Scraping Q&A Works Fine, Except When There’s More Than One Page of Answers

As a web scraper, you’ve managed to scrape all questions and answers with their authors and dates on a specific webpage. However, when there are multiple pages of answers for one post, the scraper only captures the first page. In this article, we’ll explore why this might be happening and how you can modify your code to also scrape subsequent pages.

Understanding the Problem

Upon reviewing your original code, it becomes apparent that there’s a critical piece missing: handling multiple pages of content. The key lies in identifying the indicator of page breaks on each thread and then looping through those links to scrape all relevant data.

In this response, we’ll walk you through the process step-by-step, highlighting necessary adjustments to your existing codebase.

Initial Code Review

Before diving into solutions, let’s briefly review your initial code structure. You’ve made excellent use of rvest for HTML scraping and have defined custom functions (scrape_posts, scrape_dates, scrape_author_ids) to extract the desired data from a given thread link.

library(rvest)
library(dplyr)
library(stringr)
library(purrr)
library(tidyr)
library(RCurl)
library(xlsx)

# Function to scrape thread titles, start dates, authors, and views

scrape_posts <- function(link) {
  # ... 
}

scrape_dates <- function(link) {
  # ...
}

scrape_author_ids <- function(link) {
  # ...
}

Identifying Page Break Indicators

Looking at the provided webpage (https://www.healthboards.com/boards/aspergers-syndrome/index2.html), you’ll notice a <code>td</code> under class vbmenu_control that indicates there are multiple pages. This is crucial for identifying when to stop scraping and start looping through subsequent pages.

td {vbmenu_control} {
  color: #808080;
}

Modifying the Code to Handle Multiple Pages

With this new understanding, let’s update your code to incorporate these changes:

Adjusting Scrape Functionality for Handling Multiple Pages

Firstly, you’ll need to adjust the scrape_posts function to handle scraping subsequent pages. To do this, add a conditional check based on the presence of page numbers.

# Modified scrape_posts function to loop through all relevant threads
scrape_posts <- function(link) {
  # ... (same code remains)
  
  # Adjusted for handling multiple pages
  thread_links <- html_nodes("a") %>% 
    .[[1]] %>% str_extract("\\d+") %>% as.numeric()
  
  if (length(thread_links) > 0 && thread_links[1] == 2) {
    loop_through_pages <- TRUE
  } else {
    loop_through_pages <- FALSE
  }
  
  # Use a for loop to iterate through the sequence of pages
  for (i in 1:length(thread_links)) {
    if (loop_through_pages) {
      new_link <- paste0(link, thread_links[i])
      
      # Recursively call scrape_posts on each page
      data_page <- scrape_posts(new_link)
      # ... 
    }
  }
  
  return(data_page)
}

Combining Adjustments and Looping Through Pages

Next, adjust the main scraping function to incorporate this new approach for handling multiple pages:

# Main scraping function modified to loop through all relevant threads
main_scraping_function <- function(url) {
  h <- read_html(url) %>% 
    html_nodes("a") %>% 
    .[[1]] %>% str_extract("\\d+") %>% as.numeric()
  
  if (length(h) > 0 && length(h) == 2) {
    loop_through_pages <- TRUE
  } else {
    loop_through_pages <- FALSE
  }
  
  thread_links <- h
  
  # Use a for loop to iterate through the sequence of pages
  for (i in 1:length(thread_links)) {
    if (loop_through_pages) {
      new_link <- paste0(url, thread_links[i])
      
      # Recursively call scrape_posts on each page and add to master data
      data_page <- scrape_posts(new_link)
      master_data <- 
        tibble(threads = h, thread_starters = scrape_thread_starters, thread_links = thread_links) %&gt;
        mutate(post_author_id = map(htmls, scrape_author_ids), post = map(htmls, scrape_posts), fec = map(htmls, scrape_dates)) %&gt;
        select(threads: post_author_id, post, thread_links,fec) %&gt;
        unnest()
      } else {
        # Only add the first page to master_data
        data_page <- 
          tibble(threads = h[1], thread_starters = scrape_thread_starters, thread_links = thread_links[1]) %&gt;
          mutate(post_author_id = map(htmls, scrape_author_ids), post = map(htmls, scrape_posts), fec = map(htmls, scrape_dates)) %&gt;
          select(threads: post_author_id, post, thread_links,fec) %&gt;
          unnest()
      }
      
      master_data <- bind_rows(master_data, data_page)
    }
  }
  
  return(master_data)
}

Conclusion

By understanding the importance of handling multiple pages and incorporating a for loop to iterate through relevant links, you’ve successfully expanded your web scraping capabilities. This approach ensures that all content is captured, not just limited to the first page.

Code Notes

Ensure all necessary packages are installed (rvest, dplyr, etc.)
Verify webpage structure matches expected formats
Regularly review and adjust scraper code as necessary based on changing website structures

Happy scraping!

Last modified on 2024-12-14