Features of R Programming
R comes under the most popular programming languages for learning Data Science. It has advanced features that have enabled R to become one of the most widely used programming languages. R has several features which have contributed towards its overall popularity. In this article, we will have a look at some of the unique features of R:
1. Comprehensive Language
R is a comprehensive programming language, meaning that it provides services for statistical modeling as well as for software development. R is the primary language for Data Science as well as for developing web applications through its robust package RShiny. R is also an object-oriented programming language which is an addition to its procedure programming feature.
2. Provides a Wide Array of Packages
R is most widely used because of its wide availability of libraries. R has CRAN, which is a repository holding more than 10,0000 packages. These packages appeal to every functionality and different fields that deal with data. Based on user requirements and preferences, these packages provide different features to their users.
3. Possesses a Number of Graphical Libraries
The most important feature of R that sets it apart from other programming languages of Data Science is its massive collection of graphical libraries like ggplot2, plotly, etc. that are capable of making aesthetic and quality visualizations.
R is an open-source programming language. This means that it is free of cost and requires no license. Furthermore, you can contribute towards the development of R, customize its packages and add more features.
5. Cross-Platform Compatibility
R supports cross-platform compatibility. It can be run on any OS in any software environment. It can also be run on any hardware configuration without any extra workarounds.
6. Facilities for Various Industries
Almost every industry that makes use of data, utilizes the R language. While only the academic areas made use of R in the past, it is now being heavily used in industries that require to mine insights from the data.
The health industry makes use of R for drug design and analyzing genomic strands. Manufacturing industries like Ford use it in their optimization procedures. Furthermore, Airbnb and social media companies like Twitter use it to analyze its users.
7. No Need for a Compiler
R language is interpreted instead of compiled. Therefore, it does not need a compiler to compile code into an executable program. The R code is interpreted one step at a time and directly converted into machine level calls. This makes running an R script much less time-consuming.
8. Performs Fast Calculations
Through R, you can perform a wide variety of complex operations on vectors, arrays, data frames and other data objects of varying sizes. Furthermore, all these operations operate at a lightning speed. It provides various suites of operators to perform these miscellaneous calculations.
9. Can Handle all Sorts of Data
R provides excellent data handling and storage facilities.
In conjunction with data platforms like Hadoop, R facilitates the handling of structured as well as unstructured data that imparts a comprehensive data capability. Furthermore, R provides various data modeling and data operation facilities that are often a result of active interaction with the storage facility. It also provides extensions for SQL and Big Data.
10. Integration with Other Technologies
R can be integrated with a number of different technologies, frameworks, software packages, and programming languages. It can be paired with Hadoop to use its distributed computing ability. It can also be integrated with programs in other programming languages like C, C++, Java, Python, and FORTRAN.
11. R has an Active Community
R is continuously evolving. The contribution is fuelled by the growing number of users who are using R on a daily basis. As mentioned above, R is an open-source library that is supported and maintained by a large user-base. Not only this, R has an engaging community that organizes seminars, boot camps and other training sessions of R. Once you start your journey in R, you will never feel alone.
12. Machine Learning with R
Earlier R had different packages for different machine learning algorithms. This may be considered inefficient and, therefore, the MLR package which stands for Machine Learning in R has become highly popular. This package is useful for all machine learning algorithms and provides other tools that help with machine learning as well.
Pros and Cons of R Programming
Pros of R Programming
Various benefits of R language are mentioned below, which will help you to grasp the concept:
1. Open Source
R is an open-source programming language. This means that anyone can work with R without any need for a license or a fee. Furthermore, you can contribute towards the development of R by customizing its packages, developing new ones and resolving issues.
2. Exemplary Support for Data Wrangling
R provides exemplary support for data wrangling. The packages like dplyr, readr are capable of transforming messy data into a structured form.
3. The Array of Packages
R has a vast array of packages. With over 10,000 packages in the CRAN repository, the number is constantly growing. These packages appeal to all the areas of industry.
4. Quality Plotting and Graphing
R facilitates quality plotting and graphing. The popular libraries like ggplot2 and plotly advocate for aesthetic and visually appealing graphs that set R apart from other programming languages.
5. Highly Compatible
R is highly compatible and can be paired with many other programming languages like C, C++, Java, and Python. It can also be integrated with technologies like Hadoop and various other database management systems as well.
6. Platform Independent
R is a platform-independent language. It is a cross-platform programming language, meaning that it can be run quite easily on Windows, Linux, and Mac.
7. Eye-Catching Reports
With packages like Shiny and Markdown, reporting the results of an analysis is extremely easy with R. You can make reports with the data, plots and R scripts embedded in them. You can even make interactive web apps that allow the user to play with the results and the data.
8. Machine Learning Operations
R provides various facilities for carrying out machine learning operations like classification, regression and also provides features for developing artificial neural networks.
R is prominently known as the lingua franca of statistics. This is the main reason as to why R is dominant among other programming languages for developing statistical tools.
10. Continuously Growing
R is a constantly evolving programming language. It is a state of the art technology that provides updates whenever any new feature is added.
Disadvantages of R Programming
1. Weak Origin
R shares its origin with a much older programming language “S”. This means that it’s base package does not have support for dynamic or 3D graphics. With common packages of R like Ggplot2 and Plotly, it is possible to create dynamic, 3D as well as animated graphics.
2. Data Handling
In R, the physical memory stores the objects. This is in contrast to other languages like Python. Furthermore, R utilizes more memory as compared with Python. Also, R requires the entire data in one single place, that is, in the memory. Therefore, it is not an ideal option when dealing with Big Data. However, with data management packages and integration with Hadoop possible, this is easily covered.
3. Basic Security
R lacks basic security. This feature is an essential part of most programming languages like Python. Because of this, there are several restrictions with R as it cannot be embedded into a web-application.
4. Complicated Language
R is not an easy language to learn. It has a steep learning curve. Due to this, people who do not have prior programming experience may find it difficult to learn R.
5. Lesser Speed
R packages and the R programming language is much slower than other languages like MATLAB and Python.
6. Spread Across various Packages
The algorithms in R are spread across different packages. Programmers without prior knowledge of packages may find it difficult to implement algorithms.