Amit ShahCommonly used among data miners and statisticians, R is a programming language designed for developing data analysis and statistical software. Being an implementation of the S programming language, R programming is united with lexical scoping semantics enthused by Scheme(Benefits of R Programming). There are various programming languages to opt and build your career.
Though there are a number of significant differences, but majority of the code written for S operates unchanged. R along with its libraries executes multi-facet graphical and statistical techniques, together with time-series analysis, linear and non-linear modelling, clustering, classical statistical tests, classification, and more.
R is simply extensible throughout functions and extensions, and the R community is prominent for its dynamic hand-outs in terms of packages. It becomes simple for the user to follow the algorithmic choices as most of the R’s standard functions are coded in R itself. FORTRAN, C and C++ codes can be allied and called during the run time for computationally concentrated tasks. Also the advanced users can write down C, C++, Java, .NET or Python code to maneuver R objects unswervingly.
In support of definite functions or specific areas of study, R is extremely expendable through the use of user-submitted packages. Owing to the S heritage, R has firm object-oriented programming conveniences as compared to many statistical computing languages. R is also high on demand due to its static graphics which can generate publication-quality graphs plus various mathematical symbols. The developer can also avail additional packages for further dynamic and interactive graphics.
Counting on the strength of its own LaTeX-like documentation format, R can supply all-inclusive documentation, both on-line in different formats together in hard copy.
