cascadiavsralger
cascadia is a library for Go that provides a CSS selector engine, allowing you to use CSS selectors to select elements from an HTML document.
It is built on top of the html package in the Go standard library, and provides a more efficient and powerful way to select elements from an HTML document.
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
package main
import (
"fmt"
"github.com/andybalholm/cascadia"
"golang.org/x/net/html"
"strings"
)
func main() {
// Create an HTML string
html := `<html>
<body>
<div id="content">
<p>Hello, World!</p>
<a href="http://example.com">Example</a>
</div>
</body>
</html>`
// Parse the HTML string into a node tree
doc, err := html.Parse(strings.NewReader(html))
if err != nil {
fmt.Println("Error:", err)
return
}
// Compile the CSS selector
sel, err := cascadia.Compile("p")
if err != nil {
fmt.Println("Error:", err)
return
}
// Use the Selector.Match method to select elements from the document
matches := sel.Match(doc)
if len(matches) > 0 {
fmt.Println(matches[0].FirstChild.Data)
// > Hello, World!
}
}
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