node-crawlervsralger
node-crawler is a popular web scraping library for Node.js that allows you to easily navigate and extract data from websites. It has a simple API and supports concurrency, making it efficient for scraping large numbers of pages.
Features:
- Server-side DOM & automatic jQuery insertion with Cheerio (default) or JSDOM,
- Configurable pool size and retries,
- Control rate limit,
- Priority queue of requests,
- forceUTF8 mode to let crawler deal for you with charset detection and conversion,
- Compatible with 4.x or newer version.
- Http2 support
- Proxy support
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.
Example Use
```javascript
const Crawler = require('crawler');
const c = new Crawler({
maxConnections: 10,
// This will be called for each crawled page
callback: (error, res, done) => {
if (error) {
console.log(error);
} else {
const $ = res.$;
// $ is Cheerio by default
//a lean implementation of core jQuery designed specifically for the server
console.log($('title').text());
}
done();
}
});
// Queue just one URL, with default callback
c.queue('http://www.amazon.com');
// Queue a list of URLs
c.queue(['http://www.google.com/','http://www.yahoo.com']);
// Queue URLs with custom callbacks & parameters
c.queue([{
uri: 'http://parishackers.org/',
jQuery: false,
// The global callback won't be called
callback: (error, res, done) => {
if (error) {
console.log(error);
} else {
console.log('Grabbed', res.body.length, 'bytes');
}
done();
}
}]);
// Queue some HTML code directly without grabbing (mostly for tests)
c.queue([{
html: '
This is a test
' }]); ```
```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
```
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