With the boom of e-commerce, it has become increasingly necessary to follow the pricing of products set by competitors. One method to do so involves web scraping. Web scraping has become particularly useful, as it can provide product data from e-commerce sites.
However, many scripting and programming languages work for web scraping, and it can be confusing to know which one to work with. Due to its simplicity, one of the most popular web scraping tools is the language program.
What Is R?
R is a programming language used by data scientists for its graphical and statistical capabilities.
One can perform tasks such as data visualization, modeling, calculations, training, testing, analysis, clustering, regression, classification, and more with the various open-source packages.
Web Scraping in R is another application of this programming language.
What Is Web Scraping?
Web scraping, or web data extraction, is an automated process whereby data and information are obtained from websites.
During web scraping, the software user, web crawler, or bot will fetch a web page and extract the information.
It downloads the web page and then parses, searches, and formats the data into a spreadsheet or database.
Lastly, a data scientist will retrieve the information from the database to analyze. Using this information, they can help a company make business decisions.
Web Scraping in R
Before you can start web scraping in R, you need to download the software. Start by downloading the latest version of R to your computer. Click on the file to unpack and copy the necessary files to your hard drive. Make sure you accept the default settings.
Then, you will have to get RStudio. RStudio is an open-source, integrated environment that will help you use R. Download the file, click on it, and install it according to the instructions.
You could acquire information about product titles, pricing, description, size, color, and rating. However, you will have an easier time exploring e-commerce data by using R packages.
There are a few packages that will help with web scraping. You can install them using the install.packages() function. We recommend downloading selectr, xml2, rvest, stringr, and jsonlite. These packages contain specialized functions to explore e-commerce data.
You need the HTML version of a web page before you can start scraping it. On many browsers, you can right-click the page and press “View Page Source” to get the HTML version. You may also find the source under the “Inspect Element,” “View Source,” or “Show Page Source” tabs.
Fetching E-Commerce Data in R
Begin by installing and loading rvest, xml2, jsonlite, and stringr. Enter the URL in the command window, store it to a variable, and use read_html() to gather the data.
Now, you can use HTML tags to extract specific data. On your browser, use “Inspect Element” or “View Frame Source” to locate parts of the HTML code.
On RStudio, use html_nodes(), html_text(), and head() to display specific information from the HTML code. You can clean it up using str_replace_all(). Repeat for all of the product data you need.
Next, use the toJSON() and cat() functions to get the data in the JSON format and print it. This step makes it more readable.
You can repeat this process for as many websites and products as you need to gather information for analysis.
Analyzing the Data
There are countless ways to analyze data using R. It is known for its extensive graphing libraries that will help you represent your data in a way that non-data scientists can understand.
Some of the best libraries to install and explore include:
R makes web scraping and analysis easy, especially for e-commerce. You can readily gather and structure data in a readable fashion to make better business decisions.
If you want to compare your product’s performance to similar ones on the market or prices between e-commerce websites, you can use libraries in R to gather all of the information you need.
Keep in mind that some websites have anti-scraping policies that will block you from viewing product information. Also, brush up on the ethics and legalities surrounding web scraping and analysis.