One of the most important things in the field of Data Science is the skill of getting the right data for the problem you want to solve. Data Scientists don’t always have a prepared database to work on but rather have to pull data from the right sources. For this purpose, APIs and Web Scraping are used.
- API (Application Program Interface): An API is a set of methods and tools that allows one’s to query and retrieve data dynamically. Reddit, Spotify, Twitter, Facebook, and many other companies provide free APIs that enable developers to access the information they store on their servers; others charge for access to their APIs.
- Web Scraping: A lot of data isn’t accessible through data sets or APIs but rather exists on the internet as Web pages. So, through web-scraping, one can access the data without waiting for the provider to create an API.
What’s Web Scraping?
Web scraping is a technique to fetch data from websites. While surfing on the web, many websites don’t allow the user to save data for private use. One way is to manually copy-paste the data, which both tedious and time-consuming. Web Scraping is the automatic process of data extraction from websites. This process is done with the help of web scraping software known as web scrapers. They automatically load and extract data from the websites based on user requirements. These can be custom built to work for one site or can be configured to work with any website.
Implementation of web scraping using R
There are several web scraping tools out there to perform the task and various languages too, having libraries that support web scraping. Among all these languages, R is considered as one of the programming languages for Web Scraping because of features like – a rich library, easy to use, dynamically typed, etc. The commonly used web Scraping tools for R is rvest.
- Install the package rvest in your R Studio using the following code.
- Having, knowledge of HTML and CSS will be an added advantage. It’s observed that most of the Data Scientists are not very familiar with technical knowledge of HTML and CSS. Therefore, let’s using an open-source software named Selector Gadget which will be more than sufficient for anyone in order to perform Web scraping. One can access and download the Selector Gadget extension here. Consider that one has this extension installed by following the instructions from the website. Also, consider one using Google chrome and he/she can access the extension in the extension bar to the top right.
Steps involved in Web Scraping:
- Step 1: Before started coding import rvest libraries to your R Studio.
- Step 2: Read the HTML code from the webpage. Consider this webpage.
- Step 3: Now, let’s start by scraping the heading field. For that, use the selector gadget to get the specific CSS selectors that enclose the heading. One can click on the extension in his/her browser and select the heading field with the cursor.
Once one knows the CSS selector that contains the heading, he/she can use this simple R code to get the heading.
# Using CSS selectors to scrape the heading section
# Converting the heading data to text
 "Data Structures in R Programming"
- Step 4: Now, let’s scraping the all paragraph fields. For that did the same procedure as we did before.
Once one knows the CSS selector that contains the paragraphs, he/she can use this simple R code to get all the paragraphs.
# Using CSS selectors to scrape
# all the paragraph section
# Note that we use html_nodes() here
# Converting the heading data to text
# Print the top 6 data
 “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. ”
 “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.”
 “The most essential data structures used in R include:”
 “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 homogenous data structures. Vectors are one-dimensional data structures.”
The complete R code is given below.
- Histograms in R language
- Datatypes in R language
- Boxplots in R Language
- Scatter plots in R Language
- Introduction to R Programming Language
- What Are the Tidyverse Packages in R Language?
- Interesting Facts about R Programming Language
- Differences Between MATLAB and R Programming Language
- Rounding off values in R Language - round() Function
- Converting a List to Vector in R Language - unlist() Function
- Getting Multiplication of the Objects passed as Arguments in R Language - prod() Function
- Calculate Square root of a number in R Language - sqrt() Function
- Convert elements of a Vector to Strings in R Language - toString() Function
- Changing row and column values of a Matrix in R Language - sweep() function
- Remove Leading whitespaces from a String in R Language - trimws() Function
- Modify values of a Data Frame in R Language - transform() Function
- Combine Vectors, Matrix or Data Frames by Columns in R Language - cbind() Function
- Get the natural logarithm of the beta value of Non-Negative numeric vectors in R Language - lbeta() Function
- Combine Vectors, Matrix or Data Frames by Rows in R Language - rbind() Function
- Case conversion of a String in R Language - toupper(), tolower(), casefold() and cartr() Function