Business intelligence expert Johnathan Mills, a Senior BI Consultant operating out of Vancouver, Canada thinks that ‘every programming language out there has its strengths and weaknesses. R’s strength is data; it is a language built to analyse and manipulate data. You will see this used by true, hardcore data scientists, but it does not currently have much traction outside of that area.’ (Selig, 2014)
He probably is not R’s number one fan, but as big data is becoming more and more important in modern business I contend that R, for that very reason is worth learning and that ‘yes, absolutely, you should learn R’. R is a popular language amongst data scientists and business analysts and there is a growing demand in the workplace for these skills.
Let me briefly outline ten major advantages to becoming fluent in the R programming language below. These are the main advantages, and probably the biggest of them all is that R Is open source and free to all.
Major Advantage Number One. Worldwide millions of statisticians and data scientists use R to solve statistical and calculation problems from fields as diverse as computational biology to data driven marketing. (Eglin, 2009)
Two. The power of the basic R download is pretty impressive but there are also 4,800 extra packages from repositories dealing with data mining, bio-informatics, econometrics and spatial analysis. All this for free. You are almost guaranteed to be able to download a solution for yourself or your employer.
Three. R produces excellent graphs that are arguably ready to publish as is. These include bar charts, scatter plots, mapping features – the works.
Four. If big data is your area, and this is a growing area – R specialises in this and is faster than other languages.
Five. There is a thriving worldwide R community to whom you can reach out to for answers. New open source solutions are being added every week.
Six. R is completely free and you can install it on as many machines as you like.
Seven. R is cross-platform – so it doesn’t matter if you’re using Mac, Windows or Ubuntu it will work.
Eight. R is capable of reproducible research. This means bang up to date data and analysis can be shared easily because code has already been written that pulls data, analyses it and then presents it and is at hand when needed. (Meyer, 2015)
Nine. R is open source and supports extensions. If you get really good at R you can go and write your own extension to solve a specific problem.
Ten. It relates well to other languages. (Shankhdhar, 2013)
Briefly I will play devil’s advocate and starting with point ten I could say that a general use programme like Python interfaces with other languages better and is easier to learn than R. Some people say that Python is the future for big data. It is certainly popular amongst the hi-tech startups. (Asay, 2013)
However the figures tell a different story. A couple of years ago a large survey of over 700 data professionals asked the question: What programming/statistics languages have you recently been using for analytics/data mining/data science work? (Piatetsky, 2013)
61% used R
39% used Python
37% used SQL, and on average there were 2.3 languages used.
This is likely to stay constant as it is expected that R will still be used for years to come and I predict that R’s visualisation features will be improved upon, in particular.
Asay, Matt. ‘Python Displacing R As The Programming Language For Data Science’. Readwrite.com. N.p., 2015. Web. 23 Apr. 2015.
Eglen, Stephen J. ‘A Quick Guide To Teaching R Programming To Computational Biology Students’. PLoS Computational Biology 5.8 (2009): e1000482. Web. 23 Apr. 2015.
Meyer, Justin. ‘R Programming Help, How To’s, And Examples | Rprogramming.Net’. RProgramming.net. N.p., 2015. Web. 23 Apr. 2015.
Piatetsky, Gregory. ‘Top Languages For Analytics, Data Mining, Data Science’. Kdnuggets.com. N.p., 2015. Web. 23 Apr. 2015.
Revolution Analytics,. ‘What Is R?’. N.p., 2015. Web. 23 Apr. 2015.
Selig, Abe. ‘Buzzword Breakdown 2.0: 5 Baffling BI Terms Explained’. Plottingsuccess.com. N.p., 2015. Web. 23 Apr. 2015.
Shankhdhar, Gaurav. ‘Why Learn R | Reasons To Learn R Programming | Edureka’. Edureka Blog. N.p., 2015. Web. 23 Apr. 2015.
Wager, Tor D. et al. ‘A Bayesian Model Of Category-Specific Emotional Brain Responses’. PLOS Computational Biology 11.4 (2015): e1004066. Web. 23 Apr. 2015.