Top Reasons To Learn R in 2023, R and Python are two of the best programming languages for beginners.
Both programming languages are quite user-friendly for beginners, but today we’ll concentrate on R.
R has amassed a sizable fan base over time, not just in the IT and data science communities, but also in the commercial world.
This is partly due to the fact that R offers a twin advantage because it is both a programming language and an environment for statistical computation and graphics. Nowadays, every sector depends on data.
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Top Reasons To Learn R in 2023
Linear regression, time series, statistical inference, linear and non-linear modeling, classification, clustering, machine learning algorithms, and many more statistical and graphical techniques are included in R.
R also includes excellent capabilities for visualization, reporting, and interactivity, which are essential for both the business and data science fields.
The icing on the cake is that R has packages for a wide range of business-related areas, including finance and econometrics.
R is a fantastic choice for Data Scientists, Developers, and Entrepreneurs alike for the reasons listed above.
Some of the greatest brands in the industry, like Google, Facebook, HP, Airbnb, HSBC, Bank of America, and The New York Times, to mention a few, use R as their go-to programming language today.
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Not yet convinced?
Here are eight justifications for learning R!
1. The tool is open-source
R is a completely free programming language because it is open-source. In addition to being able to freely install it on your computer, you may also update, alter, and copy it.
Additionally, as R is distributed under the GNU license, you are free to redistribute and sell it (General Public License).
2. Cross-platform compatibility exists.
Yes, R can run on a variety of operating systems with various software and hardware requirements.
R can therefore run seamlessly on any operating system, whether you’re using Windows, Mac, or Linux.
Additionally, it can easily import data from Oracle, MySQL, SQLite, and Microsoft Excel.
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3. There is a sizable library there
R has a large library of built-in packages and functions that are intended to meet a variety of applications, as we have said.
It includes specialized programs for tasks like Imputation, Machine Learning, Statistical Modeling, Data Manipulation, and Data Visualization.
You can also create your own package and contribute to the R community because R is open-source.
4. It has a sizable population
A sizable community of engaged developers, coders, and data scientists supports R.
So, you may rest confident that if you ever find yourself stuck or unable to solve a problem, you can always ask for assistance from the R community.
Not only that, but you may also collaborate on projects and engage in idea exchange with other professionals.
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5. It is fantastic for visualizing
For building intricate visualizations, R provides some best-in-class tools like ggplot2, lattice, leaflet, plotly, and RGL, to mention a few. These tools allow you to create excellent graphs.
6. It can support the development of interactive web applications
You can create interactive web apps using R, and you can do it directly from your data analysis program.
Shiny, its packaging, was specifically created for this use.
It makes it easier to develop dashboards, web pages, and much more directly from your R Console.
7. It is statisticians’ and data scientists’ favorite
R is a statistical language by nature. Consequently, it is filled with statistics in every form (like the tools and techniques we mentioned in the introduction).
It provides all the necessary statistical elements, including probability distribution, static graphs (graphic maps, basic plots, etc.), and fundamental statistics (mean, variance, and median).
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8. It has uses in numerous sectors
R has become more popular in the industry as a result of its adaptability and selection of practical packages and features.
For instance, it is applied to genomic analysis in computational biology. Finance firms use it to examine fraudulent transactions and create econometric models.
Conclusion
R programming’s application is expanding across a range of business sectors.
Since it can meet any and all user needs, it is one of the most adaptable and effective statistical tools.
The employment prospects in R are being driven by R’s growing popularity and the adoption of it by reputable businesses.
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