How can I keep pivot_wider() from dropping factor levels in names?

I would really like pivot_wider to create a column with NAs if the level of a factor exists but never appears in the data when it's used as a names_from argument. For example, the first line gives me a two column tibble, but I'd really like the three column tibble below.

tibble(Person=c("Sarah", "Jackson", "Jackson"), Rank=c(1,1,2), 
       FavoriteAnimal=factor(c("Dog", "Dog", "Cat")))%>%
    group_by(Person)%>%arrange(Rank)%>%slice(1)%>%
    pivot_wider(names_from = FavoriteAnimal, values_from=Rank)

tibble(Person=c("Jackson", "Sarah"), Dog=c(1,1), Cat=c(NA,NA))

How can I get my column of NAs for levels not appearing in my dataset?

Alternatively, you can first add the missing levels and then do the transformation:

tibble(Person = c("Sarah", "Jackson", "Jackson"), 
       Rank = c(1, 1, 2), 
       FavoriteAnimal = factor(c("Dog", "Dog", "Cat"))) %>%
 group_by(Person) %>%
 arrange(Rank) %>% 
 slice(1) %>%
 complete(FavoriteAnimal = FavoriteAnimal) %>%
 pivot_wider(names_from = FavoriteAnimal, values_from = Rank)

  Person    Cat   Dog
  <chr>   <dbl> <dbl>
1 Jackson    NA     1
2 Sarah      NA     1

You can do it with tidyr::spread - spread(key = FavoriteAnimal, value = Rank, drop = FALSE) gives you what you want.

Unfortunately the drop argument seems to have been lost in the transition from spread to pivot_wider.