R is actually a programming environment and language made specifically for graphical applications and statistical computations. R is a very unique language and has some really interesting features which aren’t present in other languages. This makes it a quite popular choice for Data Scientists. R is used for statistical computations, data analysis and graphical representation of data. Created in the 1990s by Ross Ihaka and Robert Gentleman, R was designed as a statistical platform for data cleaning, analysis, and representation.
What Makes R A Good Choice For Data Scientists
R is the only programming language that allows statisticians to perform the most complicated and intricate analyses without getting into too much of details. With so many benefits for data scientists, R has gradually mounted heights among professionals of big data. Features of R that makes it popular:-
• Open Source Programming Language. R is free for everyone to use because it is an open source programming language. Programming codes of R can be used across all platforms like Linux, Windows, and Mac.
• Best Statistical Analysis Kit. R has all standard data analysis tools to access data in varied formats, for several data manipulation operations – merges, transformations and aggregations. It includes tools for conventional and modern statistical models including Regression, ANOVA, GLM and Decision Trees.
• Benefits of Charting. R has some great tools for data visualization to create graphs, bar charts, multi panel lattice charts, scatter plots and new custom designed graphics.
• Consistent Online Support. R language is the most sophisticated statistics software because of its quick and consistent online support. The language has a loyal user base because statisticians, scientists and engineers.
• Most Powerful Ecosystem. R has the strongest ecosystem, a package with several functionalities built in for modern statisticians. ‘dplyr’ and ‘ggplot2’ are some examples for data manipulation and plotting which relieves data scientists from graphic and charting capabilities to be included in applications.
R has extraordinary statistical capabilities. For instance, in biostatistics, only SAS can be considered as a true competitor of R. Other packages have only a subset of the statistical methods implemented in R. Commercial statistical packages, like SAS, SPSS, Statistica, RapidMiner offer a way of bidirectional communication with R, thus you can mix the software.
R programming language can do almost everything. Thus, it is used by leading corporates, social networks and data scientists find it an indispensable tool.
