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.
xmltodict is a Python library that allows you to work with XML data as if it were JSON.
It allows you to parse XML documents and convert them to dictionaries,
which can then be easily manipulated using standard dictionary operations.
You can also use the library to convert a dictionary back into an XML document.
xmltodict is built on top of the popular lxml library and provides a simple, intuitive API for working with XML data.
Note that despite using lxml conversion speeds can be quite slow for large XML documents and in web scraping this should
be used to parse specific snippets instead of whole HTML documents.
xmltodict pairs well with JSON parsing tools like jmespath or jsonpath.
Alternatively, it can be used in reverse mode to parse JSON documents using HTML parsing tools like CSS selectors and XPath.
It can be installed via pip by running pip install xmltodict command.
```r
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
#>
#> 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
```
```python
import xmltodict
xml_string = """
The Great Gatsby
F. Scott Fitzgerald
Charles Scribner's Sons
1925
"""
book_dict = xmltodict.parse(xml_string)
print(book_dict)
{'book': {'title': 'The Great Gatsby',
'author': 'F. Scott Fitzgerald',
'publisher': "Charles Scribner's Sons",
'publication_date': '1925'}}
# and to reverse:
book_xml = xmltodict.unparse(book_dict)
print(book_xml)
# the xml can be loaded and parsed using parsel or beautifulsoup:
from parsel import Selector
sel = Selector(book_xml)
print(sel.css('publication_date::text').get())
'1925'
```