A Primer on Web Scraping in R
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.
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/
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
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.