While group_by() is most often used with summarize() to calculate summary statistics, it can also be used with the dplyr function filter() to filter rows of a data frame based on per-group metrics.

Suppose you work at an educational technology company that offers online courses and collects user data in an enrollments data frame:

You want to identify all the enrollments in difficult courses, which you define as courses with an average quiz_score less than 80. To filter the data frame to just these rows:

enrollments %>%
  group_by(course) %>%
  filter(mean(quiz_score) < 80)

• group_by() groups the data frame by course into two groups: learn-r and learn-python
• filter() will keep all the rows of the data frame whose per-group (per-course) average quiz_score is less than 80

Rather than filtering rows by the individual column values, the rows will be filtered by their group value since a summary function is used! The resulting data frame would look like this:

• The average quiz_score for the learn-r course is 85, so all the rows of enrollments with a value of learn-r in the course column are filtered out.
• The average quiz_score for the learn-python course is 75, so all the rows of enrollments with a value of learn-python in the course column remain.