Cleaning Data in R
Develop the skills you need to go from raw data to awesome insights as quickly and accurately as possible.
Start Course for Free4 Hours13 Videos44 Exercises31,395 Learners3700 XPData Scientist TrackImporting & Cleaning Data Track
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Course Description
It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it. The time spent cleaning is vital since analyzing dirty data can lead you to draw inaccurate conclusions.
In this course, you'll learn how to clean dirty data. Using R, you'll learn how to identify values that don't look right and fix dirty data by converting data types, filling in missing values, and using fuzzy string matching. As you learn, you’ll brush up on your skills by working with real-world datasets, including bike-share trips, customer asset portfolios, and restaurant reviews—developing the skills you need to go from raw data to awesome insights as quickly and accurately as possible!
- 1
Common Data Problems
FreeIn this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.
- 2
Categorical and Text Data
Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.
- 3
Advanced Data Problems
In this chapter, you’ll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You’ll also gain invaluable skills that will help you verify that values have been added correctly and that missing values don’t negatively impact your analyses.
- 4
Record Linkage
Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you’ll then use your new skills to join two restaurant review datasets into one clean master dataset.
Comparing strings50 xpCalculating distance50 xpSmall distance, small difference100 xpFixing typos with string distance100 xpGenerating and comparing pairs50 xpLink or join?100 xpPair blocking100 xpComparing pairs100 xpScoring and linking50 xpScore then select or select then score?100 xpPutting it together100 xpCongratulations!50 xp
Richie Cotton
Adel Nehme
Amy PetersonPrerequisites
Joining Data with dplyr
Maggie Matsui
Curriculum Manager at DataCamp
Maggie is a Curriculum Manager at DataCamp. She holds a Bachelor's degree in Statistics and Computer Science from Brown University, where she spent lots of time teaching math, programming, and statistics as a tutor and teaching assistant. She's passionate about teaching all things data-related and making programming accessible to everyone.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds 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
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