
-You could use `mutate()` together with `across()` to apply to every this treatment to (say) every numeric column in a data frame:
+You could use `mutate()` together with `across()` to apply this treatment to (say) every numeric column in a data frame:
 
 ```{r, eval = FALSE}
 df |> 
@@ -127,9 +127,9 @@ stocks <- tibble(
 
 This dataset has two missing observations:
 
--   The `price` in the fourth quarter of 2021 is explicitly missing, because its value is `NA`.
+-   The `price` in the fourth quarter of 2020 is explicitly missing, because its value is `NA`.
 
--   The `price` for the first quarter of 2022 is implicitly missing, because it simply does not appear in the dataset.
+-   The `price` for the first quarter of 2021 is implicitly missing, because it simply does not appear in the dataset.
 
 One way to think about the difference is with this Zen-like koan:
 
@@ -257,7 +257,7 @@ ggplot(health, aes(smoker)) +
 ```
 
 The same problem comes up more generally with `dplyr::group_by()`.
-You can request that all factor levels be preserved with `.drop = TRUE`:
+And again you can use `.drop = FALSE` to preserve all factor levels:
 
 ```{r}
 health |>