This essay will discuss the R language's multiple benefits and drawbacks. We will first go through the various advantages that the R language gives us before looking at some of the negatives or limitations of this language.
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We are all aware that the R language holds a unique position in the world of programming, underscoring its importance. However, it is a universal reality that each technology has both benefits and drawbacks. R programming adheres to the same rules. The fact that R programming has both advantages and disadvantages proves the statement.
Advantages of R Programming
Open Source
R may be programmed using open-source software. This suggests that anybody can use R without a licence or payment. You can also advance R by modifying its packages, making new ones, and fixing issues.
Exemplary Support for Data Wrangling
R is a fantastic tool for manipulating data. Unstructured data may be transformed into more organised data using tools like dplyr and readr.
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The Array of Packages
R has several different packages available. Over 10,000 packages are presently available in the CRAN repository, and that figure is continuously rising. These packages appeal to all sectors.
Quality Plotting and Graphing
Well-defined data can be plotted and graphed more easily with R. R sets itself apart from other programming languages by supporting the creation of attractive graphs, which are made possible by libraries like ggplot2 and plotly.
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Highly Compatible
R is very compatible with a wide range of other programming languages, including C, C++, Java, and Python. Additionally, it can be integrated with a number of other technologies and database management systems, such as Hadoop.
Platform Independent
R is a cross-platform language. It is a cross-platform programming language, making it relatively simple to use to run programmes on Windows, Linux, and Mac computers.
Eye-Catching Reports
With tools like Shiny and Markdown, R makes reporting the results of a research relatively straightforward. It is able to create reports with embedded data, graphics, and R scripts. It is even feasible to create interactive web programmes that enable users engage with the results and data.
Machine Learning Operations
R provides a range of tools for carrying out machine learning operations like classification and regression in addition to features for building artificial neural networks.
Disadvantages of R Programming
Weak Origin
R shares roots with the much-older computer language "S." Therefore, dynamic or 3D graphics are not supported by the basic package. Animated and dynamic visuals may be created using well-known R programmes like Ggplot2 and Plotly.
Data Handling
The elements are stored in physical memory in R. This is in contrast to other languages like Python. Furthermore, R consumes more RAM than Python. R also requires the entire set of data in memory, which is in one place. As a result, while working with massive data, it is not the ideal option. The availability of data management solutions and Hadoop integration, however, easily resolves issue.
Basic Security
R is insufficiently secure. Python and the bulk of programming languages need this feature to work. R cannot be incorporated into a web application, which leads to a variety of restrictions.
Lesser Speed
R packages and the R programming language are much slower than languages like MATLAB and Python.
Spread Across various Packages
The algorithms in R are spread among a number of packages. Algorithm implementation may be difficult for programmers without previous package understanding.
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