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htmlparser2vsralger

MIT 12 4 4,789
277.1 million (month) Aug 28 2011 12.0.0(2026-03-20 23:08:40 ago)
165 1 3 MIT
Dec 22 2019 327 (month) 2.3.0(2021-03-18 00:10:00 ago)

htmlparser2 is a Node.js library for parsing HTML and XML documents. It works by building a tree of elements, similar to the Document Object Model (DOM) in web browsers. This allows you to easily traverse and manipulate the structure of the document.

htmlparser2 is a low-level html tree parser but it can still be useful in web scraping as it's a powerful tool for HTML restructuring and serialization.

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 htmlparser = require("htmlparser2"); const parser = new htmlparser.Parser({ onopentag: (name, attribs) => { console.log(`Opening tag: ${name}`); }, ontext: (text) => { console.log(`Text: ${text}`); }, onclosetag: (name) => { console.log(`Closing tag: ${name}`); } }, {decodeEntities: true}); const html = "

Hello, world!

"; parser.write(html); parser.end(); ```
```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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