A good data scientist is, after all, a passionate coder-slash-statistician, and there is no better coding language for a statistician to learn than R. R is a fascinating programming language, often referred to as the ‘golden child’ of data science. It has recently become a hot skill to add to your resume.
Furthermore, as the businesses increasingly shift towards data-intensive work, the demand for tools like R for data mining, processing, and visualization is also increasing.
Why You Should Learn R
With the rising popularity of data sciences, it is more likely than ever that you will confront R in your organization.
Here are a few reasons emphasizing why you should learn R:
1. R is Free and Open Source
R is an open source language and it is freely available for download from the internet. Licensed under the GNU (General Public License), it has no license restrictions and allows you to modify the code and add your own innovations to it. R is capable of running anywhere and anytime and can also be sold adhering to the conditions of the License.
2. R is Compatible Across Platforms
R can be run on several operating systems and varied software or hardware. It is compatible to be used on Microsoft Windows (32-bit as well as 64-bit), GNU/Linux, Macintosh, UNIX and its derivatives like Mac OS X, FreeBSD, Darwin, Solaris, etc. R also supports some of the Mainframe operating systems.
3. R is Data Friendly
Several commercial versions of R language has the capacity to ease the process of handling petabytes of business data without any hassle. Revolution analytics offers a commercial library of analytics algorithms known as Scale-R to help organizations tackle tons of business data by scaling it to work effectively on parallel processors. The data is processed simultaneously on different servers.
4. R is Remunerative
R is not only fun to use, but it is a language in-demand, often equating to higher salaries for professional practising it. R has been ranked as a highest-paying skill by the Dice Technology Salary Survey. Another recent salary survey has also included R among the skills used by the highest paid data scientists.
5. R is the Most Sophisticated Statistical Programming Language
Since R is developed by top computational statisticians and computer language designers, it is undoubtedly one of the most advanced statistical programming language used worldwide by several statisticians. R allows programmers to work on large complex objects, exchange data in MS-Excel, keep a track of every computational step, ensure an accuracy of the code, preserve the history for later reference, work on advanced statistical analysis and so on.
6. R Has a Diverse Community
The R community is vast and diverse. The community comprises individuals coming from unique professional backgrounds, including academicians, business analysts, scientists, statisticians, and professional programmers. The comprehensive R Archive Network, known as CRAN, maintains packages created by the R community members that perform stock market analysis, create maps, engage in high-throughput genomic analysis and do natural language processing. There are over 7000 packages available on CRAN.
7. R is Extensible in Its Structure
R supports extensions in its structure. R consists of data structures including scalars, vectors, data frames, time series, lists, matrices, etc. R supports matrix arithmetic as well as extensible object-system such as geospatial coordinates and regression models. R also allows procedural programming and Object-Oriented Programming for certain functions. It provides the facility to extract data from Google using RCurl package.
8. R Gives Outstanding Graphical Outputs
R programming language has fantastic graphical capabilities that are superior to any other statistical language. The language’s ability to create appealing graphics, bestowed by its parallel processing functionality, makes R a strong visualization and graphics tool. R allows data scientists to create navigational graphics utilizing the data analysis results. R forms the core of Facebook’s data science team as it offers them the best overview on the kind of data they are dealing with.
9. R is Relatable to Other Programming Languages
R can relate to other programming languages easily. A new trend has begun recently to integrate the existing software as well as emerging software with various R packages to make them more productive. Resultantly, R is getting attached to several different file systems, applications, and databases. This makes R quite user-friendly while importing data from Microsoft Excel, Oracle, Microsoft Access, MySQL, SQLite, and so on. R is also easily connectible to various databases with the help of Open Database Connectivity Protocol (ODBC) and the ROracle package.
10. R is Fun to Learn
Of course, R is fun to learn. Being fully programmable, the language lets you play with it the way you want. It allows for code enhancements enabling you to develop dynamic packages, develop your own apps and automate repetitive procedures. Coding with R is fun, especially because it enables you to develop your own InfoMaps, create tables in LaTeX or LyX and generate charts and plots using just a few lines of code. R language also allows you to write your own functions and distribute it as an add-on R software package.
R is worth a try for all the above reasons and more. The growth and maturity of this language have led to its widespread adoption. Now, with Microsoft coming ahead and including R in more of its offerings, R is definitely going to be in talks in the months and years to come.