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Cleaning Data in R

Develop the skills you need to go from raw data to awesome insights as quickly and accurately as possible.

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4 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. 1

    Common Data Problems

    Free

    In 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.

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    Data type constraints
    50 xp
    Common data types
    100 xp
    Converting data types
    100 xp
    Trimming strings
    100 xp
    Range constraints
    50 xp
    Ride duration constraints
    100 xp
    Back to the future
    100 xp
    Uniqueness constraints
    50 xp
    Full duplicates
    100 xp
    Removing partial duplicates
    100 xp
    Aggregating partial duplicates
    100 xp
  2. 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.

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  3. 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.

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  4. 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.

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In the following tracks

Data ScientistImporting & Cleaning Data

Collaborators

richieRichie CottonAAN94Adel Nehmeamy-4121b590-cc52-442a-9779-03eb58089e08Amy Peterson
Maggie Matsui Headshot

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.
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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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