Data Cleaning in R

Great! We have looked at a number of different methods we may use to get data into the format we want for analysis.

Specifically, we have covered:

  • diagnosing the “tidiness” of data
  • combining multiple files
  • reshaping data
  • changing the types of values
  • manipulating strings to represent data better

You can use these methods to transform your datasets to be clean and easy to work with!



The students data frame is nearly cleaned and ready for analysis! There’s one more change that can be made to the ages of the students to help describe and visualize the data. What could that change be? What is the ideal data type for the ages column?

Make the change to age and save the resulting data frame to students.

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