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Importing Data Into R

Learn how to parse data in any format. Whether it's flat files, statistics software, databases, or web data, you'll handle it all.

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4 Hours11 Videos68 Exercises28,488 Learners
5850 XP

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Course Description

Importing data into R to start your analyses: it should be the easiest step. Unfortunately, this is almost never the case. Data can come in all sorts of formats, ranging from flat files and statistical software files to databases and web data. Knowing which approach to use is key to getting started with the actual analysis. In this course, you will learn all the basics on how to load data into R so you can get up and running in no time!

  1. 1

    Importing data from flat files

    Free

    Lots of data comes in the form of flat files: simple tabular text files. Learn how to import all common formats of flat file data with base R functions and the dedicated readr and data.table packages.

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    Introduction & Flat Files
    50 xp
    read.csv
    100 xp
    read.delim
    100 xp
    read.table
    100 xp
    stringsAsFactors
    100 xp
    Any changes?
    50 xp
    Arguments
    100 xp
    Column classes
    100 xp
    readr & data.table
    50 xp
    read_delim
    100 xp
    read_csv
    100 xp
    col_types, skip and n_max
    100 xp
    col_types with collectors
    100 xp
    fread
    100 xp
    fread: more advanced use
    100 xp
    Dedicated classes
    50 xp
  2. 2

    Importing data from Excel

    Free

    Excel is a very widely used data analysis tool. If you prefer to do your analyses in R, though, you'll need an understanding of importing CSV data into R. This chapter will explain you how to use readxl and gdata to do so. The XLConnect package that takes all of this one step further, will also be discussed.

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  3. 4

    Importing data from relational databases

    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, how to retrieve data from it, and how to make things more efficient by performing a part of your computations on the database side.

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  4. 5

    Importing data from the web

    Free

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

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Collaborators

Emna ChahedFrauke Hein
Filip Schouwenaars Headshot

Filip Schouwenaars

Data Science Instructor at DataCamp

Filip is the passionate developer behind several of DataCamp's interactive courses, covering both R and Python. Under the motto 'Eat your own dog food', he has used the techniques DataCamp teaches its students to perform data analysis for DataCamp. Filip holds degrees in Electrical Engineering and Artificial Intelligence.
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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.

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Harvard Business School

DataCamp is by far my favorite website to learn from.

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Decision Science Analytics, USAA