Add workflow advice from @jennybc

transform.Rmd

@@ -558,7 +558,8 @@ flights %>%
 In this case, where missing values represent cancelled flights, we could also tackle the problem by first removing the cancelled flights. We'll save this dataset so we can reuse in the next few examples.
 
 ```{r}
-not_cancelled <- filter(flights, !is.na(dep_delay), !is.na(arr_delay))
+not_cancelled <- flights %>% 
+  filter(!is.na(dep_delay), !is.na(arr_delay))
 
 not_cancelled %>% 
   group_by(year, month, day) %>%