R Web Scraping in 2025: Rvest, Rcrawler & Proxy Tips

R started life as a language for statisticians, and that heritage is exactly why it's an underrated tool for web scraping. Most scraping projects don't end when the data lands on disk — they end with cleaning, joining, modelling and charting. R does all of that natively, which means you can collect public data and analyse it without leaving your environment. This guide walks through scraping static pages with Rvest, crawling whole sites with Rcrawler, and handling JavaScript-rendered pages with RSelenium, plus how to wire in proxies responsibly.

A quick note on ethics before we start: everything here is aimed at collecting publicly available data for legitimate purposes — research, price monitoring, QA, or analysing content you have permission to access. Always read a site's Terms of Service and robots.txt, throttle your requests, and avoid gated or personal data. Good scraping is polite scraping.

Why Choose R Over Python for Scraping?

Python usually gets top billing for scraping, and it deserves the reputation:

• Readable syntax: Python's clean, object-oriented style makes it approachable for beginners and easy to structure into larger projects.

• Huge ecosystem: Libraries like Scrapy, BeautifulSoup and Requests are purpose-built for extraction, backed by an enormous community and endless examples.

If you want to compare toolkits, our Python web scraping guide covers that side in depth.

R isn't a runner-up, though. Its strength is what happens after the fetch — data wrangling with the Tidyverse, statistics, and visualisation with ggplot2. If your project is analysis-heavy, staying in R avoids shuffling data between languages.

Two libraries illustrate the difference:

• BeautifulSoup (Python): Extremely flexible at parsing messy HTML, with great docs. But it doesn't fetch pages itself — it needs a companion like Requests or a browser automation tool. See our Beautiful Soup walkthrough for the details.

• Rvest (R): Built to fit the Tidyverse, it fetches and parses in one flow, often solving simple scraping tasks in fewer lines. It's a little less forgiving with badly structured HTML than BeautifulSoup, but for clean, static sites it's a pleasure.

The short version: reach for Python on large, complex, dynamic projects; reach for R when analysis is the point or you already live in RStudio.

Getting Started with the Rvest Package

You'll need R and an IDE. RStudio is the popular free choice, available here.

With both installed, open RStudio and run this in the console to install Rvest:

install.packages("rvest")

Once it finishes, create a new R Script (File > New File > R Script) and load the library:

library(rvest)

Now fetch a page. We'll use books.toscrape.com, a sandbox site built for scraping practice:

# Define the target URL
target_url <- "https://books.toscrape.com/"

# Read the HTML content from the URL
web_content <- read_html(target_url)

That fetches the page but doesn't extract anything yet — you tell Rvest what to grab with CSS selectors. Let's pull every link's href attribute:

# Extract links using CSS selectors and the pipe operator
all_links <- web_content %>%
  html_nodes("a") %>%
  html_attr("href")

# Print the extracted links
print(all_links)

This takes the downloaded web_content, finds all anchor (a) nodes, and reads their href values. That %>% symbol is the "pipe" from the magrittr package (loaded automatically with Rvest), passing the result of one function into the next so the code reads top to bottom.

To run the whole script in RStudio, select all lines (Ctrl+A / Cmd+A) and click "Run", or use "Source" to execute the file. The same pattern adapts easily — swap the selector for "h1" and call html_text() to grab heading text, or select "img" and use html_attr("src") to collect image URLs. That's a solid toolkit for pulling structured data out of static HTML.

Crawling Entire Websites with Rcrawler

When you need more than one page, Rcrawler follows links automatically and extracts data across a whole site. Install and load it:

install.packages("Rcrawler")
library(Rcrawler)

A single command kicks off a crawl. Here we crawl the example site and pull H1 headings and paragraph text via XPath:

Rcrawler(
  Website = "https://books.toscrape.com/",
  ExtractXpathPat = c("//h1", "//p"),
  MaxDepth = 1,
  no_cores = 4,
  no_conn = 4
)

Rcrawler starts at the given Website. The ExtractXpathPat argument takes a vector of XPath expressions — here, every h1 and p element on the pages it visits. MaxDepth = 1 keeps the crawl shallow for this demo, while no_cores and no_conn enable parallel processing to speed things up.

Running many parallel connections is powerful, but be a good citizen: keep concurrency reasonable so you don't overload the target server, and add delays where the site's guidance suggests it. For larger, distributed crawls, proxy infrastructure helps spread requests across IPs and geographies so no single endpoint carries the whole load. Evomi's residential proxies and mobile proxies rotate IPs per connection, which is convenient for exactly this kind of multi-connection work.

By default, Rcrawler saves the raw HTML of visited pages into a folder and writes the extracted data to a CSV called INDEX.csv in your working directory. A nice touch: it's resilient — stop the process mid-run and everything collected so far is already saved.

Scraping JavaScript-Rendered Content with RSelenium

Plenty of modern sites load content with JavaScript after the initial HTML arrives. Simple fetchers like Rvest's read_html won't see that content because they don't execute scripts. To read it, you drive a real browser.

RSelenium is R's take on Selenium, letting you control a browser programmatically. Install it first:

install.packages("RSelenium")

Using it means starting a Selenium server and a browser driver. Here's a basic Chrome setup:

library(RSelenium)

# Start the Selenium server and Chrome driver
# This might prompt you to download the correct driver if needed
driver_instance <- rsDriver(
  browser = "chrome",
  port = 4445L,
  chromever = "latest"
)

remote_driver <- driver_instance[["client"]]

# Note: Ensure you have Chrome installed

Now navigate to a JavaScript-driven page with the controlled browser:

# Navigate to the URL
target_site <- "https://quotes.toscrape.com/js/" # A site that uses JS to load content
remote_driver$navigate(target_site)

# Give the page a second or two to load JavaScript content
Sys.sleep(2)

# Get the page source *after* JavaScript execution
page_html <- remote_driver$getPageSource()[[1]]

# Close the browser and server when done
remote_driver$close()
driver_instance[["server"]]$stop()

The page_html variable now holds the full HTML, including content injected by JavaScript. Hand it to Rvest's read_html() and extract as before:

# Load rvest again if needed
library(rvest)

# Parse the JS-rendered HTML
rendered_content <- read_html(page_html)

# Now extract elements as before, e.g., quotes
quotes <- rendered_content %>%
  html_nodes(".quote .text") %>%
  html_text()

print(quotes)

Adding Proxies for Reliable, Distributed Collection

Once you scrape at scale — many pages, multiple regions, or geo-specific content — proxies become a practical part of the workflow. They let you test how a public page renders for users in different countries, keep request volume off a single IP, and run parallel jobs without hammering one address. The goal here is reliability and accurate geo-testing, not evading anyone's rules — always stay within each site's Terms of Service and rate limits.

Evomi's proxies are ethically sourced and Swiss-based, with residential from $0.49/GB, datacenter from $0.30/GB, mobile at $2.2/GB and static ISP from $1/IP. There are free trials on residential, mobile and datacenter plans so you can test them against RSelenium or Rcrawler before committing — see the pricing page for details.

If maintaining browser drivers and infrastructure isn't how you want to spend your time, Evomi's managed Scraping Browser gives you a cloud headless Chromium endpoint (Playwright/Puppeteer compatible) so you can offload the heavy JavaScript rendering entirely. For a comparison of a similar approach in another language, our Rust scraping guide covers the same trade-offs from a different angle.

Final Thoughts on R for Web Scraping

R gives you an integrated environment where collection and analysis live side by side. Rvest handles clean static pages, Rcrawler covers site-wide crawling, and RSelenium tackles JavaScript-heavy pages — a capable toolkit for most public-data projects.

Do it responsibly: respect robots.txt and Terms of Service, throttle your requests, and stick to publicly available data. When a project grows past a single machine or needs geo-specific testing, reliable, ethically sourced proxies keep it running smoothly. Pair R's analytical power with sensible infrastructure and you've got a workflow that scales without cutting corners.