When we have a bunch of data, we often want to calculate aggregate statistics (mean, standard deviation, median, percentiles, etc.) over certain subsets of the data.

Suppose we have a grade book with columns student, assignment_name, and grade:

We want to get an average grade for each student across all assignments. We can do this using the helpful dplyr function group_by().

For this example, we’d use the following piece of code:

grades <- df %>%
  group_by(student) %>%
  summarize(mean_grade = mean(grade))

The output might look something like this:

In general, we use the following syntax to calculate aggregates:

df %>%
  group_by(column_1) %>%
  summarize(aggregate_name = command(column_2))

• column_1 (student in our example) is the column that we want to group_by()
• column_2 (grade in our example) is the column that we want to apply command(), a summary function, to using summarize()
• aggregate_name is the name assigned to the calculated aggregate

In addition to the summary functions discussed in the last exercise (mean(), median(), sd(), var(), min(), max(), IQR() and n_distinct()), another helpful summary function, especially for grouped data, is n(). n() will return the count of the rows within a group, and does not require a column as an argument. To get the count of the rows in each group of students from our example:

grades <- df %>%
  group_by(student) %>%
  summarize(count = n())