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How To Do Web Scraping In R? – A Quick Guide In 2022

web scraping in r

Data is growing exponentially across the globe, and businesses are relying on these data sets for making huge decisions. There are multiple datasets available on the internet that we can use for our projects. When we get an API to scrape data, web scraping becomes the more straightforward thing to do. If we want to get data in a neat format, we need to do web scraping in R.

web scraping in r

However, it is also important to note that most data is not readily available on the internet. We need to have some expertise and knowledge to access that particular data. Therefore, we perform web scraping to get that data from the HTML code of the web page. This article also highlights what an API can do in web scraping tasks. In this article, let’s dig into the concept of web scraping with r.

hypertext markup language to extract href attribute in table data

What Is Web Scraping?

First of all, it is very important to be clear about web scraping data before proceeding with how to do it. As a matter of fact, data sets are mostly present in unstructured format on various web pages.

When we need to use this data, it is important to convert it into a structured format. This is what we call web scraping.

The interesting thing to know is that we can perform web scraping in multiple languages. The most common language used for web scraping is R.

Web scraping is all about gathering data by using technical knowledge and expertise. But why should we gather the data? Continue reading to explore it.

all the titles with src attribute and matching elements in data frames

Why Do We Need Web Scraping?

When we talk about web scraping, the first thing that comes to mind is why we really need to do it. Well, there are immense possibilities to do web scraping for multiple reasons. Some of the main reasons to need web scraping are given below:

  • Price Intelligence
    • Brand and MAP compliance
    • Product trend monitoring
    • Competitir monitoring
    • Revenue optimization
    • Dynamic pricing
  • Market research
    • Competitor monitoring
    • Research and development
    • Optimizing point of entry
    • Market pricing
    • Market trend and analysis
  • Alternative data for finance
    • News Monitoring
    • Public sentiments integrations
    • Estimating the fundamentals of the company
    • Extracting data from SEC filings.
further analysis of page title in google chrome
  • Real estate
    • Understanding the direction of the market
    • Estimation of rental yields
    • Vacancy rates monitoring
    • Appraising property value
  • News and content marketing
    • Sentiment analysis
    • Political campaign
    • Competitor monitoring
    • Analysis of Online Public sentiment
    • Decision-making for Investment
  • Brand monitoring
  • Lead generation
  • Automation in business
  • Map monitoring
single element in r code

Understanding A Web Page: How A Web Page Is Structured?

Before we proceed towards scraping a web page, it is important to know how it is structured. A web page consists of images, links, and texts from a user’s perspective.

But in reality, it consists of a code that our browser interprets. When scraping a web page, we need to deal with that code.

programming languages in this blog post are CSS and HTML

The most common languages to build a web page are CSS and HTML. Our web browsers can recognize these languages easily. Let’s dig into it.

HTML

HTML(Hypertext Markup Language) provides the content and structure of a web page. Unlike R, it is a markup language and not a programming language. We organize tags for different functions in HTML. The Simplest HTML document looks like this:

The signs “< >” form a particular html tag. If we want to add more html tags, we can write it as:

The main tag is the parent tag while the tags within the parent tag are children tags. Children tags are collectively known as siblings.

node refers to rvest functions

CSS

CSS helps us to style the html element of a web page. We can decide on the looks and design of our web pages’ particular html elements using CSS.

Without CSS, the HTML web page is quite simple in text format. Styling refers to the color, font, and position of data stored on a web page.

r programming for specific elements

What Are The Common Web Scraping Scenarios With R?

Some most common web scraping scenarios using R are listed below:

Access Web Data Using R Over FTP

FTP is old but the fastest way to exchange files. We can use the CRAN FTP server if we want to get the list of files. We will filter the list for HTML files. We will do it in the following steps:

Directory Listing

We can get these files using get_files as under:

The screenshot is given below shows more clear results of fetched lists:

Files and directory inside an FTP server

Now we can parse them using str_split() and str_extract_all(). For example:

Now if we want to see what we got now, we will print the names of the files as under:

It gave us files that we want to access and that was only one HTML file.

read data using open source libraries source code

File Downloading

We will use the FTPDownloader function to download files as under:

cURL handle can help us in actual network communication as under:

Now we will call l_ply() as under:

We can also get the data in csv file. And now we are done!

specific data on page number for list object

Scraping Information From Wikipedia Using R

Suppose we want to scrap data for Leonardo Vinci’s Wikipedia page, we will do it as under:

This method works in the following steps:

  1. Using wiki_url to save URL
  2. Making use of function “readLine” for fetching HTML elements and saving them using wiki_read
  3. Parsing code into DOM(Document Object Model) tree using htmlParse() and saving it as parsed_wiki
following code of doctype html

How To Do Web Scraping In R With Rvest? Give Examples.

The Rvest package is the most popular package of R to help us in scraping data.

Its simplicity makes us able to scrape data effortlessly.

At the same time, its complexity helps us in any scraping operation. We can query any data using the CSS selector in the Rvest library.

lister item header of sapply function

Let’s do web scraping for IMDB into a data frame.

We have to figure out the cast as:

Now getting the rating of our movie as under:

This is how it all works in such a simple way.

same steps in pipe operator

What Are The Pre-requisites?

The prerequisites are categorized into two buckets when performing web scraping using R.

  1. First of all, we should have a deep and practical knowledge of R. Then we should ensure the installation of the rvest library. We can use the following function to install it:
  1. Secondly, knowledge of CSS and HTML will be beneficial. Even if we don’t have enough knowledge about it, we can use free web scraping tools such as the Selector gadget extension. But it would be great to have a high knowledge of CSS and HTML to master these skills.
r package at main page

What Is The Rvest Library?

Rvest library helps us to scrap data easily using the R language.

It is one of the tidyverse libraries; therefore, it works well with other libraries present in the bundle.

The package took inspiration from the library BeautifulSoup in python.

And it works using CSS selectors.

data of many websites

How To Scrape Web With Zenscrape Easily?

Zenscrape can handle all problems linked with web scraping.

The documentation is available on its website to perform the whole process.

In this API, we can use an endpoint to fetch the content of our website.

How To Scrape Web With Zenscrape Easily

We use only one parameter in addition to API key in basic usage.

The user needs to add the URL parameter to the request to fetch website data.

There is the usage of standard proxies in this request.

Moreover, it will be counted as one credit in our monthly limit.

We need to generate the response as under:

The web scraping API will accept multiple parameters such as location, premium, URL, render, wait_for, and so many other parameters.

There is also a proxy mode to integrate applications that rely on proxies. Hence, Zenscrape is a proxy scraper.

FAQ

Is R Good for Web Scraping?

Yes. It is the most popular and simplest way to get web-scraped data.

Is Web Scraping Easier in Python or R?

Since R’s ecosystem is wider than Python’s.

We can use it for statistical analysis.

On the other hand, Python is suitable for nonstatistical methods.

Python web scraping APIs are known to be the most reliable and efficient.

What Is Scraping in R?

It is all about extracting, formatting, and finding data for later analysis.

What Does read_HTML Do in R?

It creates an R object that stores information about our target web page.

Which Language Is Best for Web Scraping?

R and Python web scraping are the best languages for web scraping.

All issues relating to web scraping are handled by our web scraping API. Extracting HTML from websites has never been so simple. Register and get the best web scraping experience for free!

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