R: Introduction to R

R is one of the most popular languages used for Statistical Computing today. It is widely used for Data Analysis and Visualization by statisticians and data miners. Its application includes a range of purposes from data preprocessing, cleaning, web scraping, and visualization to a wide range of analytical tasks such as computational statistics, econometrics, optimization, and natural language processing.

R is in itself a command-line-based scripting language and is a dynamic language (compiled at runtime). Though you can use any text editor to write R scripts, it is way more convenient and useful to use R Studio. In fact, you will almost always use R with R Studio.

R is an open-source. popular and user-friendly (even if you have never done any computer programming) language is used by organizations like Google, Facebook, US DoD, Twitter, TechCrunch, Microsoft, etc.

But, why use R?

Programming and Statistical Features

R is an open-source software created over 20 years ago by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. However, its history is even longer as has derived its features from the S programming language created by John Chambers out of Bell Labs back in the 1970s. S was widely accepted in the world of statisticians. R is actually a combination of S with lexical scoping semantics inspired by Scheme.

R and its libraries implement a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others.

Interfaces

R Studio is the most used interface for R scripting which gives you great flexibility and visual representation of the data you are working with. There are other interfaces available too, including R Tools for Visual Studio by Microsoft and also Rattle GUI, and R Commander. You can even access R functionality from within other programming languages like Python, Ruby, Java, etc.

Flexibility and Community

The functionalities and features of R can be extended with the usage of packages that are very easily available online, developed, and maintained by the community. These packages allow specialized statistical techniques, data handling, data cleaning, graphical devices, import/export capabilities, reporting tools, etc.

These packages are developed by people from different walks of life and knowledge, which include researchers, analysts, scientists, statisticians, and more. The R community is fantastically diverse and engaged. On a daily basis, the R community generates opportunities and resources for learning about R. These cover the full spectrum of training — books, online courses, R user groups, workshops, conferences, etc. And with over 2 million users and developers, finding help and technical expertise is only a simple click away. Support is available through R mailing lists, Q&A websites, social media networks, and numerous blogs.

R: Introduction to R

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Programming and Statistical Features

Interfaces

Flexibility and Community

Command Palette

Comments

More from this blog

Programming and Statistical Features

Interfaces

Flexibility and Community