Missing package decl

model-assess.Rmd

@@ -222,7 +222,7 @@ Both the boostrap and cross-validation are build on top of a "resample" object.
 These functions return an object of class "resample", which represents the resample in a memory efficient way. Instead of storing the resampled dataset itself, it instead stores the integer indices, and a "pointer" to the original dataset. This makes resamples take up much less memory.
 
 ```{r}
-x <- resample_bootstrap(as_tibble(mtcars))
+x <- resample_bootstrap(tibble::as_tibble(mtcars))
 class(x)
 
 x

tidy.Rmd

@@ -304,12 +304,12 @@ gather(table5, "year", "population", -1)
 
 You can also identify columns by name with the notation introduced by the `select` function in `dplyr`, see Section 3.1.
 
-You can easily combine the new versions of `table4` and `table5` into a single data frame because the new versions are both tidy. To combine the datasets, use the `left_join()` function from Section 3.6.
+You can easily combine the new versions of `table4` and `table5` into a single data frame because the new versions are both tidy. To combine the datasets, use the `dplyr::left_join()` function which you'll learn about in [relational data].
 
 ```{r}
 tidy4 <- gather(table4, "year", "cases", 2:3)
 tidy5 <- gather(table5, "year", "population", 2:3)
-left_join(tidy4, tidy5)
+dplyr::left_join(tidy4, tidy5)
 ```
 
 ## `separate()` and `unite()`