A picture may be worth 1000 words, however only 10 words are required to create remarkably expressive pictures or charts in R programming language. The growth and maturity of R programming language has led to its widespread adoption and several learning resources. R might just be the 18th letter of English alphabet to someone but R is the lingua franca of many data scientists and analysts in academia and organizations across various industries. Why R programming language has quickly found a following among engineers, statisticians and scientists? Why now is the right time to learn R programming?
Daryl Pregibon, a research scientist at Google said- “R is really important to the point that it’s hard to overvalue it. It allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems.”
With petabytes of data, every organization is trying to find out the best tools and techniques to gain customer knowledge. However, merely using pivot tables in Microsoft Excel to analyse huge amounts of data is illogical and thus many organizations are doing data science using R programming language to cull the voluminosity of business analytics.
For people who are not yet familiar with R language- it is a fascinating statistical programming language that has become an appealing IT skill in a programmers’ resume. According to the statistics of download site log files, R language has more than 3 million users in US alone. R language is used by statisticians or mostly by data scientists in various professions like business analytics, scientific research, business intelligence, software development and statistical reporting.
David Smith, Chief Community Officer at Revolution Analytics said - “Investing in R, whether from the point of view of an individual Data Scientist or a company as a whole is always going to pay off because R is always available. If you’ve got a Data Scientist new to an organization, you can always use R. If you’re a company and you’re putting your practice on R, R is always going to be available. And, there’s also an ecosystem of companies built up around R including Revolution Enterprise to help organizations implement R into their machine critical production processes.”
R data science language is the magic wand for 21st century data scientists to handle and analyse huge amounts of complex and unstructured data productively. The popular saying “Patience is the mother of all virtues” holds good for R language because to learn R programming is definitely a challenge. The commands that work theoretically might not work practically and being a beginner it would be difficult to know what is exactly is going wrong with the code. Thus, students or professionals who want to make a career progression in data science should learn through a comprehensive training that offers hands-on working experience on various projects.
Persistence and Patience are the key to beginning data science with R programming language, but considering the wonders R language can do, it is worth learning R language now. R language has a teaching package called Swirl that helps beginners learn commands and also provides immediate user feedback.
The longevity of R language twigs from its awesome perks – the average salary for R programmers is better than NoSQL or MapReduce programmers. According to Dice salary survey report, R programmers are among the highest paid big data professionals with an average salary of $115,531.
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Let’s return to the question at hand –why should professionals put in efforts to learn R language? Other than the popularly known fact that it’s free - we bring you the most convincing reasons to learn R programming language now-
The ability to create pleasing graphics because of its parallel processing functionality makes R a strong visualization and graphics tool. R data science language allows data scientists to create navigational graphics from the data analysis results. The graphics can be used gain meaningful insights from the large datasets or can also be exported in a presentation report. R language is at the core of Facebook’s data science team as it gives them the best overview on the kind of data they are dealing with. The data can be anything like the correlations with the number of Facebook friends a user has or the news feed numbers.
According to Solomon Messing, Data Scientist at Facebook- “Generally, we use R language to move fast when we get a new data set. With R language, we don’t need to develop custom tools or write a bunch of code. Instead, we can just go about cleaning and exploring the data."
“One of the things we like about commercial R is that it can create beautiful graphics compared to, for example, SAS, which has very ugly, horribly ugly graphics.”- said Tess Nesbitt, director of analytics at DataSong
If you are student or professional who lives in windows environment all day this reason might not appeal to you but for others who use Mac or Linux and share work with colleagues who use windows, this feature of R language comes as a boon. All thanks to the core development team of R language, it also runs on few operating systems of Mainframes.
There is a possibility that the system might run out of memory when handling huge amount of data but there are several commercial versions of R language which ease the process of handling tons of business data without any hassle. Revolution analytics provides a commercial library of analytics algorithms known as ScaleR that helps organizations to tackle petabytes of business data by scaling it to work effectively on parallel processors. The data is processed in chunks on different servers simultaneously.
DataSong, a San Francisco based company that is transforming marketing analytics technology uses commercial R packages provided by Revolution Analytics. Their aim is to create a quantitative model based on different variables for a retail customer. The commercial R packages have helped them process 30 million rows of data for 60 variables in just 10 minutes.
Learn R Programming to transform your career in Data Science!
Most of the new developments in statistics are in the form of R packages before they make way as commercial solutions. R is not just a statistical tool but a complete package of what an object oriented programming language can do. Programmers can enjoy the benefits of an interactive language whilst capitalizing on the speed of compiled code because R language supports embedding compiled code in other languages like FORTRAN or C.
The online community and distribution system of R language beats anything out there and R language also has unrivalled resources for learning. The quantity and quality of help from the vibrant online community for R programmers is relevant when they already have grasped the basics of R language and are trying to learn new statistical techniques.
The most favourite feature of R programmers is that any code that they write for an analysis task can be saved and reused at a later point of time. R programmers can also share their code with others to expand the knowledge base among R users. If the programmers notice that changes need to be made during analysis - it is rather simple as they need not reanalyse the entire data.
R language has great presence in the research community and anybody who develops a new visualization or predictive model using R language shares the code as an open source and not just publish it in the research journals so that others can share and reuse the code.
"R can do literally everything, and all new research is done in R. So especially for businesses that really want to out-compete their competitors on the basis of advanced analytics, they can get access to everything they need within R, things that might not come for five or 10 years through commercial software.”- said David Smith, Chief Community Officer at Revolution Analytics
Programmers are usually attracted to learn R programming because of its extraordinary capabilities to generate plots and charts with just few lines of code which would otherwise require several 100’s of lines of code in any other language. R language does have a steep learning curve but when programmers start learning R they really enjoy the powerful features it provides which are geared towards complex data analysis.
Do you need more convincing reasons to learn data science with R programming language? Read more: R Programming-The Golden Child of Data Science
With the growing popularity and functionality of R language, it is going stay for long as organizations like Google, Pfizer, Bank of America, Merck, Oracle widely adopt its usage for complex business analytics. A powerful community, strong partners and a promise of providing easy-to-integrate solutions, R language is capitalizing big data analytics revolution.
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