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ralgervscrul

MIT 3 1 153
1.2 thousand (month) Dec 22 2019 2.2.4(3 years ago)
101 1 16 MIT
1.4.0(1 year, 7 months ago) Nov 09 2016 61.7 thousand (month)

ralger is a small web scraping framework for R based on rvest and xml2.

It's goal to simplify basic web scraping and it provides a convenient and easy to use API.

It offers functions for retrieving pages, parsing HTML using CSS selectors, automatic table parsing and auto link, title, image and paragraph extraction.

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("ralger")

url <- "http://www.shanghairanking.com/rankings/arwu/2021"

# retrieve HTML and select elements using CSS selectors:
best_uni <- scrap(link = url, node = "a span", clean = TRUE)
head(best_uni, 5)
#>  [1] "Harvard University"
#>  [2] "Stanford University"
#>  [3] "University of Cambridge"
#>  [4] "Massachusetts Institute of Technology (MIT)"
#>  [5] "University of California, Berkeley"

# ralger can also parse HTML attributes
attributes <- attribute_scrap(
  link = "https://ropensci.org/",
  node = "a", # the a tag
  attr = "class" # getting the class attribute
)

head(attributes, 10) # NA values are a tags without a class attribute
#>  [1] "navbar-brand logo" "nav-link"          NA
#>  [4] NA                  NA                  "nav-link"
#>  [7] NA                  "nav-link"          NA
#> [10] NA
#

# ralger can automatically scrape tables:
data <- table_scrap(link ="https://www.boxofficemojo.com/chart/top_lifetime_gross/?area=XWW")

head(data)
#> # A tibble: 6 × 4
#>    Rank Title                                      `Lifetime Gross`  Year
#>   <int> <chr>                                      <chr>            <int>
#> 1     1 Avatar                                     $2,847,397,339    2009
#> 2     2 Avengers: Endgame                          $2,797,501,328    2019
#> 3     3 Titanic                                    $2,201,647,264    1997
#> 4     4 Star Wars: Episode VII - The Force Awakens $2,069,521,700    2015
#> 5     5 Avengers: Infinity War                     $2,048,359,754    2018
#> 6     6 Spider-Man: No Way Home                    $1,901,216,740    2021
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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