Webscraping in `R` with `scrapurrr`

Overview

This package offers a functional programming style framework for webscraping and useful tools and tutorials to overcome common webscraping challenges. Check out the vignettes for webscraping workflows and infos on how to write the webscraping logic for specific webscraping challenges

Install the development version via

remotes::install_github("https://github.com/m-g-h/scrapurrr",
                        build_vignettes = T)

Solve common webscraping tasks in a `scrapefun()`

Write a taylor-made scrapefun() that solves the challenges of your specific webscraping task:

Check if you need a webdriver or not using view_html()
Use rvest or implement a webdriver like like phantomjs or Selenium to download the webpage content
Use rvest for extracting information. The helpers node_which() and html_find() add regex selection
Use a VPN to rotate proxies (currently NordVPN via NordVPN_quick_connect())
Use V8 to execute javascript, e.g. to de-obfusciate emails.
Return output via return_named_list() conveniently as named list

See vignette("scrapefuns_and_helpers") for more info.

Automate repetitive scraping tasks

Step 1: define the list of urls or html files you want to scrape

Step 2: define a flexible scrapefun() taylor-made to the specific scrapurrr challenge at hand

Step 3: use scrapurrr() to webscrape the entire list.

See vignette("workflows") for more info.

Example Workflow

Generate list of links to scrape. In this example we scrape the Wikipedia pages of all German federal states:

library(scrapurrr)
library(rvest)

state_links = read_html("https://en.wikipedia.org/wiki/States_of_Germany") %>% 
  html_element(".wikitable") %>% 
  html_elements("tr> td:nth-child(3)") %>% 
  html_elements("a") %>% 
  html_attr("href") %>% 
  paste0("https://en.wikipedia.org", .)

head(state_links)
#> [1] "https://en.wikipedia.org/wiki/Baden-W%C3%BCrttemberg"
#> [2] "https://en.wikipedia.org/wiki/Bavaria"               
#> [3] "https://en.wikipedia.org/wiki/Berlin"                
#> [4] "https://en.wikipedia.org/wiki/Brandenburg"           
#> [5] "https://en.wikipedia.org/wiki/Bremen_(state)"        
#> [6] "https://en.wikipedia.org/wiki/Hamburg"

Before defining the scrapefun() I assess whether I need a webdriver or not. With view_html() I can see what my computer receives with read_html() from rvest/xml2 in the viewer:

Looks like we have a static html page and the capital is easily accessible. If the content would be loaded dynamically or if it was obfuscated I would have needed a webdriver like phantomjs or Selenium.

Now we define a scraping function that extracts the state capital from each page. I use rvest and tidyverse to power the function.

library(tidyverse)

get_state_capital = function(link){
  # Download html
  .page = read_html(link)
  
  # Extract name of country
  State = .page %>% 
    html_element("h1") %>% 
    html_text()
  
  # Extract capital city
  Capital = .page %>% 
    html_element("table.infobox") %>% 
    html_table(header = F) %>% 
    filter(str_detect(X1, "Capital")) %>% 
    pull("X2")
  
  # Return results as named list. Objects prefixed with a dot get ignored
  return_named_list()
}

And then we can use scrapurrr() and purrr::map_dfr to execute the task:

results = scrapurrr(state_links,
                    scrapefun = get_state_capital,
                    map_fun = purrr::map_dfr)

head(results)
#> # A tibble: 6 x 2
#>   Capital                              State            
#>   <chr>                                <chr>            
#> 1 Stuttgart                            Baden-Württemberg
#> 2 Munich                               Bavaria          
#> 3 Capital city, state and municipality Berlin           
#> 4 Potsdam                              Brandenburg      
#> 5 Bremen                               Bremen (state)   
#> 6 <NA>                                 Hamburg

Further Information and Advanced `scrapefun()` features

See the vignettes for further information:

vignette("workflows") for tutorials on automating common webscraping tasks
vignette("scrapefuns_and_helpers") for features to implement in your scrapefun(), e.g. javascript execution

m-g-h/scrapurrr

Webscraping in R with scrapurrr