{"id":73600,"date":"2019-12-12T09:41:13","date_gmt":"2019-12-12T04:11:13","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=73600"},"modified":"2019-12-12T09:41:13","modified_gmt":"2019-12-12T04:11:13","slug":"why-r","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/why-r\/","title":{"rendered":"Why R programming is must for a Data Science Beginner"},"content":{"rendered":"

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.<\/strong><\/p>\n

When learning a new technology, the first question that pop-ups to mind is\u2026Why should I learn this? Why R<\/strong>? Is it worth investing my time and effort?<\/p>\n

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.<\/p>\n

You apparently have an idea about what is data science<\/strong> and how big companies use data and convert them into business insight<\/strong>.<\/p>\n

But the question here is how this raw data get processed and turned into information.<\/p>\n

The answer is<\/strong> R<\/strong>. Confused? No worries TechVidvans<\/strong> are here just to help you out.<\/p>\n

A Brief Introduction to R<\/h3>\n

R is called the Golden Child<\/strong>\u00a0of data science and with a sound reason. That’s why Chief Economist at Google says<\/p>\n

\u201cI keep saying that the sexy job in the next 10 years will be statisticians and, I\u2019m not kidding.\u201d<\/em><\/strong><\/p><\/blockquote>\n

R is a statistical programming language<\/strong>. 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.<\/p>\n

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\u2019s commercial applications increase every day. Its sheer versatility is a significant reason for its rapid increase in popularity.<\/p>\n

Data is the new raw material of business, the need for processing this data is in high demand nowadays.<\/p>\n

Before going further, let\u2019s look at the latest features of R<\/strong><\/em><\/a> programming to compare it with other languages.<\/p>\n

Let’s explore the reasons<\/strong> why R programming is a must-have skill for any data scientist and analyst:<\/p>\n

16 Reasons Why R Programming<\/h2>\n

There are many reasons to learn R, and we have listed the major<\/strong> ones that will surely answer your question as to why learn R.<\/p>\n

1. Statistic Computing<\/h3>\n

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.<\/p>\n

ANZ bank uses R to fit models for mortgage loss.<\/strong> The Bank Of America uses R for financial reporting<\/strong>.<\/p>\n

2. Open-source<\/h3>\n

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.<\/p>\n

3. Community Support<\/h3>\n

R has over 2 million<\/strong> users worldwide. The R community is massive as, well as active<\/strong>. It makes contributions to improve the R development environment continuously.<\/p>\n

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

4. Exhaustive Library Collection<\/h3>\n

CRAN<\/strong> (Comprehensive R Archive Network) houses the packages and libraries used to extend R\u2019s capabilities. The R community is tirelessly working to improve the R software package\u2019s abilities.<\/p>\n

Thus, CRAN houses more than 10,000<\/strong> different libraries and packages. There are packages in R for:<\/p>\n