In recent months, you may have started to hear more about the programming language R. Although hardly a new language, it’s popularity has spiked in recent years as developers have sought to take advantage of its statistical and machine learning applications. The use of R by big tech companies such as Facebook, Google and Uber has also helped it leap into the public domain. 

What Is R Programming?

R is a programming language which has become increasingly popular over the past decade owing to its statistical and graphical capabilities. R has a wide range of extensions, is simple to replicate and is very easy to create packages for solving certain issues. Added to the fact that R is open source, it’s easy to see why this language is such an attractive proposition for programmers. R is also popular with academic institutions given its statistical capabilities.

Originally created from the statistical programming language S in combination with lexical scoping, R was named partly for its creators Ross Ihaka and Robert Gentleman and also to reflect its derivation from S. The first version of R was made public on 24th April 1997 with release 0.49, with release 3.5 being the latest to go live, doing so on the same date 21 years later.

What Can You Do With R Programming?

Primarily, R is used for three main tasks:

  • Data analysis
  • Machine learning
  • Statistical inference

While there are other languages that can perform these functions, the main advantage of R is that it can do so with a bespoke approach rather than relying on pre-existing statistical templates. This feature can make R more time consuming, but ultimately it offers programmers great insight and scope to analyse the source data. 

Should I Learn R Programming?

R is often regarded as the most useful programming languages for data science, particularly with a focus on business. The flexibility of R alongside its capacity to build in points to check for errors (unlike basic statistical programmes such as Microsoft Excel or Google Analytics). 

The main drawback to R is that it is generally deemed to take much longer to learn than other languages. The result of this can be that R programmers either need to nurtured by a company over a few years or be recruited at greater cost than other programmers with similar experience. 

What’s more, with recent developments to its learning modules, R is beginning to move past the early perceptions of being near-impossible to learn. Most R users tend to argue that while the language is unorthodox, it isn’t too difficult to pick up.

The one caveat to this is that R can be confusing to learn alongside other languages used for statistical modelling such as Python. It’s generally recommended that people pick just one or wait until they are proficient in Python before moving on to R.

In Summary

Understanding how to use R is hugely advantageous for any company or institution looking to bore down to the granular details when performing data analysis. Although somewhat unusual in its composition, the upside for anyone looking to learn R programming is that if you stick at it, you could find yourself with a fairly niche skill that many large companies will be looking for in their employees. 


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