Grouping and reshaping similar columns
In this lesson, we saw how some of the columns in the Kaggle data science survey dataset were tied together, such as columns each addressing the frequency of different work challenges. We usually want to look at those variables together, but first, we need to find them and change them into a format that's easier to use. Let's try the process out with the questions around how useful the survey respondents found different platforms for learning.
The dataset multiple_choice_responses
has been loaded for you.
This exercise is part of the course
Categorical Data in the Tidyverse
Exercise instructions
- Select only the columns with
"LearningPlatformUsefulness"
in the name. - Change the data from wide to long format with two columns,
learning_platform
andusefulness
. - Remove rows where
usefulness
is NA. - Remove
"LearningPlatformUsefulness"
from each string inlearning_platform
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
learning_platform_usefulness <- multiple_choice_responses %>%
# Select columns with LearningPlatformUsefulness in title
___(___("LearningPlatformUsefulness")) %>%
# Change data from wide to long
___(everything(), names_to = "learning_platform", values_to = "usefulness") %>%
# Remove rows where usefulness is NA
___(___()) %>%
# Remove "LearningPlatformUsefulness" from each string in learning_platform
mutate(learning_platform = ___())