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Intermediate Importing Data in R

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

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3 Hours10 Videos48 Exercises70,926 Learners3950 XPData Analyst TrackData Scientist TrackImporting & Cleaning Data Track

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Course Description

In this course, you will take a deeper dive into the wide range of data formats out there. More specifically, you'll learn how to import data from relational databases and how to import and work with data coming from the web. Finally, you'll get hands-on experience with importing data from statistical software packages such as SAS, STATA, and SPSS.

  1. 1

    Importing data from databases (Part 1)

    Free

    Many companies store their information in relational databases. The R community has also developed R packages to get data from these architectures. You'll learn how to connect to a database and how to retrieve data from it.

    Play Chapter Now
    Connect to a database
    50 xp
    Establish a connection
    100 xp
    Inspect the connection
    50 xp
    Import table data
    50 xp
    List the database tables
    100 xp
    Import users
    100 xp
    Import all tables
    100 xp
    How do the tables relate?
    50 xp
  2. 2

    Importing data from databases (Part 2)

    Importing an entire table from a database while you might only need a tiny bit of information seems like a lot of unncessary work. In this chapter, you'll learn about SQL queries, which will help you make things more efficient by performing some computations on the database side.

    Play Chapter Now
    SQL Queries from inside R
    50 xp
    Query tweater (1)
    100 xp
    Query tweater (2)
    100 xp
    Query tweater (3)
    100 xp
    Query tweater (4)
    100 xp
    Join the query madness!
    50 xp
    DBI internals
    50 xp
    Send - Fetch - Clear
    100 xp
    Be polite and ...
    100 xp
  3. 3

    Importing data from the web (Part 1)

    More and more of the information that data scientists are using resides on the web. Importing this data into R requires an understanding of the protocols used on the web. In this chapter, you'll get a crash course in HTTP and learn to perform your own HTTP requests from inside R.

    Play Chapter Now
    HTTP
    50 xp
    Import flat files from the web
    100 xp
    Secure importing
    100 xp
    Downloading files
    50 xp
    Import Excel files from the web
    100 xp
    Downloading any file, secure or not
    100 xp
    Reading a text file from the web
    50 xp
    HTTP? httr! (1)
    100 xp
    HTTP? httr! (2)
    100 xp
  4. 4

    Importing data from the web (Part 2)

    Importing data from the web is one thing; actually being able to extract useful information is another. Learn more about the JSON format to get one step closer to web domination.

    Play Chapter Now
    APIs & JSON
    50 xp
    From JSON to R
    100 xp
    Quandl API
    100 xp
    OMDb API
    100 xp
    JSON & jsonlite
    50 xp
    JSON practice (1)
    100 xp
    JSON practice (2)
    100 xp
    toJSON()
    100 xp
    Minify and prettify
    100 xp
  5. 5

    Importing data from statistical software packages

    Next to R, there are also other commonly used statistical software packages: SAS, STATA and SPSS. Each of them has their own file format. Learn how to use the haven and foreign packages to get them into R with remarkable ease!

    Play Chapter Now
    haven
    50 xp
    Import SAS data with haven
    100 xp
    Import STATA data with haven
    100 xp
    What does the graphic tell?
    50 xp
    Import SPSS data with haven
    100 xp
    Factorize, round two
    100 xp
    foreign
    50 xp
    Import STATA data with foreign (1)
    100 xp
    Import STATA data with foreign (2)
    100 xp
    Do you know your data?
    50 xp
    Import SPSS data with foreign (1)
    100 xp
    Excursion: Correlation
    50 xp
    Import SPSS data with foreign (2)
    100 xp

In the following tracks

Data Analyst Data ScientistImporting & Cleaning Data

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Prerequisites

Introduction to Importing Data in R
Filip Schouwenaars Headshot

Filip Schouwenaars

Data Science Instructor at DataCamp

Filip is the passionate developer behind several of DataCamp's most popular Python, SQL, and R courses. Currently, Filip leads the development of DataCamp Workspace. Under the motto 'Eat your own dog food', he uses the techniques DataCamp teaches its students to understand how users learn on and interact with DataCamp. Filip holds degrees in Electrical Engineering and Artificial Intelligence.
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