Example Of Web Scraping

Example web scraping website Afghanistan.
Jan 05, 2021 Web scraping, also called web data extraction, refers to the technique of harvesting data from a web page through leveraging the patterns in the page’s underlying code. It can be used to collect unstructured information from websites for processing and storage in a structured format.
Robots.txt is a file used by web sites to let ‘search bots’ k now if or how the site should be crawled and indexe d by the search engine.Many sites simply disallow crawling, meaning the site shouldn’t be crawled by search engines or other crawler bots.
Define the VBA variable and assign the data type as “Internet Explorer.” Code: Sub WebScraping.

Web scraping is the way out. It can enable you to scrape web data in an automated fashion and allow you to save the same in a format of your choice. Isn’t it amazing? There are various tools and techniques. But one of the great ways to scrape data in such a manner is to leverage excel for web scraping. You might wonder how but no.

Once you’ve put together enough web scrapers, you start to feel like you can do it in your sleep. I’ve probably built hundreds of scrapers over the years for my own projects, as well as for clients and students in my web scraping course.

Occasionally though, I find myself referencing documentation or re-reading old code looking for snippets I can reuse. One of the students in my course suggested I put together a “cheat sheet” of commonly used code snippets and patterns for easy reference.

I decided to publish it publicly as well – as an organized set of easy-to-reference notes – in case they’re helpful to others.

While it’s written primarily for people who are new to programming, I also hope that it’ll be helpful to those who already have a background in software or python, but who are looking to learn some web scraping fundamentals and concepts.

Useful Libraries

For the most part, a scraping program deals with making HTTP requests and parsing HTML responses.

I always make sure I have requests and BeautifulSoup installed before I begin a new scraping project. From the command line:

Then, at the top of your .py file, make sure you’ve imported these libraries correctly.

Making Simple Requests

Make a simple GET request (just fetching a page)

Make a POST requests (usually used when sending information to the server like submitting a form)

Pass query arguments aka URL parameters (usually used when making a search query or paging through results)

Inspecting the Response

See what response code the server sent back (useful for detecting 4XX or 5XX errors)

Access the full response as text (get the HTML of the page in a big string)

Look for a specific substring of text within the response

Check the response’s Content Type (see if you got back HTML, JSON, XML, etc)

Extracting Content from HTML

Now that you’ve made your HTTP request and gotten some HTML content, it’s time to parse it so that you can extract the values you’re looking for.

Using Regular Expressions

Using Regular Expressions to look for HTML patterns is famously NOT recommended at all.

However, regular expressions are still useful for finding specific string patterns like prices, email addresses or phone numbers.

Run a regular expression on the response text to look for specific string patterns:

Using BeautifulSoup

BeautifulSoup is widely used due to its simple API and its powerful extraction capabilities. It has many different parser options that allow it to understand even the most poorly written HTML pages – and the default one works great.

Compared to libraries that offer similar functionality, it’s a pleasure to use. To get started, you’ll have to turn the HTML text that you got in the response into a nested, DOM-like structure that you can traverse and search

Look for all anchor tags on the page (useful if you’re building a crawler and need to find the next pages to visit)

Look for all tags with a specific class attribute (eg <li>...</li>)

Look for the tag with a specific ID attribute (eg: <div>...</div>)

Look for nested patterns of tags (useful for finding generic elements, but only within a specific section of the page)

Look for all tags matching CSS selectors (similar query to the last one, but might be easier to write for someone who knows CSS)

Get a list of strings representing the inner contents of a tag (this includes both the text nodes as well as the text representation of any other nested HTML tags within)

Return only the text contents within this tag, but ignore the text representation of other HTML tags (useful for stripping our pesky <span>, <strong>, <i>, or other inline tags that might show up sometimes)

Convert the text that are extracting from unicode to ascii if you’re having issues printing it to the console or writing it to files

Get the attribute of a tag (useful for grabbing the src attribute of an <img> tag or the href attribute of an <a> tag)

Putting several of these concepts together, here’s a common idiom: iterating over a bunch of container tags and pull out content from each of them

Using XPath Selectors

BeautifulSoup doesn’t currently support XPath selectors, and I’ve found them to be really terse and more of a pain than they’re worth. I haven’t found a pattern I couldn’t parse using the above methods.

If you’re really dedicated to using them for some reason, you can use the lxml library instead of BeautifulSoup, as described here.

Storing Your Data

Now that you’ve extracted your data from the page, it’s time to save it somewhere.

Note: The implication in these examples is that the scraper went out and collected all of the items, and then waited until the very end to iterate over all of them and write them to a spreadsheet or database.

I did this to simplify the code examples. In practice, you’d want to store the values you extract from each page as you go, so that you don’t lose all of your progress if you hit an exception towards the end of your scrape and have to go back and re-scrape every page.

Writing to a CSV

Probably the most basic thing you can do is write your extracted items to a CSV file. By default, each row that is passed to the csv.writer object to be written has to be a python list.

In order for the spreadsheet to make sense and have consistent columns, you need to make sure all of the items that you’ve extracted have their properties in the same order. This isn’t usually a problem if the lists are created consistently.

If you’re extracting lots of properties about each item, sometimes it’s more useful to store the item as a python dict instead of having to remember the order of columns within a row. The csv module has a handy DictWriter that keeps track of which column is for writing which dict key.

Writing to a SQLite Database

You can also use a simple SQL insert if you’d prefer to store your data in a database for later querying and retrieval.

Learn More

If you’d like to learn more about web scraping, I currently have an ebook and online course that I offer, as well as a free sandbox website that’s designed to be easy for beginners to scrape.

You can also subscribe to my blog to get emailed when I release new articles.

Want to scrape the web with R? You’re at the right place!

We will teach you from ground up on how to scrape the web with R, and will take you through fundamentals of web scraping (with examples from R).

Throughout this article, we won’t just take you through prominent R libraries like rvest and Rcrawler, but will also walk you through how to scrape information with barebones code.

Overall, here’s what you are going to learn:

R web scraping fundamentals
Handling different web scraping scenarios with R
Leveraging rvest and Rcrawler to carry out web scraping

Let’s start the journey!

Introduction

The first step towards scraping the web with R requires you to understand HTML and web scraping fundamentals. You’ll learn how to get browsers to display the source code, then you will develop the logic of markup languages which sets you on the path to scrape that information. And, above all - you’ll master the vocabulary you need to scrape data with R.

We would be looking at the following basics that’ll help you scrape R:

HTML Basics
Browser presentation
And Parsing HTML data in R

So, let’s get into it.

HTML Basics

HTML is behind everything on the web. Our goal here is to briefly understand how Syntax rules, browser presentation, tags and attributes help us learn how to parse HTML and scrape the web for the information we need.

Browser Presentation

Before we scrape anything using R we need to know the underlying structure of a webpage. And the first thing you notice, is what you see when you open a webpage, isn’t the HTML document. It’s rather how an underlying HTML code is represented. You can basically open any HTML document using a text editor like notepad.

HTML tells a browser how to show a webpage, what goes into a headline, what goes into a text, etc. The underlying marked up structure is what we need to understand to actually scrape it.

For example, here’s what ScrapingBee.com looks like when you see it in a browser.

And, here’s what the underlying HTML looks like for it

Looking at this source code might seem like a lot of information to digest at once, let alone scrape it! But don’t worry. The next section exactly shows how to see this information better.

HTML elements and tags

If you carefully checked the raw HTML of ScrapingBee.com earlier, you would notice something like <title>...</title>, <body>...</body etc. Those are tags that HTML uses, and each of those tags have their own unique property. For example <title> tag helps a browser render the title of a web page, similarly <body> tag defines the body of an HTML document.

Once you understand those tags, that raw HTML would start talking to you and you’d already start to get the feeling of how you would be scraping web using R. All you need to take away form this section is that a page is structured with the help of HTML tags, and while scraping knowing these tags can help you locate and extract the information easily.

Parsing a webpage using R

With what we know, let’s use R to scrape an HTML webpage and see what we get. Keep in mind, we only know about HTML page structures so far, we know what RAW HTML looks like. That’s why, with the code, we will simply scrape a webpage and get the raw HTML. It is the first step towards scraping the web as well.

Earlier in this post, I mentioned that we can even use a text editor to open an HTML document. And in the code below, we will parse HTML in the same way we would parse a text document and read it with R.

I want to scrape the HTML code of ScrapingBee.com and see how it looks. We will use readLines() to map every line of the HTML document and create a flat representation of it.

Now, when you see what flat_html looks like, you should see something like this in your R Console:

The whole output would be a hundred pages so I’ve trimmed it for you. But, here’s something you can do to have some fun before I take you further towards scraping web with R:

Scrape www.google.com and try to make sense of the information you received
Scrape a very simple web page like https://www.york.ac.uk/teaching/cws/wws/webpage1.html and see what you get

Remember, scraping is only fun if you experiment with it. So, as we move forward with the blog post, I’d love it if you try out each and every example as you go through them and bring your own twist. Share in comments if you found something interesting or feel stuck somewhere.

While our output above looks great, it still is something that doesn’t closely reflect an HTML document. In HTML we have a document hierarchy of tags which looks something like

But clearly, our output from readLines() discarded the markup structure/hierarchies of HTML. Given that, I just wanted to give you a barebones look at scraping, this code looks like a good illustration.

However, in reality, our code is a lot more complicated. But fortunately, we have a lot of libraries that simplify web scraping in R for us. We will go through four of these libraries in later sections.

First, we need to go through different scraping situations that you’ll frequently encounter when you scrape data through R.

Common web scraping scenarios with R

Access web data using R over FTP

FTP is one of the ways to access data over the web. And with the help of CRAN FTP servers, I’ll show you how you can request data over FTP with just a few lines of code. Overall, the whole process is:

Save ftp URL
Save names of files from the URL into an R object
Save files onto your local directory

Let’s get started now. The URL that we are trying to get data from is ftp://cran.r-project.org/pub/R/web/packages/BayesMixSurv/.

Let’s check the name of the files we received with get_files

Looking at the string above can you see what the file names are?

The screenshot from the URL shows real file names

It turns out that when you download those file names you get carriage return representations too. And it is pretty easy to solve this issue. In the code below, I used str_split() and str_extract_all() to get the HTML file names of interest.

Let’s print the file names to see what we have now:

extracted_html_filenames

Great! So, we now have a list of HTML files that we want to access. In our case, it was only one HTML file.

Now, all we have to do is to write a function that stores them in a folder and a function that downloads HTML docs in that folder from the web.

We are almost there now! All we now have to do is to download these files to a specified folder in your local drive. Save those files in a folder called scrapignbee_html. To do so, use GetCurlHandle().

After that, we’ll use plyr package’s l_ply() function.

And, we are done!

I can see that on my local drive I have a folder named scrapingbee_html, where I have inde.html file stored. But, if you don’t want to manually go and check the scraped content, use this command to retrieve a list of HTMLs downloaded:

That was via FTP, but what about HTML retrieving specific data from a webpage? That’s what our next section covers.

Scraping information from Wikipedia using R

In this section, I’ll show you how to retrieve information from Leonardo Da Vinci’s Wikipedia page https://en.wikipedia.org/wiki/Leonardo_da_Vinci.

Open Source Web Scraper

Let’s take the basic steps to parse information:

Leonardo Da Vinci’s Wikipedia HTML has now been parsed and stored in parsed_wiki.

But, let’s say you wanted to see what text we were able to parse. A very simple way to do that would be:

By doing that, we have essentially parsed everything that exists within the <p> node. And since it is an XML node set, we can easily use subsetting rules to access different paragraphs. For example, let’s say we pick the 4th element on a random name. Here’s what you’ll see:

Reading text is fun, but let’s do something else - let’s get all links that exist on this page. We can easily do that by using getHTMLLinks() function:

Notice what you see above is a mix of actual links and links to files.

You can also see the total number of links on this page by using length() function:

I’ll throw in one more use case here which is to scrape tables off such HTML pages. And it is something that you’ll encounter quite frequently too for web scraping purposes. XML package in R offers a function named readHTMLTable() which makes our life so easy when it comes to scraping tables from HTML pages.

Leonardo’s Wikipedia page has no HTML though, so I will use a different page to show how we can scrape HTML from a webpage using R. Here’s the new URL:

As usual, we will read this URL:

If you look at the page you’ll disagree with the number “108”. For a closer inspection I’ll use name() function to get names of all 108 tables:

Our suspicion was right, there are too many “NULL” and only a few tables. I’ll now read data from one of those tables in R:

Here’s how this table looks in HTML

Awesome isn’t it? Imagine being able to access census, pricing, etc data over R and scraping it. Wouldn’t it be fun? That’s why I took a boring one, and kept the fun part for you. Try something much cooler than what I did. Here’s an example of table data that you can scrape https://en.wikipedia.org/wiki/United_States_Census

Let me know how it goes for you. But it usually isn’t that straightforward. We have forms and authentication that can block your R code from scraping. And that’s exactly what we are going to learn to get through here.

Handling HTML forms while scraping with R

Often we come across pages that aren’t that easy to scrape. Take a look at the Meteorological Service Singapore’s page (that lack of SSL though :O). Notice the dropdowns here

Imagine if you want to scrape information that you can only get upon clicking on the dropdowns. What would you do in that case?

Well, I’ll be jumping a few steps forward and will show you a preview of rvest package while scraping this page. Our goal here is to scrape data from 2016 to 2020.

Let’s check what type of data have been able to scrape. Here’s what our data frame looks like:

From the dataframe above, we can now easily generate URLs that provide direct access to data of our interest.

Now, we can download those files at scale using lappy().

Note: This is going to download a ton of data once you execute it.

Web scraping using Rvest

Inspired by libraries like BeautifulSoup, rvest is probably one of most popular packages in R that we use to scrape the web. While it is simple enough that it makes scraping with R look effortless, it is complex enough to enable any scraping operation.

Let’s see rvest in action now. I will scrape information from IMDB and we will scrape Sharknado (because it is the best movie in the world!) https://www.imdb.com/title/tt8031422/

Awesome movie, awesome cast! Let's find out what was the cast of this movie.

Awesome cast! Probably that’s why it was such a huge hit. Who knows.

Still, there are skeptics of Sharknado. I guess the rating would prove them wrong? Here’s how you extract ratings of Sharknado from IMDB

I still stand by my words. But I hope you get the point, right? See how easy it is for us to scrape information using rvest, while we were writing 10+ lines of code in much simpler scraping scenarios.

Next on our list is Rcrawler.

Web Scraping using Rcrawler

Rcrawler is another R package that helps us harvest information from the web. But unlike rvest, we use Rcrawler for network graph related scraping tasks a lot more. For example, if you wish to scrape a very large website, you might want to try Rcrawler in a bit more depth.

Note: Rcrawler is more about crawling than scraping.

We will go back to Wikipedia and we will try to find the date of birth, date of death and other details of scientists.

Output looks like this:

And that’s it!

You pretty much know everything you need to get started with Web Scraping in R.

Is Web Scraping Legal

Try challenging yourself with interesting use cases and uncover challenges. Scraping the web with R can be really fun!

Example Of Web Browser

While this whole article tackles the main aspect of web scraping with R, it does not talk about web scraping without getting blocked.

If you want to learn how to do it, we have wrote this complete guide, and if you don't want to take care of this, you can always use our web scraping API.