Programming Language and Its Pros & Cons.

Introduction

R is a programming language used for statistical computing and graphics. It is supported by the R Core Team and the R Foundation for Statistical Computing. R was created by statisticians Ross Ihaka and Robert Gentleman and is widely used by data miners, bioinformaticians, and statisticians for data analysis and developing statistical software. The R language offers a range of functions, and users have created packages to enhance its capabilities.

According to user surveys and scholarly literature databases, R is one of the most commonly used programming languages in data mining. As of March 2022, R is ranked 11th in the TIOBE index, a measure of programming language popularity. It previously reached its highest position at 8th place in August 2020.

The official R software environment is an open-source and free software within the GNU package, available under the GNU General Public License. It is primarily written in C, Fortran, and R itself. Precompiled executables are provided for various operating systems. R can be accessed through a command line interface, and there are several third-party graphical user interfaces available, such as RStudio (an integrated development environment) and Jupyter (a notebook interface).

Feature

Data Processing

R's data structures include vectors, arrays, lists, and data frames. Vectors are ordered collections of values and can be mapped to arrays of one or more dimensions in a column-major order. This means that values are filled in along the first dimension first, then across the second dimension, and so on. R supports array arithmetic and is similar to languages like APL and MATLAB in this regard.

Programming

R is an interpreted language that can be accessed through a command-line interpreter. For example, if a user types 2+2 at the R command prompt and press enter, the computer will reply with 4.

R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions. Due to its S heritage, R has stronger object-oriented programming facilities compared to most statistical computing languages. Extending R is facilitated by its lexical scoping rules, which are derived from Scheme. R uses S-expressions to represent both data and code. R's extensible object system includes objects for regression models, time series, and geospatial coordinates, among others. Advanced users can write C, C++, Java, .NET, or Python code to manipulate R objects directly.

Pros & Cons

Pros side:

R is actively used for statistical computing and design. It has brought about revolutionary improvements in big data and data analytics. It is the most widely used language in the world of data science. Some industry giants like Google, LinkedIn, and Facebook rely on R for many of their operations.

R plays a crucial role in Data Science, offering several benefits:

Code execution without a compiler - R is an interpreted language, allowing code to be run without the need for a compiler. This makes code development easier.

Efficient vector calculations - R is a vector language, enabling functions to be applied to a single vector without the need for loops. This makes R powerful and faster than other languages.

Statistical capabilities - R is widely used in biology, genetics, and statistics. It is a Turing complete language, capable of performing various tasks.

Cons Side:

Weak Origin

R shares its origin with a much older programming language "S". As a result, its base package lacks support for dynamic or 3D graphics. However, packages like Ggplot2 and Plotly can be used in R to create dynamic, 3D, and animated graphics.

Data Handling

In R, objects are stored in the physical memory, unlike languages like Python. Additionally, R consumes more memory compared to Python. Moreover, R requires all the data to be stored in memory, making it less suitable for Big Data. However, this limitation can be overcome by using data management packages and integrating with Hadoop.

Basic security

R lacks basic security features, which are essential in most programming languages like Python. Consequently, there are limitations to embedding R into web applications.

Complicated Language

R is not an easy language to learn and has a steep learning curve. Individuals without prior programming experience may find it challenging to learn R.

Lesser Speed

R packages and the R programming language are slower compared to other languages such as MATLAB and Python.

Spread Across various Packages

R algorithms are spread across different packages. Programmers without prior knowledge of these packages may encounter difficulties when implementing algorithms.

My Points

R is a powerful programming language widely used in data mining, data analysis, and statistical computing. It offers a range of functions and has a strong object-oriented programming system. Some of the pros of R include its popularity in the data science industry, the ability to run code without a compiler, and its extensive use in statistics. However, there are some cons to consider, such as its weak origin, data handling limitations, and the fact that it may not be suitable for handling big data. Additionally, R has a steep learning curve and may be slower compared to other languages like MATLAB and Python.