Cleaning up your count
In both left and right joins, there is the opportunity for there to be NA values in the resulting table. Fortunately, the replace_na function can turn those NAs into meaningful values.
In the last exercise, we saw that the n column had NAs after the right_join. Let's use the replace_na column, which takes a list of column names and the values with which NAs should be replaced, to clean up our table.
Questo esercizio fa parte del corso
Joining Data with dplyr
Istruzioni dell'esercizio
- Use
replace_nato replace NAs in thencolumn with the value0.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
parts %>%
count(part_cat_id) %>%
right_join(part_categories, by = c("part_cat_id" = "id")) %>%
# Use replace_na to replace missing values in the n column
___