choppervsralger
Chopper is a tool to extract elements from HTML by preserving ancestors and CSS rules.
Compared to other HTML parsers Chopper is designed to retain original HTML tree but eliminate elements that do not match parsing rules. Meaning, we can parse HTML elements and keep thei structure for machine learning or other tasks where data structure is needed as well as the data value.
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
HTML = """
<html>
<head>
<title>Test</title>
</head>
<body>
<div id="header"></div>
<div id="main">
<div class="iwantthis">
HELLO WORLD
<a href="/nope">Do not want</a>
</div>
</div>
<div id="footer"></div>
</body>
</html>
"""
CSS = """
div { border: 1px solid black; }
div#main { color: blue; }
div.iwantthis { background-color: red; }
a { color: green; }
div#footer { border-top: 2px solid red; }
"""
extractor = Extractor.keep('//div[@class="iwantthis"]').discard('//a')
html, css = extractor.extract(HTML, CSS)
# will result in:
html
"""
<html>
<body>
<div id="main">
<div class="iwantthis">
HELLO WORLD
</div>
</div>
</body>
</html>"""
css
"""
div{border:1px solid black;}
div#main{color:blue;}
div.iwantthis{background-color:red;}
"""
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