All the data we need today is already available on the internet, which is great news for data scientists. The only barrier to using this data is the ability to access it. There are some platforms that even include APIs (such as Twitter) that support data collection from web pages, but it is not possible to crawl most web pages using this advantage.

Before we go on to scrape the web with R, we need to specify that this is advanced data analysis, data collection. We will use the Hadley Wickham's method for web scraping using rvest. The package also requires selectr and xml2 packages.

The way to operate the rvest pole is simple and straightforward. Just as we first made web pages manually, the rvest package defines the web page link as the first step. After that, appropriate labels have to be defined. The HTML language edits content using various tags and selectors. These selectors must be identified and marked for storage of their contents by the harvest package. Then, all the engraved data can be transformed into an appropriate dataset, and analysis can be performed.

In this section, we will discuss in detail how fast and practical it is to use R for web scraping. After this section, you will gain expertise in using R to collect data over the internet.

The topics to be covered in this chapter are as follows:

• Introducing rvest
• Step-by-step web scraping with rvest