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ralgervsscrapy

MIT 3 1 153
1.2 thousand (month) Dec 22 2019 2.2.4(3 years ago)
50,703 30 652 BSD
2.11.1(a month ago) Jul 26 2019 1.6 million (month)

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.

Scrapy is an open-source Python library for web scraping. It allows developers to extract structured data from websites using a simple and consistent interface.

Scrapy provides:

  • A built-in way to follow links and extract data from multiple pages (crawling)
  • Handling common web scraping tasks such as logging in, handling cookies, and handling redirects.

Scrapy is built on top of the Twisted networking engine, which provides a non-blocking way to handle multiple requests at the same time, allowing Scrapy to efficiently scrape large websites.

It also comes with a built-in mechanism for handling common web scraping problems, such as:

  • handling HTTP errors
  • handling broken links

Scrapy also provide these features:

  • Support for storing scraped data in various formats, such as CSV, JSON, and XML.
  • Built-in support for selecting and extracting data using XPath or CSS selectors (through parsel).
  • Built-in support for handling common web scraping problems (like deduplication and url filtering).
  • Ability to easily extend its functionality using middlewares.
  • Ability to easily extend output processing using pipelines.

Highlights


popularcss-selectorsxpath-selectorscommunity-toolsoutput-pipelinesmiddlewaresasyncproductionlarge-scale

Example Use


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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