Summarising Data

Generating summaries with summarise and group_by

In this section, we will calculate a summary value for a column. We will then group the output based on another column.

For example, we will get the mean value of mpg for each of the cylinders. There are 3 unique values in the cylinder column (4, 6, 8).

In tidyverse, we can use a summarise or summarize function. In the example below, we take the mean of mpg, by specifying mean(mpg) and then we save it as average_mpg.

summarise

mtcars |> filter(cyl == 4) |> summarise(average_mpg = mean(mpg)) mtcars |> filter(cyl == 6) |> summarise(average_mpg = mean(mpg)) mtcars |> filter(cyl == 8) |> summarise(average_mpg = mean(mpg))

Grouping and Summarising

Instead of doing this three times, we can get a single table. There may be uses for getting the values separately. The group_by function offers a simple way to get things all at once.

summarise

mtcars |> group_by(cyl) |> summarise(average_mpg = mean(mpg))

Exercise

In the mtcars dataset, calculate the mean value of the weight (wt) grouped by the carb column. Try group_by(carb) and average_weight = mean(wt).

summarise

mtcars |> group_by() |> summarise(average_weight = mean())

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