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Web Scraping in R

Learn how to efficiently collect and download data from any website using R.

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4 Hours13 Videos45 Exercises9,261 Learners3600 XPR Programmer Track

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Course Description

Have you ever come across a website that displays a lot of data such as statistics, product reviews, or prices in a format that’s not data analysis-ready? Often, authorities and other data providers publish their data in neatly formatted tables. However, not all of these sites include a download button, but don’t despair. In this course, you’ll learn how to efficiently collect and download data from any website using R. You'll learn how to automate the scraping and parsing of Wikipedia using the rvest and httr packages. Through hands-on exercises, you’ll also expand your understanding of HTML and CSS, the building blocks of web pages, as you make your data harvesting workflows less error-prone and more efficient.
  1. 1

    Introduction to HTML and Web Scraping

    Free

    In this chapter, you'll be introduced to Hyper Text Markup Language (HTML), a declarative language used to structure modern websites. Using the rvest library, you'll learn how to query simple HTML elements and scrape your first table.

    Play Chapter Now
    Introduction to HTML
    50 xp
    Read in HTML
    100 xp
    Beware of syntax errors!
    50 xp
    Navigating HTML
    50 xp
    Select all children of a list
    100 xp
    Parse hyperlinks into a data frame
    100 xp
    Scrape your first table
    50 xp
    The right order of table elements
    100 xp
    Turn a table into a data frame with html_table()
    100 xp
  2. 2

    Navigation and Selection with CSS

    Cascading Style Sheets (CSS) describe how HTML elements are displayed on a web page, including colors, fonts, and general layout. In this chapter, you'll learn why CSS selectors and combinators are a crucial ingredient for web scraping.

    Play Chapter Now
    Introduction to CSS
    50 xp
    Select multiple HTML types
    100 xp
    Order CSS selectors by the number of results
    100 xp
    CSS classes and IDs
    50 xp
    Identify the correct selector types
    100 xp
    Leverage the uniqueness of IDs
    100 xp
    Select the last child with a pseudo-class
    100 xp
    CSS combinators
    50 xp
    Select direct descendants with the child combinator
    100 xp
    How many elements get returned?
    50 xp
    Simply the best!
    100 xp
    Not every sibling is the same
    100 xp
  3. 3

    Advanced Selection with XPATH

    The CSS selectors you got to know in the last chapter are powerful but have their limitations. For example, if you want to select nodes based on the properties of their descendants. XPath to the rescue! Using this query language, you can navigate and scrape even the most hideous HTML.

    Play Chapter Now
    Introduction to XPATH
    50 xp
    Find the correct CSS equivalent
    100 xp
    Select by class and ID with XPATH
    100 xp
    Use predicates to select nodes based on their children
    100 xp
    XPATH functions and advanced predicates
    50 xp
    Find a more elegant XPATH alternative
    50 xp
    Get to know the position() function
    100 xp
    Extract nodes based on the number of their children
    100 xp
    The XPATH text() function
    50 xp
    The shortcomings of html_table() with badly structured tables
    100 xp
    Select directly from a parent element with XPATH's text()
    100 xp
    Combine extracted data into a data frame
    100 xp
    Scrape an element based on its text
    100 xp
  4. 4

    Scraping Best Practices

    Now that you know how to extract content from web pages, it's time to look behind the curtains. In this final chapter, you’ll learn why HTTP requests are the foundation of every scraping action and how they can be customized to comply with best practices in web scraping.

    Play Chapter Now
    The nature of HTTP requests
    50 xp
    Which of these statements about HTTP is false?
    50 xp
    Do it the httr way
    100 xp
    Houston, we got a 404!
    100 xp
    Telling who you are with custom user agents
    50 xp
    Check out your user agent
    100 xp
    Add a custom user agent
    100 xp
    How to be gentle and slow down your requests
    50 xp
    Custom arguments for throttled functions
    50 xp
    Apply throttling to a multi-page crawler
    100 xp
    Recap: Web Scraping in R
    50 xp

In the following tracks

R Programmer

Collaborators

Amy Peterson
Maggie Matsui

Prerequisites

Intermediate RIntroduction to the Tidyverse
Timo Grossenbacher Headshot

Timo Grossenbacher

Head of Newsroom Automation at Tamedia

Timo Grossenbacher is Head of Newsroom Automation at Swiss publisher Tamedia. Prior to that, he used to be a data journalist working with the Swiss Public Broadcast (SRF), where he used scripting and databases for almost every data-driven story he published. He also teaches data journalism at the University of Zurich and is the creator of rddj.info – resources for doing data journalism with R. Follow him at grssnbchr on Twitter or visit his personal website.
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