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So, you are a statistician or one in the making! I am sure that you either use R already or at least know about it.
‘R’ needs no introduction for professionals who deal with ‘DATA’. A well know language among data scientists and statisticians (and other folks who try to make sense of the ‘DATA’), R is being dubbed the go-to statistical software of 2014 and beyond. Today we will discuss as to why as a statistician you must be proficient in R.
R is similar to other programming languages like Java and C, but some of its features specifically appeal to statisticians. It contains a number of built-in mechanisms to organize the data, run calculations and to create graphical representations of such data sets.
Why should Statistical Professional know R?
R has a wide assortment of statistical techniques like linear and non-linear modeling, classical statistical tests, time-series analysis, classification, etc. and the graphical techniques are highly extensible through functions and extensions. Being open source, R-community is noted for its active package contributors. Statisticians find it easy to follow the algorithmic choices, as many R’s standard functions are written in R itself. R has stronger object oriented programming facilities than any other statistical computing languages. The permissive lexical scoping rule simplifies the extension of R.
Looking at the features and its usage, we know that R is a powerful statistical computing language. It falls under the category of advanced analytic techniques that are used in today’s organizations dealing with Big Data. R has been able to attract around 2 million users with its open source framework. Therefore, R seems to be the future for all statisticians.
When talking about statistics, nothing beats a good figure (both number and graphics). R has an outstanding graphical output. If you take a look, the graphs created by R are unbelievably clear, of high quality and quite impressive. Static graph is an absolute strength of R and produces publication-quality graphs along with dynamic and interactive graphics with additional packages.
What makes R better?
Several job tracking sites show that the demand for ‘R’ is at its all-time high and rapidly increasing. So, as a Statistics professional and choose to ignore R language, you are bound to be on the losing side.