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()

Introduction to HTML and Web Scraping

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

Navigation and Selection with CSS

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

Advanced Selection with XPATH

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.

La natura delle richieste HTTP

Quale di queste affermazioni su HTTP è falsa?

Fallo con httr

Houston, abbiamo un 404!

Dire chi sei con user agent personalizzati

Dai un'occhiata al tuo user agent

Aggiungi un user agent personalizzato

Come essere gentile e rallentare le tue richieste

Argomenti personalizzati per funzioni con throttling

Applica il throttling a un crawler multi-pagina

Riepilogo: Web Scraping in R

Scraping Best Practices

Ti è mai capitato di trovare un sito pieno di dati—statistiche, recensioni di prodotti o prezzi—ma in un formato poco adatto all’analisi? Spesso enti e altri fornitori pubblicano i dati in tabelle ben formattate. Tuttavia, non tutti questi siti includono un pulsante di download: niente panico. In questo corso imparerai a raccogliere e scaricare in modo efficiente dati da qualsiasi sito web usando R. Vedrai come automatizzare lo scraping e il parsing di Wikipedia con i pacchetti rvest e httr. Con esercizi pratici, approfondirai anche HTML e CSS, i mattoni delle pagine web, rendendo i tuoi flussi di raccolta dati meno soggetti a errori e più efficienti.

Intermediate R

Introduction to the Tidyverse

Impara a usare R per raccogliere dati da siti web e automatizzare lo scraping di Wikipedia con rvest e httr.

Web scraping in R

Scopri come raccogliere e scaricare dati da qualsiasi sito web in modo efficiente usando R.

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