Learn

group_by() can also be used with the dplyr function mutate() to add columns to a data frame that involve per-group metrics.

Consider the same educational technology company’s enrollments table from the previous exercise:

user_id	course	quiz_score
1234	learn_r	80
1234	learn_python	95
4567	learn_r	90
4567	learn_python	55

You want to add a new column to the data frame that stores the difference between a row’s quiz_score and the average quiz_score for that row’s course. To add the column:

enrollments %>% 
  group_by(course) %>% 
  mutate(diff_from_course_mean = quiz_score - mean(quiz_score))

group_by() groups the data frame by course into two groups: learn-r and learn-python
mutate() will add a new column diff_from_course_mean which is calculated as the difference between a row’s individual quiz_score and the mean(quiz_score) for that row’s group (course)

The resulting data frame would look like this:

user_id	course	quiz_score	diff_from_course_mean
1234	learn_r	80	-5
1234	learn_python	95	20
4567	learn_r	90	5
4567	learn_python	55	-20

The average quiz_score for the learn-r course is 85, so diff_from_course_mean is calculated as quiz_score - 85 for all the rows of enrollments with a value of learn-r in the course column.
The average quiz_score for the learn-python course is 75, so diff_from_course_mean is calculated as quiz_score - 75 for all the rows of enrollments with a value of learn-python in the course column.

Instructions

You want to be able to tell how expensive each order is compared to the average price of orders with the same shoe_type.

Group orders by shoe_type and create a new column named diff_from_shoe_type_mean that stores the difference in price between an orders price and the average price of orders with the same shoe_type. Save the result to diff_from_mean, and view it.

Don’t forget to include na.rm = TRUE as an argument in the summary function you call!

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