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@@ -582,7 +582,7 @@ It's then up to you to figure out what's gone wrong with your data and either re
 In this chapter you learned about tidy data: data that has variables in columns and observations in rows.
 Tidy data makes working in the tidyverse easier, because it's a consistent structure understood by most functions: the main challenge is data from whatever structure you receive it in to a tidy format.
 To that end, you learned about `pivot_longer()` and `pivot_wider()` which allow you to tidy up many untidy datasets.
-The examples we used here are just a selection of those from `vignette(pivot, package = "tidyr")`, so if you encounter a problem that this chapter doesn't help you with, that vignette is a good place to try next.
+The examples we used here are just a selection of those from `vignette("pivot", package = "tidyr")`, so if you encounter a problem that this chapter doesn't help you with, that vignette is a good place to try next.
 
 If you particularly enjoyed this chapter and want to learn more about the underlying theory, you can learn more about the history and theoretical underpinnings in the [Tidy Data](https://www.jstatsoft.org/article/view/v059i10) paper published in the Journal of Statistical Software.