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.

This exercise is part of the course

Joining Data with dplyr

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Exercise instructions

Use replace_na to replace NAs in the n column with the value 0.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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
  ___

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