The rise of R EARL conference 2015

Nick Mandella our consultant managing the role
Posting date: 11/2/2015 1:21 PM
There is a fundamental shift happening in the analytics space at the moment, one which is seeing the rise of open-source software staking its claim on the sector. More frequently we at Harnham are seeing companies of all sizes introducing open-source software such as R, Python and Hadoop. Moving away from the traditional analytics tools of the past – most notably, SAS. This dramatic shift is something which has been comfortably reinforced at the EARL conference 2015.  Back by popular demand, it hosts a number of key figures in the analytics space to showcase the latest trends and topics in R.

Over the past decade R programming has arguably become the most important tool for statistical analysis, data visualisation and predictive modelling, used by statisticians and data scientists to name a few, in both academia and the commercial world.

The Benefit of Being Open

So why is R so great? Well first of all, being open-source, it’s FREE – anyone can use it. Who doesn't like stuff that comes for free? After this, you also have a robust tool that can manage any statistical task whilst providing the ability to deliver high quality visualisations. Something nicely illustrated by Joe Cheng’s presentation on the increasingly popular applications of Rshiny. On top of this, once you know how to, it’s easy to use R, and gives fast results. Romain Francois and Dirk Eddelbuettel’s ‘Rccp’ package is a perfect example of this, and has become the most widely used R package. Seamlessly combining the power of C++ and an easy to approach API that lets you write high-performance code it’s easy to see why.

Finally (and probably most importantly) its open-source nature means it becomes a collaborative effort to constantly find ways to improve its use, keeping the tool ahead of the game. With over 2 million users worldwide experimenting, finding bugs and working together as a community. R manages to stay at the forefront of its field. A tool of this nature has thrived as well as similar projects, such as Linux and MySQL; in culture; which has collaboration/brainstorming and sharing information as the key to its continued furtherance and enrichment.

One Step Ahead of the Competition

Combine these factors, and you can see why R has become one of – if not the – most popular analytics software tools right now. Both companies and universities alike are jumping on the bandwagon, meaning that more and more analysts are coming into the field having added R to their toolbox. What occurs is a snowball effect, sustaining the continued rise of R.

With R thriving it places a huge pressure on others to keep up – can they adapt or will they soon come to play little, if any, part in the analytics space in years’ to come? Whilst there was little talk at the EARL Conference about R’s biggest competitor, it became clear that the battle beginning to take shape, potentially for the years to come is between R and Python. Who will win the fight to become the data scientist’s language of choice? Despite all the talk it is obvious to see that there are pros and cons to both, and use cases whereby one would be more effective than the other. Therefore is it really a war? Or, is the outcome more likely a co-existence?

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