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htmlparser2vsralger

MIT 10 4 4,338
127.4 million (month) Aug 28 2011 9.1.0(6 months ago)
152 1 3 MIT
Dec 22 2019 1.0 thousand (month) 2.2.4(3 years 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


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 = "<p>Hello, <b>world</b>!</p>";
parser.write(html);
parser.end();
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

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