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Cleaning Data with R and the Tidyverse in Swirl

Author(s): Rachel Hartnett

Mount St. Mary's University

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The overall goal is to help students learn common data cleaning procedures on a dataset once they’ve collected measurements and before they are able to start their analysis. This swirl lesson is designed specifically to deal with missing data.

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Version 1.0 - published on 09 Jun 2020 doi:10.25334/SKPR-CB95 - cite this


This swirl lesson is designed to provide a baseline of knowledge for what steps need to be taken to deal with missing data, and how to do that in R with the tidyverse package. By the end of the lesson, students should be able to provide a checklist for cleaning their datasets of missing data, be able to modify a dataset in R with missing data, and be able to export that dataset into a .csv file. It is recommended that students use R studio for this lesson as some of those features are necessary for the lesson.


This lesson is designed to be completed within ~35 minutes during class; however due to COVID-19 disruptions to in-person classes, this lesson was implemented online. Future modifications of this lesson could include more advanced visualizations and include different datasets that require different assumptions to be met or need to be cleaned in different ways. In addition, I hope to add additional lessons in order to include more data cleaning steps.




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