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Introduction to Importing Data in R

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3 hours
3,550 XP
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Course Description

Importing data into R should be the easiest step in your analysis. Unfortunately, that is almost never the case. Data can come in many formats, ranging from .csv and text files, to statistical software files, to databases and HTML data. Knowing which approach to use is key to getting started with the actual analysis. In this course, you’ll start by learning how to read .csv and text files in R. You will then cover the readr and data.table packages to easily and efficiently import flat file data. After that, you will learn how to read .xls files in R using readxl and gdata.
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  1. 1

    Importing data from flat files with utils


    A lot of data comes in the form of flat files: simple tabular text files. Learn how to import the common formats of flat file data with base R functions.

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    Introduction & read.csv
    50 xp
    100 xp
    100 xp
    Any changes?
    50 xp
    read.delim & read.table
    50 xp
    100 xp
    100 xp
    100 xp
    Column classes
    100 xp
    Final Thoughts
    50 xp
  2. 2

    readr & data.table

    In addition to base R, there are dedicated packages to easily and efficiently import flat file data. We'll talk about two such packages: readr and data.table.

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  3. 3

    Importing Excel data

    Excel is a widely used data analysis tool. If you prefer to do your analyses in R, though, you'll need an understanding of how to import .csv data into R. This chapter will show you how to use readxl and gdata to do so.

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  4. 4

    Reproducible Excel work with XLConnect

    Beyond importing data from Excel, you can take things one step further with XLConnect. Learn all about it and bridge the gap between R and Excel.

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In the following tracks

Associate Data ScientistImporting & Cleaning Data


Introduction to R
Filip Schouwenaars HeadshotFilip 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, a collaborative data science notebook. Under the motto 'Eat your own dog food', he uses Workspace to understand how users learn on and interact with DataCamp. Filip holds degrees in Electrical Engineering and Artificial Intelligence.
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