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httrvscrul

MIT 1 9 978
703.8 thousand (month) May 06 2012 1.4.7(9 months ago)
101 1 16 MIT
1.4.0(1 year, 5 months ago) Nov 09 2016 59.0 thousand (month)

The aim of httr is to provide a wrapper for the curl package, customised to the demands of modern web APIs.

Key features:

  • Functions for the most important http verbs: GET(), HEAD(), PATCH(), PUT(), DELETE() and POST().
  • Automatic connection sharing across requests to the same website (by default, curl handles are managed automatically), cookies are maintained across requests, and a up-to-date root-level SSL certificate store is used.
  • Requests return a standard reponse object that captures the http status line, headers and body, along with other useful information.
  • Response content is available with content() as a raw vector (as = "raw"), a character vector (as = "text"), or parsed into an R object (as = "parsed"), currently for html, xml, json, png and jpeg.
  • You can convert http errors into R errors with stop_for_status().
  • Config functions make it easier to modify the request in common ways: set_cookies(), add_headers(), authenticate(), use_proxy(), verbose(), timeout(), content_type(), accept(), progress().
  • Support for OAuth 1.0 and 2.0 with oauth1.0_token() and oauth2.0_token(). The demo directory has eight OAuth demos: four for 1.0 (twitter, vimeo, withings and yahoo) and four for 2.0 (facebook, github, google, linkedin). OAuth credentials are automatically cached within a project.

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(httr)

# GET requests:
resp <- GET("http://httpbin.org/get")
status_code(resp)  # status code
headers(resp)  # headers
str(content(resp))  # body

# POST requests: 
# Form encoded
resp <- POST(url, body = body, encode = "form")
# Multipart encoded
resp <- POST(url, body = body, encode = "multipart")
# JSON encoded
resp <- POST(url, body = body, encode = "json")

# setting cookies:
resp <- GET("http://httpbin.org/cookies", set_cookies("MeWant" = "cookies"))
content(r)$cookies  # get response cookies
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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