Cleaning up strings

In this lesson, we learned some basics of "regex," or regular expressions, which allow us to capture general patterns. We've covered two notations:

Expression	Does this
`.`	matches any character
`*`	zero or more times

For example, ".*science " would match "data science " in the string "data science rocks!"

Let's use what we've learned to change the response_var in the dataset you created in the previous lesson, gathered_data.

This exercise is part of the course

Categorical Data in the Tidyverse

View Course

Exercise instructions

Use str_remove to remove everything before and including "rude to " (with the space at the end) in the response_var column.
Use str_remove to remove "on a plane" from the response_var column.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

gathered_data %>%
    # Remove everything before and including "rude to " (with that space at the end!)
    mutate(response_var = ___(response_var, ___)) %>%
    # Remove "on a plane"
    mutate(response_var = ___(response_var, ___))

Edit and Run Code