rvestvswombat
rvest is a popular R library for web scraping and parsing HTML and XML documents. It is built on top of the xml2 and httr libraries and provides a simple and consistent API for interacting with web pages.
One of the main advantages of using rvest is its simplicity and ease of use. It provides a number of functions that make it easy to extract information from web pages, even for those who are not familiar with web scraping. The html_nodes and html_node functions allow you to select elements from an HTML document using CSS selectors, similar to how you would select elements in JavaScript.
rvest also provides functions for interacting with forms, including html_form, set_values, and submit_form functions. These functions make it easy to navigate through forms and submit data to the server, which can be useful when scraping sites that require authentication or when interacting with dynamic web pages.
rvest also provides functions for parsing XML documents. It includes xml_nodes and xml_node functions, which also use CSS selectors to select elements from an XML document, as well as xml_attrs and xml_attr functions to extract attributes from elements.
Another advantage of rvest is that it provides a way to handle cookies, so you can keep the session alive while scraping a website, and also you can handle redirections with handle_redirects
Wombat is a Ruby gem that makes it easy to scrape websites and extract structured data from HTML pages. It is built on top of Nokogiri, a popular Ruby gem for parsing and searching HTML and XML documents, and it provides a simple and intuitive API for defining and running web scraping operations.
One of the main features of Wombat is its ability to extract structured data from HTML pages using a simple, CSS-like syntax. It allows you to define a set of rules for extracting data from a page, and then automatically applies those rules to the page's HTML to extract the desired data. This makes it easy to extract data from even complex and dynamic pages, without having to write a lot of custom code.
In addition to its data extraction capabilities, Wombat also provides a variety of other features that can simplify the web scraping process. It can automatically follow links and scrape multiple pages, it can handle pagination and AJAX requests, and it can handle cookies and authentication. It also provides a built-in support for parallelism and queueing to speed up the scraping process.
Example Use
library("rvest")
# Rvest can use basic HTTP client to download remote HTML:
tree <- read_html("http://webscraping.fyi/lib/r/rvest")
# or read from string:
tree <- read_html('
<div class="products">
<a href="/product/1">Cat Food</a>
<a href="/product/2">Dog Food</a>
</div>
')
# to parse HTML trees with rvest we use r pipes (the %>% symbol) and html_element function:
# we can use css selectors:
print(tree %>% html_element(".products>a") %>% html_text())
# "[1] "\nCat Food\nDog Food\n""
# or XPath:
print(tree %>% html_element(xpath="//div[@class='products']/a") %>% html_text())
# "[1] "\nCat Food\nDog Food\n""
# Additionally rvest offers many quality of life functions:
# html_text2 - removes trailing and leading spaces and joins values
print(tree %>% html_element("div") %>% html_text2())
# "[1] "Cat Food Dog Food""
# html_attr - selects element's attribute:
print(tree %>% html_element("div") %>% html_attr('class'))
# "products"
require 'wombat'
Wombat.crawl do
base_url "https://www.github.com"
path "/"
headline xpath: "//h1"
subheading css: "p.alt-lead"
what_is({ css: ".one-fourth h4" }, :list)
links do
explore xpath: '/html/body/header/div/div/nav[1]/a[4]' do |e|
e.gsub(/Explore/, "Love")
end
features css: '.nav-item-opensource'
business css: '.nav-item-business'
end
end
{
"headline"=>"How people build software",
"subheading"=>"Millions of developers use GitHub to build personal projects, support their businesses, and work together on open source technologies.",
"what_is"=>[
"For everything you build",
"A better way to work",
"Millions of projects",
"One platform, from start to finish"
],
"links"=>{
"explore"=>"Love",
"features"=>"Open source",
"business"=>"Business"
}
}