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rvestvscrul

MIT 17 1 1,455
629.3 thousand (month) Nov 22 2014 1.0.4(1 year, 7 months ago)
101 1 16 MIT
1.4.0(1 year, 7 months ago) Nov 09 2016 61.7 thousand (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

crul is a R library for sending HTTP requests and web scraping. It is designed to be simple and easy to use, while still providing powerful functionality for working with HTTP requests and scraping web pages.

One of the main features of crul is its intuitive and easy-to-use syntax for sending HTTP requests. It allows you to easily specify the HTTP method, headers, and body of a request, and also provides a simple way to handle the response.

crul also has the ability to handle different types of requests and responses, including GET, POST, PUT, DELETE, and PATCH. It also support for handling redirects, cookies, and authentication.

Another feature of crul is its support for web scraping. The library provides a simple and efficient way to extract data from web pages, using a syntax similar to that of the XML and httr libraries. It also allows to easily filter the extracted data based on a specific criteria.

crul also supports parallel scraping, which allows to make multiple requests at the same time, thus speeding up the scraping process.

In addition to these features, crul has a good compatibility with other R packages such as tidyverse and purrr which facilitates the manipulation of the data obtained after scraping.

Highlights


http2uses-curlasync

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"
library(crul)

# Sending a GET request to a website
response <- HttpClient$new("https://www.example.com")$get()
# Sending a POST request to a website
request_body <- list(param1 = "value1", param2 = "value2")
response <- HttpClient$new("https://www.example.com")$post(body = request_body)

# Extracting the status code and body of the response
status_code <- response$status_code()
body <- response$body()

# crul also allows easy asynchronous requests:
urls <- c("https://www.example1.com", "https://www.example2.com", "https://www.example3.com")
# Creating a list of request objects from urls
requests <- lapply(urls, function(url) {
  HttpClient$new(url)$get()
})

# Sending the requests asynchronously
responses <- async(requests)

# Extracting the status code and body of the responses
status_codes <- lapply(responses, function(response) response$status_code())
bodies <- lapply(responses, function(response) response$body())

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