soupvsralger
soup is a Go library for parsing and querying HTML documents.
It provides a simple and intuitive interface for extracting information from HTML pages. It's inspired by popular Python web scraping
library BeautifulSoup and shares similar use API implementing functions like Find and FindAll.
soup can also use go's built-in http client to download HTML content.
Note that unlike beautifulsoup, soup does not support CSS selectors or 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.
Example Use
package main
import (
"fmt"
"log"
"github.com/anaskhan96/soup"
)
func main() {
url := "https://www.bing.com/search?q=weather+Toronto"
# soup has basic HTTP client though it's not recommended for scraping:
resp, err := soup.Get(url)
if err != nil {
log.Fatal(err)
}
# create soup object from HTML
doc := soup.HTMLParse(resp)
# html elements can be found using Find or FindStrict methods:
# in this case find <div> elements where "class" attribute matches some values:
grid := doc.FindStrict("div", "class", "b_antiTopBleed b_antiSideBleed b_antiBottomBleed")
# note: to find all elements FindAll() method can be used the same way
# elements can be further searched for descendents:
heading := grid.Find("div", "class", "wtr_titleCtrn").Find("div").Text()
conditions := grid.Find("div", "class", "wtr_condition")
primaryCondition := conditions.Find("div")
secondaryCondition := primaryCondition.FindNextElementSibling()
temp := primaryCondition.Find("div", "class", "wtr_condiTemp").Find("div").Text()
others := primaryCondition.Find("div", "class", "wtr_condiAttribs").FindAll("div")
caption := secondaryCondition.Find("div").Text()
fmt.Println("City Name : " + heading)
fmt.Println("Temperature : " + temp + "˚C")
for _, i := range others {
fmt.Println(i.Text())
}
fmt.Println(caption)
}
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