Import Excel files from the web

When working with multiple data sources, .xls is a common format for storage and exchange. You have been provided with a URL which you'll be using to download and import a .xls file. The URL is already available in the sample code. Once downloaded, you will import the file using readxl and inspect its content.

This exercise is part of the course

Intermediate Importing Data in R

Exercise instructions

Load the readxl package. It is already installed on DataCamp's servers.
Use download.file() to download the .xls file behind the URL and store it locally as "local_latitude.xls".
Import the .xls file located at the URL url_xls using read_excel(). Store the resulting data frame as excel_readxl.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load the readxl package


# Specification of url: url_xls
url_xls <- "https://assets.datacamp.com/production/course_1478/datasets/latitude.xls"

# Download file behind URL, name it local_latitude.xls


# Import the local .xls file

Edit and Run Code

This exercise is part of the course

Intermediate Importing Data in R

IntermediateSkill Level

4.9+

Start Course for 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 and how to retrieve data from it.

Exercise 1: Connect to a database Exercise 2: Establish a connection Exercise 3: Inspect the connection Exercise 4: Import table data Exercise 5: List the database tables Exercise 6: Import users Exercise 7: Import all tables Exercise 8: How do the tables relate?

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.

Exercise 1: SQL Queries from inside R Exercise 2: Query tweater (1)Exercise 3: Query tweater (2)Exercise 4: Query tweater (3)Exercise 5: Query tweater (4)Exercise 6: Join the query madness!Exercise 7: DBI internals Exercise 8: Send - Fetch - Clear Exercise 9: Be polite and ...

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.

Exercise 1: HTTP Exercise 2: Import flat files from the web Exercise 3: Secure importing Exercise 4: Downloading files Exercise 5: Import Excel files from the web

Current Exercise

Exercise 6: Downloading any file, secure or not Exercise 7: Reading a text file from the web Exercise 8: HTTP? httr! (1)Exercise 9: HTTP? httr! (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.

Exercise 1: APIs & JSON Exercise 2: From JSON to R Exercise 3: Import JSON data from API Exercise 4: OMDb API Exercise 5: JSON & jsonlite Exercise 6: JSON practice (1)Exercise 7: JSON practice (2)Exercise 8: toJSON()Exercise 9: Minify and prettify

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!

Exercise 1: haven Exercise 2: Import SAS data with haven Exercise 3: Import STATA data with haven Exercise 4: What does the graphic tell?Exercise 5: Import SPSS data with haven Exercise 6: Factorize, round two Exercise 7: foreign Exercise 8: Import STATA data with foreign (1)Exercise 9: Import STATA data with foreign (2)Exercise 10: Do you know your data?Exercise 11: Import SPSS data with foreign (1)Exercise 12: Excursion: Correlation Exercise 13: Import SPSS data with foreign (2)