Introducing R Programming into Business Analytics
Created by Ross Ihaka and Robert Gentleman, the R language aptly garners its name based on the initials of its founders. Again, being an implementation of the S programming language, it is often regarded as a tribute to S. R is an open source scripting language widely used by statisticians and data miners for statistical computing, predictive analytics, and data visualization. Earlier confined to academic statistics labs, this programming language is now expanding into business analytics. The R language typically inclines to the cutting edge of data science, providing businesses with the latest data analysis tools for application development, rapid prototyping, research, training, and education. However, like all things else, R language too comes with its own share of limitations. With uncertain standards and a whole lot of diverse suppliers, it might turn out to be vulnerable for business.
Nevertheless, the R language is the most popular statistical software in operation today boasting of over 2 million users worldwide, and its usage continues to be on the rise. Social media companies were among the first to leverage R so as to mine their rich user behavior databases. By doing so, they could figure out what their customers needed and accordingly revamped their platforms with innovative data-driven features. Citing an instance, Facebook uses R language to understand the way its users interact with the service. With the aid of exploratory data analysis, Facebook identifies how viral data circulate through the social network while evaluating the actions of its users throughout the day.
Data analysis is also perceived to have made its way into media where the accessibility to public data sources has led to the practice of data journalism. In this case, The New York Times has been a forerunner harnessing R as a base for interactive data analysis features. Besides, the newspaper also uses R to upgrade its traditional reporting leveraging R’s rapid prototyping capabilities. This, in turn, enables data journalists to move from a concept to a graphic to a complete illustration in a matter of hours.
Another industry that has great prospects for R language is marketing. Retailers these days collect inclusive data about customers—purchasing habits, likings, as well as backgrounds. To help enterprises make sense of these rich sources of information, marketing analytics companies are emerging at a fast pace. Finance and insurance companies—being primary users of advanced statistical analysis—too extensively draw on R language to formulate advanced trading, pricing, and optimization strategies for boosting ROI and curtailing risks.
It is important to note here that R Programming is not restricted to large-scale businesses alone; it can be practiced by anyone in general as it is platform-independent, hence applicable to any operating system. R Programming is a free service that can be implemented in any organization barring the need of a license. What’s more, it is open-source enabling users to verify the source code, fix bugs, and add features, all by themselves avoiding vendor lock-in.
R Programming easily integrates with most other languages including C, C++, Python, and Java while also communicating with many data sources like OBDC-compliant databases (Excel, Access) and statistical packages (Minitab, SAS, SPSS, Stata). The best part is that R is becoming expeditious with time and serves as a connecting language that links different data sets, tools, or software packages. All in all, R Programming is a favorable approach to carry out reproducible, excessive-quality analysis. It offers just the right level of flexibility and power when it comes to handling data.
It must be kept in mind, though, that commercial applications built on R Programming are still in a nascent stage. In spite of the fact that several vendors provision workarounds to dole out jobs across multiple serves, using R language is yet code-intensive. Thus, it is advisable for businesses to employ programmers who have an in-depth knowledge of R and deliver tools that can control their data sets before implementing anything else.