Examples of Web Scraping With R Want a quick way to gather data for your projects? Welcome to our guide to web scraping with R, a collection of articles and tutorials which walk you through how to automate grabbing data from the web and unpacking it into a data frame. This is when, Web Scraping or Web Crawling comes into picture. Web Scraping is an easy way to perform the repetitive task of copy and pasting data from the websites. With web scraping, we can crawl/surf through the websites and save and represent the necessary data in a customized format. Let us now understand the working of Web Scraping in the.

1. What Is The Meaning Of Web Scraping
2. Python Web Page Scraping

Introduction

Web scraping or crawling is the process of fetching data from a third-party website by downloading and parsing the HTML code to extract the data you want.

“But you should use an API for this!'

However, not every website offers an API, and APIs don't always expose every piece of information you need. So, it's often the only solution to extract website data.

There are many use cases for web scraping:

• E-commerce price monitoring
• News aggregation
• Lead generation
• SEO (search engine result page monitoring)
• Bank account aggregation (Mint in the US, Bankin’ in Europe)
• Individuals and researchers building datasets otherwise not available.

The main problem is that most websites do not want to be scraped. They only want to serve content to real users using real web browsers (except Google - they all want to be scraped by Google).

So, when you scrape, you do not want to be recognized as a robot. There are two main ways to seem human: use human tools and emulate human behavior.

This post will guide you through all the tools websites use to block you and all the ways you can successfully overcome these obstacles.

Emulate Human Tool: Headless Chrome

Why Using Headless Browsing?

When you open your browser and go to a webpage, it almost always means that you ask an HTTP server for some content. One of the easiest ways to pull content from an HTTP server is to use a classic command-line tool such as cURL.

The thing is, if you just do: curl www.google.com, Google has many ways to know that you are not a human (for example by looking at the headers). Headers are small pieces of information that go with every HTTP request that hits the servers. One of those pieces of information precisely describes the client making the request, This is the infamous “User-Agent” header. Just by looking at the “User-Agent” header, Google knows that you are using cURL. If you want to learn more about headers, the Wikipedia page is great. As an experiment, just go over here. This webpage simply displays the headers information of your request.

Headers are easy to alter with cURL, and copying the User-Agent header of a legit browser could do the trick. In the real world, you'd need to set more than one header. But it is not difficult to artificially forge an HTTP request with cURL or any library to make the request look exactly like a request made with a browser. Everybody knows this. So, to determine if you are using a real browser, websites will check something that cURL and library can not do: executing Javascript code.

Do you speak Javascript?

The concept is simple, the website embeds a Javascript snippet in its webpage that, once executed, will “unlock” the webpage. If you're using a real browser, you won't notice the difference. If you're not, you'll receive an HTML page with some obscure Javascript code in it:

Once again, this solution is not completely bulletproof, mainly because it is now very easy to execute Javascript outside of a browser with Node.js. However, the web has evolved and there are other tricks to determine if you are using a real browser.

Headless Browsing

Trying to execute Javascript snippets on the side with Node.js is difficult and not robust. And more importantly, as soon as the website has a more complicated check system or is a big single-page application cURL and pseudo-JS execution with Node.js become useless. So the best way to look like a real browser is to actually use one.

Headless Browsers will behave like a real browser except that you will easily be able to programmatically use them. The most popular is Chrome Headless, a Chrome option that behaves like Chrome without all of the user interface wrapping it.

The easiest way to use Headless Chrome is by calling a driver that wraps all functionality into an easy API. SeleniumPlaywright and Puppeteer are the three most famous solutions.

However, it will not be enough as websites now have tools that detect headless browsers. This arms race has been going on for a long time.

While these solutions can be easy to do on your local computer, it can be trickier to make this work at scale.

Managing lots of Chrome headless instances is one of the many problems we solve at ScrapingBee

Browser Fingerprinting

Everyone, especially front-end devs, know that every browser behaves differently. Sometimes it's about rendering CSS, sometimes Javascript, and sometimes just internal properties. Most of these differences are well-known and it is now possible to detect if a browser is actually who it pretends to be. This means the website asks “do all of the browser properties and behaviors match what I know about the User-Agent sent by this browser?'.

This is why there is an everlasting arms race between web scrapers who want to pass themselves as a real browser and websites who want to distinguish headless from the rest.

However, in this arms race, web scrapers tend to have a big advantage here is why:

Most of the time, when a Javascript code tries to detect whether it's being run in headless mode, it is when a malware is trying to evade behavioral fingerprinting. This means that the Javascript will behave nicely inside a scanning environment and badly inside real browsers. And this is why the team behind the Chrome headless mode is trying to make it indistinguishable from a real user's web browser in order to stop malware from doing that. Web scrapers can profit from this effort.

Another thing to know is that while running 20 cURL in parallel is trivial and Chrome Headless is relatively easy to use for small use cases, it can be tricky to put at scale. Because it uses lots of RAM, managing more than 20 instances of it is a challenge.

If you want to learn more about browser fingerprinting I suggest you take a look at Antoine Vastel's blog, which is entirely dedicated to this subject.

That's about all you need to know about how to pretend like you are using a real browser. Let's now take a look at how to behave like a real human.

TLS Fingerprinting

What is it?

TLS stands for Transport Layer Security and is the successor of SSL which was basically what the “S” of HTTPS stood for.

This protocol ensures privacy and data integrity between two or more communicating computer applications (in our case, a web browser or a script and an HTTP server).

Similar to browser fingerprinting the goal of TLS fingerprinting is to uniquely identify users based on the way they use TLS.

How this protocol works can be split into two big parts.

First, when the client connects to the server, a TLS handshake happens. During this handshake, many requests are sent between the two to ensure that everyone is actually who they claim to be.

Then, if the handshake has been successful the protocol describes how the client and the server should encrypt and decrypt the data in a secure way. If you want a detailed explanation, check out this great introduction by Cloudflare.

Most of the data point used to build the fingerprint are from the TLS handshake and if you want to see what does a TLS fingerprint looks like, you can go visit this awesome online database.

On this website, you can see that the most used fingerprint last week was used 22.19% of the time (at the time of writing this article).

This number is very big and at least two orders of magnitude higher than the most common browser fingerprint. It actually makes sense as a TLS fingerprint is computed using way fewer parameters than a browser fingerprint.

Those parameters are, amongst others:

• TLS version
• Handshake version
• Cipher suites supported
• Extensions

If you wish to know what your TLS fingerprint is, I suggest you visit this website.

How do I change it?

Ideally, in order to increase your stealth when scraping the web, you should be changing your TLS parameters. However, this is harder than it looks.

Firstly, because there are not that many TLS fingerprints out there, simply randomizing those parameters won't work. Your fingerprint will be so rare that it will be instantly flagged as fake.

Secondly, TLS parameters are low-level stuff that rely heavily on system dependencies. So, changing them is not straight-forward.

For examples, the famous Python requests module doesn't support changing the TLS fingerprint out of the box. Here are a few resources to change your TLS version and cypher suite in your favorite language:

• Python with HTTPAdapter and requests
• NodeJS with the TLS package
• Ruby with OpenSSL

Keep in mind that most of these libraries rely on the SSL and TLS implementation of your system, OpenSSL is the most widely used, and you might need to change its version in order to completely alter your fingerprint.

Emulate Human Behaviour: Proxy, Captcha Solving and Request Patterns

Proxy Yourself

A human using a real browser will rarely request 20 pages per second from the same website. So if you want to request a lot of page from the same website you have to trick the website into thinking that all those requests come from different places in the world i.e: different I.P addresses. In other words, you need to use proxies.

Proxies are not very expensive: ~1$ per IP. However, if you need to do more than ~10k requests per day on the same website, costs can go up quickly, with hundreds of addresses needed. One thing to consider is that proxy IPs needs to be constantly monitored in order to discard the one that is not working anymore and replace it.

There are several proxy solutions on the market, here are the most used rotating proxy providers: Luminati Network, Blazing SEO and SmartProxy.

There is also a lot of free proxy lists and I don’t recommend using these because they are often slow and unreliable, and websites offering these lists are not always transparent about where these proxies are located. Free proxy lists are usually public, and therefore, their IPs will be automatically banned by the most website. Proxy quality is important. Anti-crawling services are known to maintain an internal list of proxy IP so any traffic coming from those IPs will also be blocked. Be careful to choose a good reputation. This is why I recommend using a paid proxy network or build your own.

Another proxy type that you could look into is mobile, 3g and 4g proxies. This is helpful for scraping hard-to-scrape mobile first websites, like social media.

To build your own proxy you could take a look at scrapoxy, a great open-source API, allowing you to build a proxy API on top of different cloud providers. Scrapoxy will create a proxy pool by creating instances on various cloud providers (AWS, OVH, Digital Ocean). Then, you will be able to configure your client so it uses the Scrapoxy URL as the main proxy, and Scrapoxy it will automatically assign a proxy inside the proxy pool. Scrapoxy is easily customizable to fit your needs (rate limit, blacklist …) it can be a little tedious to put in place.

You could also use the TOR network, aka, The Onion Router. It is a worldwide computer network designed to route traffic through many different servers to hide its origin. TOR usage makes network surveillance/traffic analysis very difficult. There are a lot of use cases for TOR usage, such as privacy, freedom of speech, journalists in a dictatorship regime, and of course, illegal activities. In the context of web scraping, TOR can hide your IP address, and change your bot’s IP address every 10 minutes. The TOR exit nodes IP addresses are public. Some websites block TOR traffic using a simple rule: if the server receives a request from one of the TOR public exit nodes, it will block it. That’s why in many cases, TOR won’t help you, compared to classic proxies. It's worth noting that traffic through TOR is also inherently much slower because of the multiple routing.

Captchas

Sometimes proxies will not be enough. Some websites systematically ask you to confirm that you are a human with so-called CAPTCHAs. Most of the time CAPTCHAs are only displayed to suspicious IP, so switching proxy will work in those cases. For the other cases, you'll need to use CAPTCHAs solving service (2Captchas and DeathByCaptchas come to mind).

While some Captchas can be automatically resolved with optical character recognition (OCR), the most recent one has to be solved by hand.

If you use the aforementioned services, on the other side of the API call you'll have hundreds of people resolving CAPTCHAs for as low as 20ct an hour.

But then again, even if you solve CAPCHAs or switch proxy as soon as you see one, websites can still detect your data extraction process.

Request Pattern

Another advanced tool used by websites to detect scraping is pattern recognition. So if you plan to scrape every IDs from 1 to 10 000 for the URL www.example.com/product/, try to not do it sequentially or with a constant rate of request. You could, for example, maintain a set of integer going from 1 to 10 000 and randomly choose one integer inside this set and then scrape your product.

Some websites also do statistic on browser fingerprint per endpoint. This means that if you don't change some parameters in your headless browser and target a single endpoint, they might block you.

Websites also tend to monitor the origin of traffic, so if you want to scrape a website if Brazil, try to not do it with proxies in Vietnam.

But from experience, I can tell you that rate is the most important factor in “Request Pattern Recognition”, so the slower you scrape, the less chance you have of being discovered.

Emulate Machine Behaviour: Reverse engineering of API

Sometimes, the server expect the client to be a machine. In these cases, hiding yourself is way easier.

Reverse engineering of API

Basically, this “trick” comes down to two things:

1. Analyzing a web page behaviour to find interesting API calls
2. Forging those API calls with your code

What Is The Meaning Of Web Scraping

For example, let's say that I want to get all the comments of a famous social network. I notice that when I click on the “load more comments” button, this happens in my inspector:

Notice that we filter out every requests except “XHR” ones to avoid noise.

When we try to see which request is being made and which response do we get… - bingo!

Now if we look at the “Headers” tab we should have everything we need to replay this request and understand the value of each parameters. This will allow us to make this request from a simple HTTP client.

The hardest part of this process is to understand the role of each parameter in the request. Know that you can left-click on any request in the Chrome dev tool inspector, export in HAR format and then import it in your favorite HTTP client, (I love Paw and PostMan).

This will allow you to have all the parameters of a working request laid out and will make your experimentation much faster and fun.

Reverse-Engineering of Mobile Apps

The same principles apply when it comes to reverse engineering mobile app. You will want to intercept the request your mobile app make to the server and replay it with your code.

Doing this is hard for two reasons:

• To intercept requests, you will need a Man In The Middle proxy. (Charles proxy for example)
• Mobile Apps can fingerprint your request and obfuscate them more easily than a web app

Python Web Page Scraping

For example, when Pokemon Go was released a few years ago, tons of people cheated the game after reverse-engineering the requests the mobile app made.

What they did not know was that the mobile app was sending a “secret” parameter that was not sent by the cheating script. It was easy for Niantic to then identify the cheaters. A few weeks after, a massive amount of players were banned for cheating.

Also, here is an interesting example about someone who reverse-engineered the Starbucks API.

Conclusion

Here is a recap of all the anti-bot techniques we saw in this article:

I hope that this overview will help you understand web-scraping and that you learned a lot reading this article.

We leverage everything I talked about in this post at ScrapingBee. Our web scraping API handles thousands of requests per second without ever being blocked. If you don’t want to lose too much time setting everything up, make sure to try ScrapingBee. The first 1k API calls are on us :).

We recently published a guide about the best web scraping tools on the market, don't hesitate to take a look!