Data cleaning is a necessary skill for anyone who wants to work in a data-related field.
You’ll start this course by learning how to identify data cleaning needs prior to analysis, how to use functionals for data cleaning, how to practice string manipulation, how to work with relational data, and how to reshape data using tools from the tidyverse. You’ll create correlation matrices to identify trends in your data, and then you’ll then learn how to deal with missing values in your dataset.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll work on a guided project to analyze parents’, students’, and teachers’ perceptions of NYC schools. You’ll learn to work with survey data — specifically how to import, simplify, and reshape the data. You’ll also learn about R Notebooks and how you can use them to showcase your work.
- Manipulating DataFrames with new tools
- Resolving missing data
- Joining DataFrames
- Reshaping data using the tidyr package
Introduction to Data Cleaning in R [6 lessons]
Projects in this course
The Dataquest guarantee
Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.
We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.