Currently, R is among the most widely used scripting languages for statistical programming. Since the early 2010s, the need for R programmers has been steadily increasing, and R continues to be the preferred programming language among data scientists.
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These days, R has also been deep learning-adapted, which made it easier for many statisticians to adopt deep learning in their respective fields and has made R an essential component of the current, developing AI scenario.
Advantages of R Programming
Due to many of its advantages, R continues to be used more frequently than S and S-plus in statistical programming.
R is and will always be an open-source programme because it was created with the goal of creating an open-source version of S.
R is constantly being used and improved by thousands of scientists and statisticians.
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Linux, MacOS, and Windows all support R. It uses little room and almost runs everywhere.
R may be used as a general-purpose programming language with functional programming and object-oriented programming skills in addition to its statistical processing functions.
Due to the inclusion of ggplot2 and plotly, R has significantly better visualisation capabilities than a number of commercial products.
R offers more beautiful graphics that are favoured by professionals worldwide.
R is not a graphical user interface-based environment by nature. It only accepts commands, which makes it simple to save commands as scripts and transfer them between domains.
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R manages sessions effectively. You can easily pick up where you left off because your command history and data are saved between sessions.
A robust and helpful online developer community exists for R.
Limitations of R
The least despised programming language is reportedly R. R is far from flawless, just like any other language, despite all of its advantages. Knowing the drawbacks will be helpful before diving into studying R.
R has a steep learning curve, and it is not an easy language to learn. Because of the command-line interface, beginners have a difficult time getting started. This constraint can be partially circumvented by IDEs like RStudio. Beginners may also find the large selection of packages perplexing.
Physical Memory Hungry: Unlike Python, a prominent competitor, R saves all of its data in the physical memory. Large datasets are difficult to manage as a result. But fortunately, Hadoop integration for R has greatly improved recently, greatly alleviating the problem.
Slower performance: Before your code can run as quickly on R as it does on MATLAB or Python, a lot of optimization is required. When developing a programme, it is essential to have a thorough understanding of how objects operate internally to prevent delayed execution.
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