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rvestvsscrapy

MIT 17 1 1,455
629.3 thousand (month) Nov 22 2014 1.0.4(1 year, 7 months ago)
50,703 30 652 BSD
2.11.1(a month ago) Jul 26 2019 1.6 million (month)

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

Scrapy is an open-source Python library for web scraping. It allows developers to extract structured data from websites using a simple and consistent interface.

Scrapy provides:

  • A built-in way to follow links and extract data from multiple pages (crawling)
  • Handling common web scraping tasks such as logging in, handling cookies, and handling redirects.

Scrapy is built on top of the Twisted networking engine, which provides a non-blocking way to handle multiple requests at the same time, allowing Scrapy to efficiently scrape large websites.

It also comes with a built-in mechanism for handling common web scraping problems, such as:

  • handling HTTP errors
  • handling broken links

Scrapy also provide these features:

  • Support for storing scraped data in various formats, such as CSV, JSON, and XML.
  • Built-in support for selecting and extracting data using XPath or CSS selectors (through parsel).
  • Built-in support for handling common web scraping problems (like deduplication and url filtering).
  • Ability to easily extend its functionality using middlewares.
  • Ability to easily extend output processing using pipelines.

Highlights


popularcss-selectorsxpath-selectorscommunity-toolsoutput-pipelinesmiddlewaresasyncproductionlarge-scale

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"

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