Data Analytics using R-Programming
R Analytics (or R programming language) is a free, open-source software used for heavy statistical computing. The language is built specifically for, and used widely by, statistical analysis and data mining.
More specifically, it’s used to not just analyze data, but create software and applications that can reliably perform statistical analysis.
In addition to the standard statistical tools, R includes a graphical interface. As such, it can be used in a wide range of analytical modeling including classical statistical tests, lineal/non-lineal modelling, data clustering, time-series analysis and more.
Many statisticians use R because it produces plots and graphics that are ready for publication, down to the correct mathematical notation and formulae. Another reason for its popularity is that its command-line scripting allows users to store complex analytical methods in steps to be reused later with new data.
Instead of having to reconfigure a test, users can simply recall it. This also makes it useful for validation and confirmation purposes. Researchers can explore statistical models to validate them or check their existing work for possible errors. Even though it’s known as a more complex language, it remains one of the most popular for data analytics.
There are multiple ways for R to be deployed today across a variety of industries and fields. One common use of R for business analytics is building custom data collection, clustering, and analytical models.
Instead of opting for a pre-made approach, R allows companies to create analytics and statistics engines that can provide better, more relevant insights due to more precise data collection and storage.More importantly, using R as opposed to boxed software means that companies can build in ways to check for errors in analytical models while easily reusing existing queries and ad-hoc analyses.