R programming language, the present of Data Science

If at the time we told you that the name of Python comes from the famous British comedy group, tell you that the R programming language is not called that by chance.

R was established in 1993 at the University of Auckland by statisticians Robert Gentleman and Ross Ihaka. 

As you may have noticed, the initials of both researchers begin with "r". But that's not the only reason: the R language derives from the S language and Scheme. So its creators simply decided to change the letter... Curious, isn't it?

Well, in this article we are going to talk to you about the R programming language, the present of Data Science and one of the world's leading statisticians.

What is R as a programming language?

The R programming language is essential for data analysis, both statistical and graphical. It is functional, cross-platform, open source, free and object-oriented.

Consequently, R as a programming language is ideal for all kinds of fields. Including Data Science.

Features to take into account when programming in R

These are some of the most outstanding features of R as a programming language:

• Free software: R is a language with an open source philosophy. That is to say, the code is free, any user can download it and use it. This also helps R to be a collaborative project.
• Interpreted language: It is an interpreted language and not compiled (like Java). Therefore, it is a language that is executed directly, which in turn facilitates data analysis.
• Object-oriented language: This means that different value calculations can be performed at the same time without having to use a complex algorithm.
• Functional: R is based on mathematical functions. From tests, to clustering algorithms, to linear and non-linear models... the options with R are many.
• Multi-platform: This language is available on different operating systems.

Thus, R is very important for statisticsIt is stable and is constantly being updated.

What is R for?

If we say that the R programming language is the present of Data Science, it is for a reason. And that is because this type of programming language is used in different sectors of work such as:

• Statistics
• Bioinformatics
• Financial analysis
• Biomedical research
• Machine Learning

Thus, when asked what the R programming language is for, our answer is clear: for all phases of data analysis.

R makes it possible to acquire data from different sources such as databases (redundancy aside). It also facilitates their organisation: it eliminates duplications, as well as incorrect data, among other possibilities. 

In addition, R is essential for data analysis itself. It generates classification or clustering models, which favours predictions.

Finally, R is also essential in the communication and application of the results achieved: from reports or presentations, to their subsequent use.

R programming language: advantages in Data Science

So why do we say that the "R programming language is the present of Data Science"? Because if you work as a Data ScientistThis interactive language will be really useful for you:

• R is key to generating, visualising and analysing data.
• It generates reports as well as high quality graphics.
• It is available on different operating systems such as Windows, macOS or Linux, among others.
• It encourages flexible working, as R can work with others programming languagesas well as software (Excel, for example).
• It is essential for data processing.

Do you now understand why the R programming language is important in Data Science?

Learning R at the IMMUNE technology institute 

In fact, R is one of the programming languages that you will use in our centre. Together with our teachers, you will learn to use a computer language that is fundamental in Data Science.

IMMUNE's training offer is characterised by its practical approachOur teaching staff is made up of experts from leading companies in the technology sector.

So now you know, train with us. Discover our Data Science Masteralso available as online master's degreeas well as our Data Analytics Bootcamp. 

Become a data science professional with IMMUNE!