collyvsrvest
Colly is a popular web scraping library for the Go programming language. It's designed to be fast and easy to use, and it provides a simple and flexible API for traversing and extracting information from websites.
Colly supports:
- Concurrent scraping with a simple API
- Automatic handling of cookies and sessions
- Automatic handling of redirects
- Support for parsing HTML and XML
- Support for parsing JSON and binary data
- Support for custom storage (e.g. scraping results to a database)
- Simple JavaScript rendering with Colly's built-in rendering engine.
Colly also provides several optional features, such as support for user-agents, delay between requests, rate-limiting and proxy usage.
Colly's API is quite simple, and it is easy to get started with basic web scraping tasks. It's a good choice for scraping moderate to heavy sites, and it can be useful for a wide range of use cases, such as data mining, content extraction, and more.
Additionally, you can use it together with Goquery, a library that allow you to make jquery like queries on HTML documents and it is often used together with Colly to ease the way of parsing the HTML.
rvest is a popular R library for web scraping and parsing HTML and XML documents. It is built on top of the xml2 and httr libraries and provides a simple and consistent API for interacting with web pages.
One of the main advantages of using rvest is its simplicity and ease of use. It provides a number of functions that make it easy to extract information from web pages, even for those who are not familiar with web scraping. The html_nodes and html_node functions allow you to select elements from an HTML document using CSS selectors, similar to how you would select elements in JavaScript.
rvest also provides functions for interacting with forms, including html_form, set_values, and submit_form functions. These functions make it easy to navigate through forms and submit data to the server, which can be useful when scraping sites that require authentication or when interacting with dynamic web pages.
rvest also provides functions for parsing XML documents. It includes xml_nodes and xml_node functions, which also use CSS selectors to select elements from an XML document, as well as xml_attrs and xml_attr functions to extract attributes from elements.
Another advantage of rvest is that it provides a way to handle cookies, so you can keep the session alive while scraping a website, and also you can handle redirections with handle_redirects
Highlights
Example Use
package main
import (
"fmt"
"github.com/gocolly/colly/v2"
)
func main() {
// Instantiate default collector
c := colly.NewCollector(
// Visit only domains: hackerspaces.org, wiki.hackerspaces.org
colly.AllowedDomains("hackerspaces.org", "wiki.hackerspaces.org"),
)
// On every a element which has href attribute call callback
c.OnHTML("a[href]", func(e *colly.HTMLElement) {
link := e.Attr("href")
// Print link
fmt.Printf("Link found: %q -> %s\n", e.Text, link)
// Visit link found on page
// Only those links are visited which are in AllowedDomains
c.Visit(e.Request.AbsoluteURL(link))
})
// Before making a request print "Visiting ..."
c.OnRequest(func(r *colly.Request) {
fmt.Println("Visiting", r.URL.String())
})
// Start scraping on https://hackerspaces.org
c.Visit("https://hackerspaces.org/")
}
library("rvest")
# Rvest can use basic HTTP client to download remote HTML:
tree <- read_html("http://webscraping.fyi/lib/r/rvest")
# or read from string:
tree <- read_html('
<div class="products">
<a href="/product/1">Cat Food</a>
<a href="/product/2">Dog Food</a>
</div>
')
# to parse HTML trees with rvest we use r pipes (the %>% symbol) and html_element function:
# we can use css selectors:
print(tree %>% html_element(".products>a") %>% html_text())
# "[1] "\nCat Food\nDog Food\n""
# or XPath:
print(tree %>% html_element(xpath="//div[@class='products']/a") %>% html_text())
# "[1] "\nCat Food\nDog Food\n""
# Additionally rvest offers many quality of life functions:
# html_text2 - removes trailing and leading spaces and joins values
print(tree %>% html_element("div") %>% html_text2())
# "[1] "Cat Food Dog Food""
# html_attr - selects element's attribute:
print(tree %>% html_element("div") %>% html_attr('class'))
# "products"