How to Extract Data From Websites With R | Web Scraping Tutorial

Content Introduction

Key Information

• The video introduces how data scientists can use R for web scraping, allowing extraction of static pages, HTML tables, and dynamic content.
• To get started, R and RStudio need to be installed and the 'rvest' package should be imported into the script.
• Users are guided through creating a URL object to specify the webpage to scrape, leading to extracting HTML elements and assigning them to a web page object.
• The process includes identifying the HTML nodes to scrape using tools like right-click 'inspect', selecting nodes based on class names or IDs.
• A data frame is created to store various attributes such as country names, populations, and areas. A loop is utilized to iterate through the values in the selected HTML nodes.
• The video also covers scraping HTML tables using R, mentioning that a similar approach applies, requiring reading the HTML content and parsing tables into variables.
• It addresses scraping JavaScript-rendered pages by using the rvest and tidyverse packages, defining the website and identifying the necessary data.
• Pagination handling is introduced, allowing users to scrape data from multiple pages by iterating through links until there are no more pages.
• The scraped data can be printed and saved in CSV format, with the option to customize file names and include additional columns as needed.