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.
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.
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- 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.
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- 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.
Top Data Science Skills to Learn
| SL. No | Top Data Science Skills to Learn | |
| 1 | Data Analysis Programs | Inferential Statistics Programs |
| 2 | Hypothesis Testing Programs | Logistic Regression Programs |
| 3 | Linear Regression Programs | Linear Algebra for Analysis Programs |
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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