Filip Schouwenaars
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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Prerequisites

Course Description

Importing data into R to start your analyses—it should be the easiest step. Unfortunately, this is almost never the case. Data come in all sorts of formats, ranging from CSV and text files and 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 will get started with 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

    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.

  2. readr & data.table

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

  3. Importing Excel data

    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 show you how to use readxl and gdata to do so.

  4. Reproducible Excel work with XLConnect

    Next to 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!