According to the Spectrum Survey by IEEE, R programming language - the king of statistical computing languages for analysing and visualizing big data takes 6 th place in “The 2015 Top Ten Programming Languages”. In 2014, R programming was at the 9th position and the drastic move this year reflects the significance of R language emerging as a powerful statistical tool in data science.
A 2013 survey conducted by Rexer Analytics stated that 70% of respondents use R programming at least occasionally when compared to 47% respondents in 2012. Rexer Analytics survey indicated R programming language to be the most popular statistical analysis tool.
A Dice tech salary survey report released on January 2014 found that R programmers are among the highly paid big data professionals with an average salary of $115,531.
Organizations in every industry have started realizing the fact that - the secret to success is being able collect, store and analyse data at a faster pace than the competitors. The consequence of this big data revolution is that the hiring demand for data scientists with Hadoop, NoSQL, Python programming, R programming and other big data skills is heating up.
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With big data analytics and machine learning driving intelligence in almost every Internet connected device, software application and smartphones, R is a powerful statistical tool that data scientists use to find answers from the large treasure troves of data.R programming helps data scientists with statistical analysis of data more quickly and powerfully when compared to any other statistical computing tools.
R language is used by more than 2 million statisticians and data scientists across the world, and with the wider adoption of R language for business applications, the usage of this statistical software is increasing exponentially. R programming language was developed for statistical analysis at a small-scale in academic settings. R language is a powerful statistical computing tool for visualizing data, exploring large data sets and creating novel statistical models.
"R is more about sketching, and not building. You won’t find R at the core of Google’s page rank or Facebook’s friend suggestion algorithms. Engineers will prototype in R, then hand off the model to be written in Java or Python."- says Michael Driscoll, CEO of Metamarkets.
R is on the rise as a powerful business analytics tool with contributions from popular statisticians to the open source community over 20 years. R language is among the most powerful and popular data science tools because it presents different faces to different users. R programming language has been kicking around since 1997 as an alternative to expensive statistical programming tools like SAS or Matlab.
R is the first programing language that takes input through a command line which might seem unfriendly to non-coders in the beginning, however beginners can directly make calls to pre-defined software packages that have ready-made commands for data visualization and statistical analysis. Pre-set R packages can be adapted by beginners to learn R programming in a fun and interactive way. R software packages act as a middle ground between the world of coding experts and the ease of commercial black-box solutions.
R language has a huge library of several novel scientific algorithms that make it easier for big data professionals to build intelligent analytic big data applications rapidly.
“R is really important to the point that it’s hard to overvalue it.R language allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems.”-said Daryl Pregibon,a research scientist at Google.
With more than 9000 packages, close to 206 R Meetup groups and 54K+ members on LinkedIn’s R group, the interest to learn R for statistical data analysis is mounting among big data professionals.
According to a survey conducted by O’Reilly Media in 2014, R is the most popular and powerful programming language that data scientists are currently using.
R is an open source programming language-free for anyone to use. R language code can be executed on all platforms Windows, Mac, or Linux. Data geeks can inspect R language code and play with it as much as they want without having to bother about user limits, subscription cost and license management. The programming libraries are free to access; however there are certain commercial libraries that are meant for organizations that often deal with data in the terabyte range. Hadoop’s project libraries are popular free libraries that help users manage their data in hadoop data computing environment.
R language has all standard data analysis tools for accessing data in different formats, for various data manipulation operations like aggregations, transformations and merges. There are also tools for traditional and modern statistical models like ANOVA, Regression, Tree models, GLM included in an object oriented framework making it easy to extract and merge the required information from the results instead of copying and pasting it from a static report.
R language has excellent tools for data visualization for creating graphs, multi panel lattice charts, bar charts, scatterplots and also new custom designed graphics. The graphics and charting capabilities of R language are unmatched. The graphical system of R language is influenced by popular data visualization thought leaders like Edward Tufte and Bill Cleveland.R programming based graphics appear in blogs like the FlowingData, The New York Times and The Economist.
R language stands out as a statistics software due to its consistent and quick online support.R language has quickly found great following because engineers, scientists and statisticians even without much knowledge of computer programming find it easy to use. The discussion forums related to R programming topics surpass the discussion about any other commercial statistical analysis software. If somebody posts a question, they can be assured that the person who developed the package will respond promptly and rapid response is the key to a data scientists in basic research.
“The great beauty of R language is that you can modify it to do all sorts of things. And you have a lot of pre-packaged stuff that’s already available, so you’re standing on the shoulders of giants.”- said Hal Varia,chief economist at Google
R has a strong package ecosystem with so many functionalities that are built in for statisticians. Packages like “ggplot2” and “dplyr” for plotting and data manipulation relieve data scientists from all charting and graphic capabilities they need to include in their applications. Caret package in R language helps data scientists apply machine learning through a unified application programming interface. Using this package, lots of machine learning algorithms have already been implemented.
“The vastness of package ecosystem is definitely one of R's strongest qualities -- if a statistical technique exists, odds are there's already an R package out there for it.”- said Matt Adams, data scientist at Code School.
"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.
Whether you want to create a compelling chart or want to grab the sexiest job of 21 st century-‘Data Scientist’, want to run your analysis or want to pick a skill that gives you an added advantage - your next move must be to master “Data Science with R Language”. Similar to other big data skills, mastering data science with R cannot be achieved instantaneously. Megan Jennings, an ecologist at San Diego State University in California rightly said that - “Make that time. Set it aside as an investment: for saving time later, and for building skills that can be used across multiple problems we face as scientists.”
We hope that it was a helpful overview on what makes R language the powerful statistical analysis tool. If you have some other benefits of using R for data science, please add them in the comments below!