A Revolutionary In-Memory Big Data Predictive Analytics Language: R

Introduction

R language is a freeware open to the public for tenacious statistical computing and visualization of sophisticated calculations in a graphical interface for big data and data mining. R is a programming language as well and can integrate with Apache Hadoop and MapReduce. Recently, Microsoft acquired Revolution Analytics that manufacturers R language to integrate it with SQL Server for corporations that run Microsoft products, it would be seamless to plug and play R for large-scale projects (Yegulalp, 2015). R language has been making strides in ever-expanding scientific fields including high-performance computing for biology and genomics with mathematical and statistical layers (Schmidt, 2014). Recently R announced Enterprise Editions RevoR and ScaleR commercial distributions. These packages can throttle the bottlenecks by identifying the operating capacity of the processor cores. RevoR mechanizes the throughput of the cores by self-regulating multitasking by executing a large number of threads simultaneously. ScaleR is another big data strategy that can perform truckloads of statistical predictive analytics in several industries (Analytics, 2015). Media and publishing companies, Internet providers, search giants, social media, and banking industries fully tap into the potential of language R for statistical computing. By mid-2014, the number of installations jumped to two million. In a recent survey, R ranked as the top statistical computing language for big data to crunch millions of records (Nicolaou, 2014).

Handling big data requires features that are not overly complex and after gleaning the big data result sets with MapReduce from Hadoop platform, it requires fine-tuning and performing calculations to provide the value for the big data after running a bevy of algorithms on clusters of network rack-mounted servers. In Chapter 6, (Minelli, Chambers, & Dhiraj, 2013) paraphrase this as the last mile of data analytics or applying data mining techniques and deliver the analysis to the corporations. The authors also refer to the difference between creation and consumption of the data. Most of the high-end infrastructure creates the data result sets and the tools like R language excel in consuming the data and discover the underlying trends and patterns by turning it into valuable insights with statistical computing. In previous years, SAS and SPSS were the most popular statistical computing tools to build the last mile analysis. However, none of these tools created the big data revolution as much R has generated in the past decade.

IBM SPSS, Revolution Analytics, and SAS

IBM acquired SPSS, and Microsoft bought Revolution Analytics, and North Carolina State University founded SAS. The premises for creating these groundbreaking statistical programming languages differ with the rise of the big data analytics. The genesis of SAS was to collect, process, analyze, and synthesize the big data generated from agronomics, crops, and plants. R language is the next-generation innovation of S language. Bell Labs founded S language. C, Fortran, and R fuel the source code of R language. The roots of R show the utilization in the world of academia for research and development. In the recent times launch of RevoR and ScaleR from R has opened the gates for enterprises looking to wrangle their data. However, R has the freeware version for further development. Corporations like Google, Facebook, The New York Times, and FDA profoundly perform statistical computing with graphics on big data by R. However, R is not just a statistical computing big data tool, it is a big data programming language with advanced features such as neural networks. R rapidly embraced a larger community of big data development community in a short time blowing SAS and SPSS out of water (Datacamp, 2014).

R comes with several features for handling big data for various use cases:

Data sampling and analysis from large data sets

Corporations perform statistical analysis on the total result datasets returned from Hadoop platform for discovering the insights of the big data. In certain scenarios, enterprises may have to carry out prototyping solutions to analyze the feasibility of a solution for financials, banking, media, publishing, bioinformatics, and several other industries. These industries can engage in performing the big data analysis with R without having to run the statistical analysis against the entire data set. Thus, corporations can make a choice for the usage of sample datasets for rapid prototyping solutions, as long as the magnitude of the data is in the realm of a billion data sets. However, the data procured for sampling should be unbiased that encompasses all the demographics, regions, segmentations, and channels from where the data generates. This provides the most real dimension of the data to keep the prototyping close to the requirements (Bracht, 2013).

In-Memory Analytics

R requires all the objects to run on DRAM (Dynamic random access memory). A new league of high-performance computing meets R due to the need for the speed (Rosario, 2010). However, on regular home computing machines that differ at the core level of operating system with 32-bit and 64-bit CPUs, the memory limitation is two gigabytes and eight terabytes of RAM. The memory constraints arise from the design of the operating system to execute arithmetic operations per cycle. Once the levee breaks, R will start dispatching premonitory messages (Bracht, 2013). R language provisions various functions to determine the load size of each data set object, this way; the machine can load up the data sets that comply the threshold limits (Wickham, 2015). Because the data resides on main-memory, R and SAP HANA complement each other to be able to run the large sets of data on a potential 100 TB main memory of SAP HANA (Ohri, 2014).

Parallel Computing

To run R language on a home computing machine, breaking the data into several data nuggets can optimize the devices with moderate main memory. This method works as a parallel computing procedure by parallel processing the data chunks in small groups and then combine the result sets with specific functions. However, there are specific functions that can perform such specific tasks in language R. This may not apply to large data sets that require staying in the main memory of the machine (Bracht, 2013).

Synergy with a bevy of programming languages

As of today, R language has 6700+ built-in packages covering a substantial number of operating systems and programming languages. R, ahead of its time harmonizes object-oriented programming languages such as Java and C++ as an integral part of R. There are packages within R that can enable such integration to multiple programming languages. These packages capacitate other programming languages. The source code can be written seamlessly in integrated programming languages to allow the native integration and advanced concepts with Java and C++ to write in R. However, there could be some limitations on the usage of some of features from R when written in other languages (Vries, 2015).

A list of top 10 books to learn R Programming language

An introduction to learn An Introduction To Statistical Learning – With Applications in R
The Elements Of Data Analytical Thinking
R Programming For Data Science
Exploratory Data Analysis With R
Learning RStudio For R Statistical Computing
R In Action
R Graphics Cook-Book
R Cook-Book
R Packages
Advanced R

References

Analytics, R. (2015). Optimizing Open Source R for Multi-Threaded Performance. Retrieved October 31, 2015, from http://www.revolutionanalytics.com/high-performance-r
Bracht, O. (2013). Five ways to handle Big Data in R. Retrieved October 31, 2015, from http://www.r-bloggers.com/five-ways-to-handle-big-data-in-r/
Datacamp (2014). What is the best statistical programming language? Infograph. Retrieved October 31, 2015, from http://blog.datacamp.com/statistical-language-wars-the-infograph/
Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses (1 ed.). Hoboken, NJ: Wiley.
Nicolaou, A. (2014). The 9 Best languages for crunching data. Retrieved October 31, 2015, from http://www.fastcompany.com/3030716/the-9-best-languages-for-crunching-data
Ohri, A. (2014). R for Cloud Computing: An Approach for Data Scientists. New York City, NY: Springer .
Rosario, R. R. (2010). Taking R to the Limit (High Performance Computing in R), Part 2 — Large Datasets, LA R Users’ Group 8/17/10. Retrieved October 31, 2015, from http://www.slideshare.net/bytemining/r-hpc
Schmidt, D. (2014). High Performance Computing with R. Retrieved October 31, 2015, from http://rbigdata.github.io/NIMBioS/presentations/hpcR.pdf
Shah, A. (2015). Top 10 R Programming Books To Learn From. Retrieved December 26, 2015, from http://www.edvancer.in/top-10-r-programming-books/
Vries, A. D. (2015). How many packages are there really on CRAN? Retrieved October 31, 2015, from http://blog.revolutionanalytics.com/2015/06/how-many-packages-are-there-really-on-cran.html
Wickham, H. (2015). Memory. Retrieved October 31, 2015, from http://adv-r.had.co.nz/memory.html
Yegulalp, S. (2015). SQL Server 2016 gets an R (language) rating. Retrieved October 31, 2015, from http://www.infoworld.com/article/2998648/sql/sql-server-2016-gets-an-r-language-rating.html

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