ralgervsselectolax
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.
selectolax is a fast and lightweight library for parsing HTML and XML documents in Python. It is designed to be a drop-in replacement for the popular BeautifulSoup library, with significantly faster performance.
selectolax uses a Cython-based parser to quickly parse and navigate through HTML and XML documents. It provides a simple and intuitive API for working with the document's structure, similar to BeautifulSoup.
To use selectolax, you first need to install it via pip by running pip install selectolax``.
Once it is installed, you can use theselectolax.html.fromstring()function to parse an HTML document and create a selectolax object.
For example:
selectolax.html.fromstring()from selectolax.parser import HTMLParser
html_string = "<html><body>Hello, World!</body></html>"
root = HTMLParser(html_string).root
print(root.tag) # html
with file-like objects, bytes or file paths,
as well asselectolax.xml.fromstring()`` for parsing XML documents.
Once you have a selectolax object, you can use the select() method to search for elements in the document using CSS selectors,
similar to BeautifulSoup. For example:
body = root.select("body")[0]
print(body.text()) # "Hello, World!"
Like BeautifulSoups find and find_all methods selectolax also supports searching using the search()`` method, which returns the first matching element,
and thesearch_all()`` method, which returns all matching elements.
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
from selectolax.parser import HTMLParser
html_string = "<html><body>Hello, World!</body></html>"
root = HTMLParser(html_string).root
print(root.tag) # html
# use css selectors:
body = root.select("body")[0]
print(body.text()) # "Hello, World!"
# find first matching element:
body = root.search("body")
print(body.text()) # "Hello, World!"
# or all matching elements:
html_string = "<html><body><p>paragraph1</p><p>paragraph2</p></body></html>"
root = HTMLParser(html_string).root
for el in root.search_all("p"):
print(el.text())
# will print:
# paragraph 1
# paragraph 2