Web Scraping using R Language

Web scraping is a technique that allows us to automatically extract information from websites, in situations where the data we need isn’t available through downloadable datasets or public APIs (Application Programming Interfaces). Instead of manually copying and pasting content, web scraping uses code to fetch and parse the structure of a web page.

In this article, we’ll use the rvest package in R, which simplifies the process of web scraping.

Implementation of Web Scraping using R

We will use rvest library in R. Install the package rvest in your R Studio using the following code. 

install.packages(‘rvest’)

To simplify web scraping, we can use an open-source browser extension called SelectorGadget. It helps identify CSS selectors for extracting specific elements from a webpage.

You can install SelectorGadget. Once installed (preferably on Google Chrome), it will appear in the browser’s extension bar at the top right.

1. Import rvest libraries

We will import the rvest library.

library(rvest)

2. Read the Webpage

We will read the HTML code from the webpage using read_html(). Consider this webpage as a example.

webpage = read_html("https://www.geeksforgeeks.org /\
data-structures-in-r-programming")

3. Scrape Data From the Webpage

Now, let’s start by scraping the heading section. We will use SelectorGadget to identify the specific CSS selector that wraps the heading. Simply click on the extension in your browser and then click on the heading element, which will highlight the corresponding selector needed for scraping.

# Using CSS selectors to scrape the heading section
heading = html_node(webpage, '.entry-title')

text = html_text(heading)
print(text)

Output: 

[1] “Data Structures in R Programming”

Now, let’s scrape the all paragraph fields.

# Using CSS selectors to scrape 
paragraph = html_nodes(webpage, 'p')

pText = html_text(paragraph)

print(head(pText))

Output:

[1] “A data structure is a particular way of organizing data in a computer so that it can be used effectively. The idea is to reduce the space and time complexities of different tasks. Data structures in R programming are tools for holding multiple values. ” 
[2] “R’s base data structures are often organized by their dimensionality (1D, 2D, or nD) and whether they’re homogeneous (all elements must be of the identical type) or heterogeneous (the elements are often of various types). This gives rise to the five data types which are most frequently utilized in data analysis. the subsequent table shows a transparent cut view of those data structures.” 
[3] “The most essential data structures used in R include:” 
[4] “” 
[5] “A vector is an ordered collection of basic data types of a given length. The only key thing here is all the elements of a vector must be of the identical data type e.g homogeneous data structures. Vectors are one-dimensional data structures.” 
[6] “Example:” 

Complete Code Block

library(rvest)
webpage = read_html("https://www.geeksforgeeks.org /
data-structures-in-r-programming")

heading = html_node(webpage, '.entry-title')
text = html_text(heading)
print(text)

paragraph = html_nodes(webpage, 'p')
pText = html_text(paragraph)
print(head(pText))

Output:

[1] “Data Structures in R Programming”

[1] “A data structure is a particular way of organizing data in a computer so that it can be used effectively. The idea is to reduce the space and time complexities of different tasks. Data structures in R programming are tools for holding multiple values. ” [2] “R’s base data structures are often organized by their dimensionality (1D, 2D, or nD) and whether they’re homogeneous (all elements must be of the identical type) or heterogeneous (the elements are often of various types). This gives rise to the five data types which are most frequently utilized in data analysis. the subsequent table shows a transparent cut view of those data structures.” [3] “The most essential data structures used in R include:” [4] “” [5] “A vector is an ordered collection of basic data types of a given length. The only key thing here is all the elements of a vector must be of the identical data type e.g homogeneous data structures. Vectors are one-dimensional data structures.” [6] “Example:” 

Applications of Web scraping

1. Price comparison: Automatically gathers and compares product prices across multiple online retailers.
2. Real estate research: Extracts property listings (price, location, size, bedrooms, bathrooms) from sites like Zillow or Redfin.
3. Trend spotting: Identifies patterns in pricing and availability over time and across regions.
4. Visualization: Generates clear charts and maps to present market insights and support decision‑making.