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geziyorvsralger

MPL-2.0 23 1 2,470
Jun 06 2019 2024-04-04(a day ago)
153 1 3 MIT
2.2.4(3 years ago) Dec 22 2019 1.2 thousand (month)

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

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