With tons of data already generated, the limitation now is its interpretation for usage. Data Science bridges the gap between the availability of data and the extraction of meaningful insights from the same. The field leverages the power of programming languages such as Python, R, Perl, and others. Similar to others, R plays a critical role in the process and is demanded by a variety of organizations. So, let’s explore the related information about R. 

Here’s what we’ll cover:

• What is the R Language?
• Features of R Language
• Role of R Language in Data Science
• Packages of R Essential for Data Science
• Real-Life Applications of R in Data Science
• Become a Data Scientist with Top-Notch Preparation From Interview Kickstart
• FAQs About R Language

What is the R Language?

It is an open-source language available for utilization on a variety of operating systems and platforms. It was developed by Ross Ihaka and Robert Gentleman in New Zealand and includes a command-line interface. R language is highly extensible, integrated with FORTRAN, Python, C++, and .NET, and available as free software. It is used for data manipulation, calculation, and visualization purposes. The language provides users with objects, functions, and operators and can handle big data for statistical modeling and data analysis. 

It also has wide applicability in clustering, statistical tests, classification, and linear and non-linear modeling. The R language is the implementation of the S programming language. Moreover, currently, it is among the hot trends and, hence, is the most preferred programming language in Data Science.  

Features of R Language

Holding wide importance in multiple fields including Data Science, the features of R are worth the discussion. Here are those: 

• Comprehensive environment for multiple functions 
• Allows distributed computing 
• Support towards extensions, complex statistical calculation, and statistical modeling 
• Easy to handle and understand 
• Runs code in the absence of a compiler 
• Coding interface with DBMS
• Contains quality visualizaiton libraries 
• Allows advanced data analytics 
• Widely used in both industry and academics 
• Handles a variety of data 
• Utilized for web scraping 
• Allows direct operations on vectors, eliminating the requirement of looping 
• Easily extracts data from multiple platforms and formats 

Role of R Language in Data Science

Now you are well-aware of what is the programming language R and its features. We can head towards its role in Data Science. 

• The R language is primarily preferred for its statistical and graphical techniques that allow efficient statistical analysis and aesthetic data visualizaiton. 
• It offers compatibility in cleaning, importing, and data analysis. 
• It comprises an Integrated Development Environment or IDE that eases the handling of software packages
• RStudio is the IDE for R that offers a syntax highlighting editor useful for code execution. 
• It is capable of unstructured data analysis and interfacing with the NoSQL database 
• It supports Data Science processes such as data wrangling, web applications, and non-vector based statistical calculations. 
• The different packages of R are suited for multiple industries 
• It is an advanced language capable of performing complex statistical modeling 
• For candidates clear with fundamentals, handling R is comparatively easier 
• With increasing community, the answers to R-specific queries are easy to find 

Packages of R Essential for Data Science 

Some of the significant R packages for application in Data Science are: 

• Tidyr: For data cleaning and organization 
• Dplyr: For data organization, management, and wrangling 
• Ggplot2: Contributes to data visualization
• Shiny: Supports animals and offers an interactive web interface. 
• Knitr: Effective for generating reports 
• Mlr3: Supports OOP and eases Machine Learning based workflow 
• E10701: Implements functionalities like Naive Bayes, clustering, SVM, transformation, and others 
• randomForest: Specifically used in unsupervised learning in Machine Learning 
• Stringr: Associated with regular expressions and character strings 

Real-Life Applications of R in Data Science

The technological giants have incorporated R for their Data Science based tasks. Here are a few examples of the same: 

IBM: The open computing platform of IBM with the name IBM Watson primarily uses the programming language R. IBM also joined the R consortium group. 

Google: Google utilizes R in multiple of its projects. While it is used for economic forecasting and efficiency in advertising, R is also preferred for analytical operations. An example of the latter is the Google Flu Trends project. Moreover, the combination of Google Analytics and R available as the RGoogle Analytics package is suggested for efficient statistical analysis and better data visualizations. 

Novartis: The pharmaceutical company uses R for clinical data analysis. 

Uber: Uber also uses R for statistical analysis. It uses Shiny, an R package, for charting components. 

Facebook: It primarily uses R for social network analytics and behavior analysis for updating status and profile pictures. It figures out the relationship between the users and their behavior. 

ANZ: The company utilizes R in the banking sector for analytical solutions such as loan default prediction and credit risk analytics. 

T-Mobile: The communication company makes use of R for customer requirement analysis and to provide services accordingly. 

BBC: The BBC uses R primarily for all the data visualization related tasks. It even offers training for the employees to leverage the power of R. 

Become a Data Scientist with Top-Notch Preparation From Interview Kickstart

Considering the mentioned details on R language, the importance of a Data Science career is unnegotiable. With the ease of complex calculations, availability of a wide number of packages and libraries, and multiple other features, the programming language is in high demand. Candidates seeking a career in the profession of Data Scientist should ensure a good command of the language for better opportunities. 

So, are you seeking the best mentors to guide you thoroughly in the language and interview process? Much more is available at Interview Kickstart. With experts from FAANG+ companies currently working at IT giants, they are well aware of the current industry requirements and special areas to focus on to make candidates job-ready. If you are willing to stand out from the crowd with a personalized educational platform, here is the link to register for the webinar for FREE. Find answers to your queries and begin taking steps toward a better career. 

FAQs About R Language

Q1. Which is harder, R or Python?

Both Python and R are comparatively easier to learn languages. 

Q2. Should I learn R programming?

Yes, it is a worthy and fascinating programming language used in wide industries and professions. 

Q3. Why R is not as popular as Python?

The readable syntax in Python is responsible for its popularity. 

Q4. What are the system requirements for R programming? 

The requirements are minimal and enlisted as follows: 

• Intel-compatible platform running from Windows 2003 to Windows 11 
• At least RAM of 256 MB
• Administrative permissions 
• Network connection 

Q5. What should I know to learn R programming language?

The candidates interested in learning can begin the process through a beginner course which does not require previous knowledge. However, basic computer skills and knowledge, data science concepts, statistics, and data analytics contribute to decreasing the time of learning journey. 

Q6. What are other skills required to become a Data Scientist?

Besides the knowledge of programming languages, candidates must be familiar with statistical analysis, data visualization and wrangling, mathematics, Machine Learning, Deep Learning, and handling data to become Data Scientists. Knowledge about data storage and analysis tools is also a must. 

Q7. Are there drawbacks of R?

Yes, the R has certain drawbacks like it is slow, lacks basic security, it is complicated language, and documents are inconsistent.

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