Ward and Barker (2013) traced back definition of Volume, Velocity, and Variety from Gartner. Now, a predominately widely accepted definition for big data is any set of data that has high velocity, volume, and variety (Davenport & Dyche, 2013; Fox & Do 2013, Kaur & Rani, 2015. Mao, Xu, Wu, Li, Li, & Lu, 2015; Podesta, Pritzker, Moniz, Holdren, & Zients, 2014; Richards & King, 2014; Sagiroglu & Sinanc, 2013; Zikopoulous and Eaton, 2012). Davenport et al. (2012), stated that IT companies define big data as “more insightful data analysis”, but if used properly companies can gain a competitive edge. Data scientists from companies like Google, Facebook, and LinkedIn, use R for their finance and data analytics (Revolution Analytics, n.d.). According to Minelli, Chambers and Dhiraj (2013) R has 2 million endusers and is used in industries like health, finance, etc.
Why is R so popular and have that many users? It could be that R is a free opensource software that works on multiple platforms (Unix, Windows, Mac), and has an extensive statistical library to help conduct basic statistical data analysis, to multivariate analysis, scaling up to big data analytics (Hothorn, 2016; Leisch & Gruen, 2016; Schumacker, 2014 & 2016; Templ, 2016; Theussl & Borchers, 2016; Wild, 2015). Given the opensourced nature of the R software, many libraries are being built and shared with the greater community, and the Comprehensive R Archive Network (CRAN), has a ton of these programs as part of R Packages (Schumacker, 2014). Other advantages of R, is the customizable statistical analysis, control over the analytical processes, extensive documentation, and references (Schumacker, 2016). R Packages allow for everyday data analytics, visually aesthetic data visualizations, faster results than legacy statistical software that the enduser can control, drawing upon the talents of leading data scientists (Revolution Analytics, n.d.). R programming features include dealing with a whole suite of data types, (scalars, vectors, matrices, arrays, and data frames), as well as impetrating and exporting data into multiple other commercially available statistical/data software (SPSS, SAS, Excel, etc.) (Schumacker, 2014 & 2016). All the features of R related to big data analytics, statistical, and programming features are listed in Table 1 (below). Given all the R Packages listed below and the importing and exporting features to other big data statistical software illustrates how useful R is for analyzing big datasets of various types (Schumacker, 2014, 2016).
Finally, R is the most dominant analytics tool for Big Data Analytics (Minelli et al., 2013). Big data analytics is at the border of computing science, data mining, and statistics, it is natural to see multiple R Packages and libraries listed within CRAN that are freely available to use. Within the field of big data analytics, some (but not all) of common sets of techniques that have R Packages are machine learning, cluster analysis, finite mixture models, and natural language processing. Given the extensive libraries through R Packages and extensive documentation, R is well suited for Big Data.
Table 1: Big Data Analytics, Statistical, and Programmable features of R
R Programming Features (Schumacker, 2014)  Input, Process, Output, R Packages 
Variables in R (Schumacker, 2014)  number, character, logical 
Data Types in R (Schumacker, 2014)  scalars, arrays, vectors, matrices, list, data frames 
Flow control: Loops (Schumacker, 2014)  Loops (for, if, while, else, …)
Boolean Operators (and, not, or) 
Visualizations (Schumacker, 2014)  pie charts, bar charts, histogram, stemandleaf plots, scatter plots, boxwhiskers plot, surface plots, contour plots, geographic maps, colors, plus others from the many R Packages 
Statistical Analysis (Schumacker, 2014)  Central tendency, dispersion, correlation test, linear Regression, multiple regression, logistic regression, loglinear regression, analysis of variance, probability, confidence intervals, plus others from the many R Packages 
Distributions: population, sampling, and statistical (Schumacker, 2014)  Binomial, Uniform, Exponential, Normal, Hypothesis testing, chisquare, ztest, ttest, ftest, plus others from the many R Packages 
Multivariate Statistical Analysis (Schumacker, 2016)  MANOVA, MANCOVA, factor analysis, principle components analysis, structural equation modeling, multidimensional scaling, discriminant analysis, canonical correlation, multiple group multivariate statistical analysis, plus others from the many R Packages 
Big Data Analytics: Cluster Analysis (Leisch & Gruen, 2016)

hierarchical clustering, partitioning clustering, modelbased clustering, Kmeans clustering, fuzzy clustering, clusterwise regression, principal component analysis, selforganizing maps, density based clustering 
Big Data Analytics: Machine Learning
(Hothorn, 2016; Templ, 2016) 
neural networks, recursive partitioning, random forests, regularized and shrinkage methods, boosting, support vector machines, association rules, fuzzy rules based systems, model selection and validation, tree methods, expectationmaximization, nearest neighbor 
Big Data Analytics: Natural Language Processing (Wild, 2015)

Frameworks, lexical databases, keyword extraction, string manipulation, stemming, semantic, pragmatics 
Big Data Analytics: Optimization and Mathematical Programing (Theussl & Borchers, 2016)

optimization infrastructure packages, general purpose continuous solvers, leastsquares problems, semidefinite and convex solvers, global and stochastic optimization, mathematical programming solvers

References
 Davenport, T. H., Barth, P., & Bean, R. (2012). How big data is different. MIT Sloan Management Review, 54(1), 43.
 Fox, S., & Do, T. (2013). Getting real about Big Data: applying critical realism to analyse Big Data hype. International Journal of Managing Projects in Business, 6(4), 739–760. http://doi.org/10.1108/IJMPB0820120049
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