node-fetchvsralger
node-fetch is a lightweight library that provides a fetch()-like API for making HTTP requests in Node.js. It is a light-weight implementation of the Fetch API, which is mostly compatible with the browser's version.
node-fetch is primarily known as almost identical package fetch() is included in web browsers so it shares the same use common API. It's great starting point for people coming from front-end environment.
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.
Highlights
popular
Example Use
```javascript
const fetch = require('node-fetch');
// fetch supports both Promises and async/await
fetch('http://httpbin.org/get')
.then(res => res.text())
.then(body => console.log(body))
.catch(err => console.error(err));
const response = await fetch('http://httpbin.org/get');
// for concurrent scraping Promise.all can be used
const results = await Promise.all([
fetch('http://httpbin.org/html'),
fetch('http://httpbin.org/html'),
fetch('http://httpbin.org/html'),
])
// POST requests
await fetch('http://httpbin.org/post', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ name: 'John Doe' }),
})
// Proxy use:
const agent = new https.Agent({
rejectUnauthorized: false,
proxy: {
host: 'proxy.example.com',
port: 8080
}
});
await fetch('https://httpbin.org/ip', { agent })
// setting headers and cookies
const headers = new fetch.Headers();
headers.append('Cookie', 'myCookie=123');
headers.append('X-My-Header', 'myValue');
await fetch('https://httpbin.org/headers', { headers })
```
```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
```
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