cheerio is a popular JavaScript library that allows you to interact with and manipulate HTML and XML documents
in a similar way to how you would with jQuery in a browser.
It is a fast, flexible, and lean implementation of core jQuery designed specifically for the server.
One of the main benefits of using cheerio is that it allows you to use jQuery-like syntax to navigate and m
anipulate the Document Object Model (DOM) of an HTML or XML document, making it easy to work with.
cheerio supports CSS selectors though not XPath.
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.
```javascript
const cheerio = require('cheerio');
const $ = cheerio.load('
My titleHello World!
');
// use css selectors
console.log($('title').text()); // My title
console.log($('.name').text()); // Hello World!
// select multiple elements
const $ = cheerio.load('
');
$('li').each(function(i, elem) {
console.log($(this).text());
});
// modify elements
const $ = cheerio.load('
Hello World!
');
$('h1').text('Hello, Cheerio!');
console.log($.html());
```
```r
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
#>
#> 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
```