Cleaning Data in R
Learn to explore your data so you can properly clean and prepare it for analysis.
- 4 hours
- 15 Videos
- 58 Exercises
- 101,513 Participants
- 4,700 XP
Create Your Free Account
By pressing Continue you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.
Loved by learners at thousands of top companies:
Course Description
It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it. For this reason, it is critical to become familiar with the data cleaning process and all of the tools available to you along the way. This course provides a very basic introduction to cleaning data in R using the tidyr, dplyr, and stringr packages. After taking the course you'll be able to go from raw data to awesome insights as quickly and painlessly as possible!
-
1
Introduction and exploring raw data
FreeThis chapter will give you an overview of the process of data cleaning with R, then walk you through the basics of exploring raw data.
-
Preparing data for analysis
This chapter will teach you how to prepare your data for analysis. We will look at type conversion, string manipulation, missing and special values, and outliers and obvious errors.
-
Tidying data
This chapter will give you an overview of the principles of tidy data, how to identify messy data, and what to do about it.
-
Putting it all together
In this chapter, you will practice everything you've learned from the first three chapters in order to clean a messy dataset using R.
-
1
Introduction and exploring raw data
FreeThis chapter will give you an overview of the process of data cleaning with R, then walk you through the basics of exploring raw data.
-
Tidying data
This chapter will give you an overview of the principles of tidy data, how to identify messy data, and what to do about it.
-
Preparing data for analysis
This chapter will teach you how to prepare your data for analysis. We will look at type conversion, string manipulation, missing and special values, and outliers and obvious errors.
-
Putting it all together
In this chapter, you will practice everything you've learned from the first three chapters in order to clean a messy dataset using R.
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics @ USAA
Nick Carchedi
Product Manager at DataCamp
Nick is a Product Manager at DataCamp. Prior to joining DataCamp, he earned his master's degree at Johns Hopkins Biostatistics and worked as a data scientist for McKinsey. Nick's passion for teaching data science began in graduate school, where he was heavily involved in tutoring fellow students, developing the Johns Hopkins Data Science Specialization, and building the swirl R package.
See MoreCollaborators
-
Jeff Paadre