Web scraping is an advanced task that not many people perform. Web scraping with R is, certainly, technical and advanced programming. An adequate understanding of R is essential for web scraping in this way. To start with, R is a language for statistical computing and graphics. What exactly is web scraping or web mining or web harvesting? It is a technique for extracting data from websites. Remember, websites contain wealth of useful data but designed for human consumption and not data analysis.

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.

The first step is to look at the source you want to scrape. Pull up the “developer tools” section in your favorite web browser and look at the page. Can you find the data you’re looking for?

• If the data is available as a CSV file, you can read it directly from the web.
• If the web page is simple, you can parse it using Readlines() and RCurl package.
• For complex pages, consider using the rvest package to target slices of the page using CSS tags. Web developers use CSS tags (Cascading Style Sheets) to format and decorate content). They are a good way to go after data on news sites and Wikipedia.
• Trying to grab data from a site that uses AJAX? Never fear, this is actually very easy – here’s how to grab data using JSON.

Looking for ways to dig deeper into this topic?

• Check out our list of suggested projects to master web scraping!

By Perceptive Analytics

The more data you collect, the better your models, but what if the data you want resides on a website? This is the problem of social media analysis when the data comes from users posting content online and can be very unstructured. While there are some websites who support data collection from their web pages and have even exposed packages and APIs (such as Twitter), most of the web pages lack the capability and infrastructure for this. If you are a data scientist who wants to capture data from such web pages then you wouldn’t want to be the one to open all these pages manually and scrape the web pages one by one. To push away the boundaries limiting data scientists from accessing such data from web pages, there are packages available in R. They are based on a technique known as ‘Web scraping’ which is a method to convert the data, whether structured or unstructured, from HTML into a form on which analysis can be performed. Let us look into web scraping technique using R.

Harvest Data with “rvest”

Before diving into web scraping with R, one should know that this area is an advanced topic to begin working on in my opinion. It is absolutely necessary to have a working knowledge of R. Hadley Wickham authored the rvest package for web scraping using R which I will be demonstrating in this article.The package also requires ‘selectr’ and ‘xml2’ packages to be installed. Let’s install the package and load it first.

The way rvest works is straightforward and simple. Much like the way you and me manually scrape web pages, rvest requires identifying the webpage link as the first step. The pages are then read and appropriate tags need to be identified. We know that HTML language organizes its content using various tags and selectors. These selectors need to be identified and marked so that their content is stored by the rvest package. We can then convert all the scraped data into a data frame and perform our analysis. Let’s take an example of capturing the content from a blog page - the PGDBA wordpress blog for analytics. We will look at one of the pages from their experiences section. The link to the page is: http://pgdbablog.wordpress.com/2015/12/10/pre-semester-at-iim-calcutta/

As the first step mentioned earlier, I store the web address in a variable url and pass it to the read_html() function. The url is read into memory similar to the way we read csv files using read.csv() function.

Not All Content on a Web Page is Gold - Identifying What to Scrape

Web scraping starts after the url has been read. However, a web page can contain a lot of content and we may not need everything. This is why web scraping is performed for targeted content. For this, we use the selector gadget. The selector gadget now has an extension in chrome and is used to pinpoint the names of the tags which we want to capture. If you don’t have the selector gadget and have not used it, you can read about it using the command in R. You can also install the gadget by going to the website http://selectorgadget.com/

Web Scraping In R

After installing the selector gadget, open the webpage and click on the content which you want to capture. Based on the content selected, the selector gadget generates the tag which was used to store it in HTML. The content can then be scraped by mentioning the tag (also known as CSS selector) in html_nodes() function and converting it into html_text. The sample code in R looks like this:

Simple! Isn’t it? Let’s take a step further and capture the content our target webpage!

Scraping Your First Webpage

I choose a blog page because it is all text and serves as a good starting example. Let’s begin by capturing the date on which the article was posted. Using the selector gadget, clicking on the date revealed that the tag required to get this data was .entry-date

Web Scraping In Rstudio

It’s an old post! The next step is to capture the headings. However, there are two headings here. One is the title of the article and other is the summary. Interestingly, both of them can be identified using the same tag. The beauty of rvest package comes here that it can capture both of the headings in one go. Let’s perform this step

The main title is stored as the second value in the title_summary vector. The first value contains the summary of the data. With this, the only section remaining is the main content. This is probably organized using the paragraph tag. We will use the ‘p’ tag to capture all of it.

Web Scraping In R Tutorial