geziyorvsralger
Geziyor is a blazing fast web crawling and web scraping framework. It can be used to crawl websites and extract structured data from them. Geziyor is useful for a wide range of purposes such as data mining, monitoring and automated testing.
Features:
- JS Rendering
- 5.000+ Requests/Sec
- Caching (Memory/Disk/LevelDB)
- Automatic Data Exporting (JSON, CSV, or custom)
- Metrics (Prometheus, Expvar, or custom)
- Limit Concurrency (Global/Per Domain)
- Request Delays (Constant/Randomized)
- Cookies, Middlewares, robots.txt
- Automatic response decoding to UTF-8
- Proxy management (Single, Round-Robin, Custom)
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
// This example extracts all quotes from quotes.toscrape.com and exports to JSON file.
func main() {
geziyor.NewGeziyor(&geziyor.Options{
StartURLs: []string{"http://quotes.toscrape.com/"},
ParseFunc: quotesParse,
Exporters: []export.Exporter{&export.JSON{}},
}).Start()
}
func quotesParse(g *geziyor.Geziyor, r *client.Response) {
r.HTMLDoc.Find("div.quote").Each(func(i int, s *goquery.Selection) {
g.Exports <- map[string]interface{}{
"text": s.Find("span.text").Text(),
"author": s.Find("small.author").Text(),
}
})
if href, ok := r.HTMLDoc.Find("li.next > a").Attr("href"); ok {
g.Get(r.JoinURL(href), quotesParse)
}
}
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