R : The language of Statistical Computing
R is an open source programming language and software environment for statistical computing and graphics that is maintained by the R core-development team — team of volunteer developers from across the globe.
R is the way to go
Ross Ihaka and Robert Gentleman designed the R language , with a goal of making a more user-friendly environment for data analysis, statistics and visualization computation. Recently, R has been gaining huge popularity in commercial data analysis, and therefore has become an obvious and frequent choice for data scientists.
“It’s free, open source, powerful and highly extensible. You have a lot of prepackaged stuff that’s already available, so you’re standing on the shoulders of giants,”
- Google’s chief economist told The New York Times back in 2009.
More Reasons To Learn R
1. R is a Free, Open Source Language :
R is freely available, this means, anyone can download R from the internet and start working on it. R language has no licence restrictions as it is issued under the GNU (General Public License) and is an open source programming language.
2. R is Compatible Across Platforms :
One of the greatest advantages of R is that an R script can run on several operating systems and varied Software/Hardware. It is quite often used on Microsoft Windows, Macintosh, GNU/Linux, UNIX, and the scripts work seamlessly across any operating system.
3. The Best Statistical Programming Language :
R is developed by top computational statisticians and computer language designers, it has powerful numerical analysis tools for linear algebra, differential equations, and stochastic analysis. The overall structure and syntax of R is exclusively developed for statistical computation.
4. Outstanding Graphical Outputs :
The graphs generated with R are high in quality and comparable to publication graphs, it includes static and dynamic graphs depending upon the packages chosen. The graphical capabilities of R are fully programmable, and the quality surpasses most other statistical and graphical packages
5. R has a Vast Community :
R has a large, active, and growing community of users with 95,000+ members of LinkedIn’s R group and more than 400 R Meetup groups currently in existence. The large community ensures that for each problem you encounter there exists another user who encountered the same problem and is most likely to be able to help you.
Recommended Resources For Learning R
The internet has a plethora of resources for learning R, below is a curated list of the best resources for the various different types of learners.
Online Courses:
- Coursera - R Programming by Johns Hopkins University
- DataCamp Introduction to R
- Udemy R Programming A-Z™: R For Data Science With Real Exercises!
Books :
1. Hands-on Programming with R
2. R for Everyone: Advanced Analytics and Graphics
3. The Art of R programming by Norman Matloff
4. An Introduction to Statistical Learning: with Applications in R
If you are already proficient in another programming language, then you should try R language for programmers.
Therefore , it can be seen that R is worth its popularity and it is going to scale further. R allows practicing a wide variety of statistical and graphical techniques and offers more than 10,000 libraries to choose from. All of this makes R an ideal choice for data science, big data analysis, and machine learning so starting learning R today.
