Intermediate Importing Data in R
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
- 3 hours
- 10 Videos
- 48 Exercises
- 50,892 Participants
- 3,950 XP
Create Your Free Account
By pressing Continue you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.
Loved by learners at thousands of top companies:
Course Description
In this second part to Importing Data in R, 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 SAS, STATA and SPSS.
-
1
Importing data from databases (Part 1)
FreeMany 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.
-
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.
-
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!
-
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.
-
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.
-
1
Importing data from databases (Part 1)
FreeMany 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.
-
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.
-
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.
-
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.
-
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!
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics @ USAA
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.
See More