Is r in data science beneficial?

We use numerous softwares and tools in data science, there are various numbers of tools and softwares which can be used, but let me just focus on the R for a while. Let us discuss this in detail and find out if R is really a beneficial tool?

So let’s start from the beginning, R is one of the most reliable tools that is used there in the field of data science. There are a lot of statistical tools available in this software and that was the primary reason that it was used in various analysis tasks. But as the technology in data science advanced, it became quite popular among scientists for analyzing large and small data sets. 

Another amazing feature of this program is that it contains various machine learning packages which make it a very convenient tool for various operations such as boosting, building random forests or performing regression and classification. Seriously, machine learning provides a wide range of packages. 

One more thing that makes this program more reliable is that it is used by various top companies such as:

• Facebook
• Airbnb
• Uber
• Google
• Novartis
• IBM
• And various other big companies too 

One thing that was keeping users from adding this software to their arsenal was that, it has a steep learning curve, it had been considered among one of the most complex languages to learn, but this barrier was removed finally with the help of Tidyverse. Tidyverse made learning R more and more convenient. It almost reduced the steepness to negligible and this language made it to most of the skills sets of the data scientist. 

This language has almost no limit when it comes to excellency and with the growth in its community, it is continuously expanding and the supportive community is adding further values to the users. 

You must be well aware of the most famous ggplot2 package, which is considered the best and is most famous for creating the visualizations. The aesthetic visualizations created by this package are considered to be the most suitable for the visualizations. And not just this, there is a lot of scope of interactivity to the users to make them understand embedded data which is present in the data. 

So in the conclusive statement, I would like to say that R is not just beneficial in data science, but it is one of the best for this job. Well, I wanted to make this statement in the very beginning only, but I wanted to explain the things to you rather than just making a statement without any foundational knowledge.