# Why R programming is must for a Data Science Beginner

This guide is designed to explain why learning R is worth your efforts and how it will jumpstart your journey to a career in data science.

When learning a new technology, the first question that pop-ups to mind is…Why should I learn this? Why R? Is it worth investing my time and effort?

That’s OK! We are going to share with you answers to these questions and will try to clear all your doubts about the importance of learning R programming.

You apparently have an idea about what is data science and how big companies use data and convert them into business insight.

But the question here is how this raw data get processed and turned into information.

### A Brief Introduction to R

R is called the Golden Child of data science and with a sound reason. That’s why Chief Economist at Google says

“I keep saying that the sexy job in the next 10 years will be statisticians and, I’m not kidding.”

R is a statistical programming language. Its primary function is statistical computing. It is widely used in various industries to analyze structured and unstructured data. It is a popular skill amongst prominent data analysts and data scientists.

R is coveted by some of the biggest companies out there, like Facebook, Google, and Twitter. Companies have a whole business model based on it. R’s commercial applications increase every day. Its sheer versatility is a significant reason for its rapid increase in popularity.

Data is the new raw material of business, the need for processing this data is in high demand nowadays.

Before going further, let’s look at the latest features of R programming to compare it with other languages.

Let’s explore the reasons why R programming is a must-have skill for any data scientist and analyst:

## 16 Reasons Why R Programming

There are many reasons to learn R, and we have listed the major ones that will surely answer your question as to why learn R.

### 1. Statistic Computing

There is no better language or tool than R for statistical operations on data. R is the primary language for simple calculations of mean or medians. Simple R functions can make complex statistical models.

ANZ bank uses R to fit models for mortgage loss. The Bank Of America uses R for financial reporting.

### 2. Open-source

R is an open-source language. Thus, it can be used by anyone, anywhere, at any time. You can contribute to R and any of its available libraries, make new libraries for whatever functionality you wish to add, edit the code and add modifications to it.

### 3. Community Support

R has over 2 million users worldwide. The R community is massive as, well as active. It makes contributions to improve the R development environment continuously.

No matter how large or complex the project, there are users from all around the world to help and support you. You can share your ideas and connect with like-minded people. You can also collaborate on exciting and innovative projects.

### 4. Exhaustive Library Collection

CRAN (Comprehensive R Archive Network) houses the packages and libraries used to extend R’s capabilities. The R community is tirelessly working to improve the R software package’s abilities.

Thus, CRAN houses more than 10,000 different libraries and packages. There are packages in R for:

• Database interactivity (dbplyr, odbc).
• Connecting with other languages (Rjava, Reticular).
• Data manipulation (dplyr, tidyr).
• Handling big data (sparklyr).
• Deep learning (keras, TensorFlow).
• High-end machine learning (H2O).
• Data visualization (ggplot2).
• Communicating results and making elegant reports (Rmarkdown, shiny) and much more.

### 5. Compatible with Other Programming Languages

Most functions and packages in R are present in R itself. For computationally heavy tasks, other languages like C, C++, and FORTRAN are also used.

Other languages like .NET, Java, Python can directly manipulate objects.

Explore the Pros & Cons of R and check what advantages it provides over other languages

### 6. Eye-catching Visualizations

Visualizing and presenting data in an eye-catching and elegant manner is very important in today’s data and business savvy world.

R is a powerhouse when it comes to creating production quality graphs and visuals. R packages like ggplot2, plotly, and ggvis create detailed and attractive visualizations.

The New York Times uses R to make graphics and visualizations of their data.

With packages like RHIVE, RHIPE, and Rhadoop, we can team up R and Hadoop to complement each other for big-data analysis and visualizations.

R’s powerful statistical computing and Hadoop’s data storage and processing power make an ideal solution for big-data analysis.

Ford Motor Company uses R along with Hadoop to process customer feedback. This helps them in business decision making and improving their design choices.

### 8. Interactive Web-apps

You can make interactive web apps in R using the shiny package. These web-apps showcase your data, results, and visualizations.

We can host these apps on their own or include them in reports made by R markdown. It allows your users to interact with your analysis and data.

### 9. Cross-platform Compatibility

R can operate with any software and hardware configuration. It supports a wide variety of operating systems. Irrespective of its environment, R delivers consistent results.

### 10. Comprehensive Environment

R has a comprehensive development environment. It is useful for statistical computing as well as software development.

R is an object-oriented programming language. It can be used for data analytics, for making reports, to develop interactive web-apps, and to make software packages as well.

Any confusion in the article why R till now? Comment down below.

### 11. Distributed Computing

R has packages like ddR and multiDplyr which can be used for distributed or parallel computing. This enables R to process large data sets by splitting tasks among different nodes. This increases processing speed and efficiency.

### 12. Running Code without a Compiler

R is an interpreted language, which means that it does not need a compiler to make a program from the code. R interprets the provided code into lower-level calls and pre-compiled code.

### 13. Machine Learning

R can be used for machine learning as well. Predictive analysis, sentiment analysis, and many other machine learning techniques are possible with R. Facebook use R for predictive analysis. They also use R for sentiment analysis of their users.

Navigate through the sidebar in left to understand R thoroughly.

### 14. R Applications in Various Industry Sectors

Every major industry sector uses R for many purposes. Few industries using R are :

• Financial firms use it for risk assessment, market predictions, and building economic models.
• Pharmaceutical companies using it for drug research and analyzing genetic sequences.
• Social media companies use R to perform behavior analysis of their users. They also use it to improve their feed and post suggestions.
• Search engines use it to improve their search results.
• E-commerce companies use it to identify potential clients and for targeted advertising. R is also useful for analyzing customer sentiment and feedback.
• Telecom companies use R for subscriber profiling and personalized advertising.

### 15. Used by Many International Companies

Some of the biggest brands and companies across the world utilize R for data analytics. Here are some of the companies that use R and hire R programmers every year.

And that is not even all of them! Many companies and firms from all sectors use R for one purpose or another.

### 16. High Profile Jobs in Every Industrial Sector

Data analysts, financial analysts, business analysts, business intelligence experts, and quantitative analysts are some of the roles that require R programming.

There has been a steady increase in requirements for R programmers in every sector and industry worldwide.

As companies are trying to gather more and more data, the need for data scientists is rapidly increasing, and that’s why the demand for skilled R developers is at an all-time high.

How much does a Data Scientist make?

Source – glassdoor

For a successful career as a data scientist or an analyst, R is a must.

If you are still confused about R programming then do check the article on – What exactly is R?

## Summary

Data Science is the most popular technology in the world today. Since it is mostly comprised of statistics, R is the prerequisite to start from.

If you are looking for an opportunity as a fresher, or want to upgrade your skills for switching your profile to Data Science, R is the solution to all your problems.

The features that R offers make it an obvious choice for data science and business intelligence. With the rise in importance of data science, R’s popularity is on an explosive rise as well.

We learned the different reasons why learning R programming is an essential skill for a data scientist.

Sneak into Career Opportunities in R Programming & ease your way to become a data scientist

Still not convinced why learn R? Ask us!!

Keep learning.