Anyone who is planning to learn a programming language must have heard (more times than one can count) that R and Python are two of the top 6 programming languages to learn for beginners. While both the programming languages are extremely beginner-friendly, today our focus will be on R.
Over the years, R has gained a massive fan following not just in the Data Science and IT community, but also in the business domain. This is primarily because every industry now relies on data, and R offers a dual advantage – it is both a programming language and environment for statistical computing and graphics.
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R comes with a vast assortment of statistical and graphical methods including linear regression, time series, statistical inference linear and non-linear modeling, classification, clustering, ML algorithms, and much more. Also, R has top-notch tools for visualization, reporting, and interactivity, which are crucial both for Data Science and business domains. The cherry on top – R includes packages covering a wide range of topics on the business front like finance and econometrics. These are the reasons why R is a wonderful option for Data Scientists, Developers, and Entrepreneurs alike.
Today, R is the trusted programming language of some of the biggest names in the industry including Google, Facebook, HP, Airbnb, HSBC, Bank of America, The New York Times, to name a few.
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Still not convinced?
Here are eight reasons why you should learn R!
- It is an open-source tool.
R is an open-source programming language, meaning it is entirely free. Not only can you freely install it on your machine, but you can also update, modify, and clone it. What’s more, is that you can also redistribute and resell R since it has no license restrictions – it is issued under the GNU (General Public License).
- It is cross-platform compatible.
Yes, R is capable of running on multiple operating systems with different software/hardware specifications. So, whether you are using Windows or Mac, or Linux, R can run smoothly on all operating systems. Also, it can seamlessly import data from Microsoft Excel, Oracle, MySQL, and SQLite.
- It has an extensive library.
As we mentioned earlier, R comes with an extensive library of inbuilt packages and functions designed to cater to different needs. It has special packages for Machine Learning, Statistical Modeling, Data Manipulation, Data Visualization, Imputation, among other things. And as R is open-source, you can also build your own package and enrich the R community.
- It has a massive community.
R is backed by a vast community of active Developers, Coders, and Data Scientists. So, rest assured, you can always seek help from the R community if you are ever stuck in a rut or cannot find a fix to an issue. Not just that, you can also engage in exchange ideas with other professionals and collaborate on projects.
- It is excellent for visualization.
R has some best-in-class packages for creating detailed visualizations like ggplot2, lattice, leaflet, plotly, and RGL, to name a few. With these packages, you can design high-quality graphs.
- It can help create interactive web apps.
R allows you to develop interactive web applications, that too, directly from your data analysis software. Its package, Shiny, is exclusively designed for this purpose. It helps create web pages, dashboard designs, and a lot more from your R Console itself.
- It is the favorite of Statisticians and Data Scientists.
R is inherently a statistical language. Hence, it is replete with all-things-statistics (like the tools and techniques we mentioned in the introduction). It has all the necessary statistical features ranging from basic statistics (mean, variance, median) to static graphs (graphic maps, basic plots, etc.) and probability distribution.
- It finds applications in many industries.
R has gained increasing traction in the industry, thanks to its versatility and assortment of useful packages and functions. For instance, it is used in Computational Biology to conduct genomic analysis. It is used by finance companies to analyze fraudulent transactions and build econometric models.
The scope of R programming is growing across the various parallels of the industry. After all, it is one of the most versatile and efficient statistical tools that can accommodate any and every need of the user. The rising popularity of R and its adoption by reputed companies is driving the job opportunities in R.
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What is the importance of R in Data Science?
Using R in Data Science will benefit you in multiple ways. First of all, you will not need any compiler to execute your code since R is an interpreted language. It quickly interprets the code, thus, making code development easy. Secondly, R is a vector language and many calculations are done using vectors. Therefore, instead of using a loop, you can add multiple functions with a single vector. The last importance of R in data science is its use in genetics, statistics, and biology. R aims to become a wholesome language so that performing any task smoothens.
What are the advantages of using R programming?
With the plenty of advantages and applications that R has, here are a few of them. Every new technology shows up in R at first since it is a statistical package. It being open-source is the next advantage as you can use R whenever you want and wherever you want. Moreover, if you desire, you can also sell it under license conditions. The next reason why R is going to have a significant future is how welcoming it is, as you can induce bug fixes, code enhancements, and new packages. Finally, R is cross-platform compatible, so it can run on any operating system. However, it is widely used with Linux, MS Windows, and GNU.
Is it hard to learn R?
To work around the basics of R might take a month or two. However, if you plan to use your learning of R in professional practice, devoting a minimum of six months is mandatory to upskill yourself on the knowledge you would need. R is considered a very different programming language than others. Plus, there are tons of packages in R which are specifically written to use R in various concepts. Sometimes you might have to spend months to understand bigger packages better.