R Programming: What It Means to a Data Scientist

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Programmers commonly have many questions about R, a popular programming language in data science and analysis. R is used all over the world by professionals in the fields of data science, data visualization, data mining, and statistical analysis.

But what exactly is R? Where did it come from? And why is it being used specifically by data science professionals? This article attempts to answer all these questions, including the most important of them all: Should you be learning R as well?


What is R?

R is a free, open-source programming language for statistical computing and graphics that is available under the GNU general public license. One of the most powerful languages for statistical programming and applied machine learning, R is widely used by data scientists, data miners, and statisticians for all sorts of tasks, including data analysis, data visualization, and statistical software development.

R debuted in the programming world in 1993 over two decades ago, when two professors, Ross Ihaka and Robert Gentleman, decided to develop a programming language for their students that would be easy to understand and use. The University of Auckland (New Zealand) professors developed R for statistical computing—hence why it’s recognized as a programming language for mathematicians.

Professional R users work in RStudio, a graphical integrated development environment (IDE) that allows you to write, modify, and execute R scripts.

How does R work?

According to the TIOBE Index, ranks as the 11th most popular programming language in the world. Its success can be credited to its simplicity and flexibility. Most importantly, R does a good job of fulfilling one of the primary goals of a programming language: mirroring the way people think.

Since we as humans tend to think of situations rather than individual numbers, R was developed as a vectorized language—meaning everything you do involves vectors (collections) of numbers. Importantly, R is an extremely flexible and powerful language. You can even call C++ functions in R using special packages developed by its open-source community.

Additionally, as part of its universal appeal, R is compatible with all major operating systems.

Where is R used?

R is commonly used in computational statistics in the hard sciences. It was originally created for the purpose of analyzing data rapidly. Nowadays, R can be used in different fields ranging from astronomy to chemistry, genomics, finance, health care, drug research, and so much more. And with such an active and growing open-source community, R now has nearly 5,000 packages (libraries of functions) that users can install to perform a variety of tasks.

Notably, R is heavily used in some of the best companies that hire data scientists. Google and Facebook data scientists use R in their day-to-day work. But beyond tech giants like Google, Facebook, and Microsoft, R is also used by a wide range of companies, including Bank of America, Ford, TechCrunch, Uber, and Trulia.

Why learn R for data science?

For starters, learning R programming will help you master data science, which comprises three core skills: data manipulation, data visualization, and machine learning. But before you can apply R to these specializations, you need to form a strong foundation. If you are not familiar with the basics of data science, learning SQL is also a good place to start.

Data Manipulation

Sometimes, data in its current form isn’t useful—and that’s where data manipulation comes in handy. In turn, descriptive and inferential statistics are essential for understanding data manipulation. Fortunately, R has some of the best data management libraries; the dplyr package, in particular, is quite good.

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Data visualization

Raw numbers themselves communicate very little to humans because we’re a visual species. This is why data visualization is so important. By translating numbers into pictures, we’re able to communicate ideas more clearly and derive key insights from data. Learning ggplot2—the foremost data visualization package in R—will help you see data in a different light.

Machine learning

Machine learning studies the design of algorithms that machines can learn by observing available data through instructions or long-term experience. Typical machine learning tasks include predictive modeling, clustering, concept learning, and predictive pattern identification.

In summary

R is one of the most popular programming languages among data scientists, analysts, and statisticians. It’s a must-have for data scientists who aspire to join big-data companies like Google and Facebook, and it’s almost essential for data enthusiasts and statisticians looking to start a career in the growing field of big data.

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An important note to beginners: R requires a foundation in statistics, Excel and SQL before it can be mastered. Build the fundamentals with Vertabelo Academy, and kick-start your data science career today!

The post R Programming: What It Means to a Data Scientist appeared first on Vertabelo Academy Blog.

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