Summarizing data
Let's now make a faceted plot to compare usefulness across different learning platforms.
In this exercise, we'll introduce a new dplyr
function, add_count()
. add_count()
adds a column to the dataset, n
, keeping the same number of rows as the original dataset. Just like count()
, n
defaults to be the number of rows for each group, but you can change that with the wt
(weight) argument. You set wt
equal to another column to make n
now equal to the sum of that column for each group.
Let's say you wanted to add a column to iris
that is the sum of the Petal.Length
for all the flowers of the same Species
. You would write:
iris %>%
add_count(Species, wt = Petal.Length) %>%
select(Species, Petal.Length, n)
This would give you back:
# A tibble: 150 x 3
Species Petal.Length n
<fct> <dbl> <dbl>
1 setosa 1.4 73.1
2 setosa 1.4 73.1
3 virginica 6.4 278.
This exercise is part of the course
Categorical Data in the Tidyverse
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
learning_platform_usefulness %>%
# Change dataset to one row per learning_platform usefulness pair with number of entries for each
___(___, ___)