Statistical Computing Technology is of prime significance in this environment so hugely driven by everything financial. Every business, small or large has to definitely deal with financial data and it’s never an easy task. Manually carrying out the processes makes the output more unreliable and prone to flaws. To undertake this type of computing, the R programming language was strategically devised out. This system of graphics and statistical computing provides debugging facilities and high level graphics. The field of work which makes extensive use of this language are the arenas of data analysis and development of Statistical Software programs.
The R programming script has seen a recent rise in popularity owing to the fact that the services of data miners and statisticians has seen a sudden influx in the recent years. Its most unique feature is that this language can take inputs in form of expressions and this goes a long way in accurate statistical modelling and graphic designing. The availability of a number of shortcuts helps the user to perform the most complicated tasks in a simple manner while using the R language for statistical interpretations.
This language is freely available and FORTRAN, C etc. have been used to write its source codes. S Programming language is the core and forms the basic framework for R, though R language maintains its basic distinctions. A number of graphic techniques, classifications, statistical tests and linear- nonlinear modelling can be implemented under R and its libraries. The scope of inventing new functionality and working beyond the conventional levels has been prolifically provided by this dynamic language. As compared to other programming languages, R exhibits a greater technological advancement, providing for an object-oriented programming facility. The possibility to create dynamic and production quality graphs using this language adds a lot to its usability and popularity.