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Introduction to Importing Data in R

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

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3 Hours11 Videos42 Exercises171,360 Learners3550 XPData Scientist with R TrackImporting & Cleaning Data with R Track

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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.
  1. 1

    Importing data from flat files with utils

    Free

    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.

    Play Chapter Now
    Introduction & read.csv
    50 xp
    read.csv
    100 xp
    stringsAsFactors
    100 xp
    Any changes?
    50 xp
    read.delim & read.table
    50 xp
    read.delim
    100 xp
    read.table
    100 xp
    Arguments
    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.

    Play Chapter Now
    readr: read_csv & read_tsv
    50 xp
    read_csv
    100 xp
    read_tsv
    100 xp
    readr: read_delim
    50 xp
    read_delim
    100 xp
    skip and n_max
    100 xp
    col_types
    100 xp
    col_types with collectors
    100 xp
    data.table: fread
    50 xp
    fread
    100 xp
    fread: more advanced use
    100 xp
    Dedicated classes
    50 xp
  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.

    Play Chapter Now
    readxl (1)
    50 xp
    List the sheets of an Excel file
    100 xp
    Import an Excel sheet
    100 xp
    Reading a workbook
    100 xp
    readxl (2)
    50 xp
    The col_names argument
    100 xp
    The skip argument
    100 xp
    gdata
    50 xp
    Import a local file
    100 xp
    read.xls() wraps around read.table()
    100 xp
    Work that Excel data!
    100 xp
  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.

    Play Chapter Now
    Reading sheets
    50 xp
    Connect to a workbook
    100 xp
    List and read Excel sheets
    100 xp
    Customize readWorksheet
    100 xp
    Adapting sheets
    50 xp
    Add worksheet
    100 xp
    Populate worksheet
    100 xp
    Renaming sheets
    100 xp
    Removing sheets
    100 xp

In the following tracks

Data Scientist with RImporting & Cleaning Data with R

Datasets

HotdogsPotatoes (CSV)Potatoes (TSV)Swimming poolsUrban population (XLS)Urban population (XLSX)

Prerequisites

Introduction to 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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