Scraping Multiple Pages in R with rvest and purrr

Web scraping is useful to programmatically extract data from websites. Often you need to scrape multiple pages from a site to gather complete information. In this article, we'll see how to scrape multiple pages in R using the rvest and purrr packages.

Prerequisites

To follow along, you'll need:

library(rvest)
library(purrr)

Define Base URL

—

We'll scrape a blog - https://copyblogger.com/blog/. The page URLs follow a pattern:

<https://copyblogger.com/blog/>
<https://copyblogger.com/blog/page/2/>
<https://copyblogger.com/blog/page/3/>

Let's define the base URL pattern:

base_url <- "<https://copyblogger.com/blog/page/%d/>"

The %d allows us to insert the page number.

Specify Number of Pages

Next, we'll specify how many pages to scrape. Let's scrape the first 5 pages:

num_pages <- 5

Loop Through Pages

We can now loop from 1 to num_pages and construct the URL for each page:

map(1:num_pages, function(page) {

  # Construct page URL
  url <- sprintf(base_url, page)

  # Code to scrape each page

})

Send Request and Parse HTML

Inside the loop, we'll send a GET request and parse the HTML using rvest:

page <- read_html(url)

html_nodes(page, "article") %>%
  map(function(article) {

    # Extract data from article

  })

This gives us parsed HTML nodes to extract data from.

Extract Data

Now within the loop we can extract information like title, URL, author etc from each article node:

title <- html_node(article, "h2.entry-title") %>% html_text()
url <- html_node(article, "a.entry-title-link") %>% html_attr("href")
author <- html_node(article, "div.post-author a") %>% html_text()

Full Code

Our full code to scrape 5 pages is:

library(rvest)
library(purrr)

base_url <- "https://copyblogger.com/blog/page/%d/"
num_pages <- 5

map(1:num_pages, function(page) {

  url <- sprintf(base_url, page)
  
  page <- read_html(url)

  html_nodes(page, "article") %>%
    map(function(article) {
    
      title <- html_node(article, "h2.entry-title") %>% html_text()
      url <- html_node(article, "a.entry-title-link") %>% html_attr("href")
      author <- html_node(article, "div.post-author a") %>% html_text()
      
      categories <- html_nodes(article, "div.entry-categories a") %>% 
        html_text()
      
      print(title)
      print(url)
      print(author)
      print(categories)
      
    })
  
})

This allows us to scrape and extract data from multiple pages sequentially in R. The code can be extended to scrape any number of pages.

Summary

Web scraping enables collecting large datasets programmatically. With the techniques here, you can scrape and extract information from multiple pages of a website in R.

While these examples are great for learning, scraping production-level sites can pose challenges like CAPTCHAs, IP blocks, and bot detection. Rotating proxies and automated CAPTCHA solving can help.

Proxies API offers a simple API for rendering pages with built-in proxy rotation, CAPTCHA solving, and evasion of IP blocks. You can fetch rendered pages in any language without configuring browsers or proxies yourself.

This allows scraping at scale without headaches of IP blocks. Proxies API has a free tier to get started. Check out the API and sign up for an API key to supercharge your web scraping.

With the power of Proxies API combined with Python libraries like Beautiful Soup, you can scrape data at scale without getting blocked.