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Curs

Web Scraping in R

IntermediarNivel de competențe

Actualizat 04.2024

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

Începe cursul gratuit

RData Preparation

4 h

13 videoclipuri

45 Exerciții

3,600 XP

14,989

Certificat de realizare

Îndrăgit de cursanți din mii de companii

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Descrierea cursului

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.

Cerințe prealabile

Intermediate R Introduction to the Tidyverse

1

Introduction to HTML and Web Scraping

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.

Introduction to HTML

Read in HTML

Beware of syntax errors!

Navigating HTML

Select all children of a list

Parse hyperlinks into a data frame

Scrape your first table

The right order of table elements

Turn a table into a data frame with html_table()

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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.

Introduction to CSS

Select multiple HTML types

Order CSS selectors by the number of results

CSS classes and IDs

Identify the correct selector types

Leverage the uniqueness of IDs

Select the last child with a pseudo-class

CSS combinators

Select direct descendants with the child combinator

How many elements get returned?

Simply the best!

Not every sibling is the same

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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.

Introduction to XPATH

Find the correct CSS equivalent

Select by class and ID with XPATH

Use predicates to select nodes based on their children

XPATH functions and advanced predicates

Find a more elegant XPATH alternative

Get to know the position() function

Extract nodes based on the number of their children

The XPATH text() function

The shortcomings of html_table() with badly structured tables

Select directly from a parent element with XPATH's text()

Combine extracted data into a data frame

Scrape an element based on its text

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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.

The nature of HTTP requests

Which of these statements about HTTP is false?

Do it the httr way

Houston, we got a 404!

Telling who you are with custom user agents

Check out your user agent

Add a custom user agent

How to be gentle and slow down your requests

Custom arguments for throttled functions

Apply throttling to a multi-page crawler

Recap: Web Scraping in R

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Web Scraping in R

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