Cloud Deployment Models
19th August 2020
What is SAS?
20th August 2020
- R is an open-source tool
- R is also a cross-platform compatible language
- R is a great visualization tool
- R is used for data science and machine learning tasks.
R: Open Source Tool
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Since R is an open-source programming language, you can download it for free and start to learn R Programming. And if you already are an expert at R Programming, you can contribute to the R Community by creating your own packages which the entire R community can use. So, you can add your own innovations to the existing set of libraries in R.
R: Cross-Platform Compatible Language
Since R is a cross-platform compatible language, you can run the same R code in multiple operating systems. Let’s say, you are using a windows system, but your client has a MAC, you don’t have to worry at all, because your R code will run without any problems on your client’s system.
R: Visualization Tool
R is a great visualization tool. It provides multiple packages such as ggplot2, ggvis and plotly with which we can create stunning visualizations. This is also one of the major reasons why people learn R Programming. When it comes to visualization R is far ahead when compared to Python.
R: Data Science and Machine Learning Tool
With the help of R language, you can implement various machine learning algorithms such as Linear Regression, Decision Tree and Naïve Bayes.
Pros of R Language
- R is the most comprehensive statistical analysis package, as new technology and ideas often appear first in R.
- R is an open-source that’s why you can run R anywhere any time, and even sell it under conditions of the license.
- It is cross-platform which runs on many operating systems. It’s best for GNU/Linux and Microsoft Windows.
- In R, everyone is welcomed to provide bug fixes, code enhancements, and new packages.
Cons of R Language
- The quality of some packages in R is less than perfect.
- There’s no customer support of R Language whom you can complain if something doesn’t work.
- R commands hardly concerns over memory management, and so R can consume all the available memory.
