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Importing & Cleaning Data in R: Case Studies

In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.

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Course Description

Running exciting analyses on interesting datasets is the dream of every data scientist. But first, some importing and cleaning must be done. In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.

  1. 1

    Ticket Sales Data


    Hone your skills by importing and cleaning some wonderfully messy online ticket sales data.

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    Importing the data
    100 xp
    Examining the data
    100 xp
    Summarizing the data
    100 xp
    Removing redundant info
    100 xp
    Information not worth keeping
    100 xp
    Separating columns
    100 xp
    Dealing with warnings
    100 xp
    Identifying dates
    100 xp
    More warnings!
    100 xp
    Combining columns
    100 xp
  2. 3

    World Food Facts


    We all know that you are what you eat, so what exactly are you? In this chapter, you'll import and clean some data about food products from around the world.

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  3. 4

    School Attendance Data


    Use all of the tools you've learned to import and clean a gnarly dataset containing information on average school attendance in the US.

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Tom JeonJim Looney
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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.
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