Explore The Main Differences Between R and Python
The software development industry is home to many languages designed to build applications and products par excellence. Over time, some of them have adapted to the changing ways of the world and hence, they have naturally taken precedence. Two of the most popular languages today are Python and R, especially in the spheres of Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and other new-age data-driven sectors.
For aspiring developers who are new to this industry, making a choice between which language to master can be a challenge, given that these programs do share some similarities. Upon taking a closer look, the distinctions become all the more evident, making it easier to take a pick. So, which one takes the crown in the battle of R vs Python?
In this blog, we will take a close look at the primary differences between R and Python to answer this very question. Before diving into the differences, let’s understand the purpose of these programs individually.
What Is Python?
The main objective of the Python programming language is to promote code readability. It is known to be simple, straightforward, and versatile. Since its inception, Python has been the ideal choice for a wide range of projects, from simple web applications to operating systems. Any programmer with significant experience in coding can pick up this programming language. Given this fact, it happens to be a preferred one among today’s software developers.
As its easy-to-learn syntax promotes readability, the cost of program maintenance is significantly reduced. Python is both object-oriented and functional, allowing programmers to think of code as both data and functionality. Moreover, Python is compatible with all operating systems. It can help you build native applications for both Windows and Mac computers.
What Is R?
R is an open-source programming language that is primarily leveraged for statistical analysis or in the fields associated with computing and graphics. It is compatible with multiple operating systems like Windows, UNIX, and macOS. R enables developers to add additional functionality by defining user-specific functions. When it comes to complex projects, it allows developers to link the C and C++ codes during the runtime.
There are two key features of the R programming language that gives it an extra edge. Firstly, it gives developers access to diverse tools and techniques, for example – statistical tests, linear modeling, non-linear modeling, clustering, and the list goes on. The second great benefit is that it produces a plot with ease using mathematical formulas. In addition, the R language can be extended with other languages like C++ using the packages.
Key Differences Between R and Python
R vs Python Speed
If we consider parameters like speed and performance, Python does take the lead by a few steps. Given that it is built to function optimally even in the most complex of environments as compared to R, it proves to be an ideal choice for large-scale or complex projects.
R serves a smaller role in the bigger picture by focusing on the statistical part of a project; whereas Python is an expert tool that is versatile enough o accommodate multiple operations systems and environments, as well as effortlessly handle heavy data analysis tasks.
Should You Learn R Or Python?
The fact remains that both Python and R are quite significant in the software industry, and are sure to hold the popular stops for the decades to come. While R is the most sought-after tool for visualizing data in the form of graphs, developers do find it tricky to use R in a production environment due to its yet-to-develop production tools.
On the other hand, Python can work wonders despite the complex environments in which it has to perform. However, this language can come with its own set of challenges as well. Ultimately, it all boils down to specific project requirements and the level of expertise required.
| Python | R |
| Created by Guido van Rossum and first released in 1991, Python is designed to promote code readability. The enhanced readability also led to a significant decrease in the cost of program maintenance. | Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians, and statisticians for data analysis and developing statistical software. The first official release came in June 1995. |
| Python is a general-purpose language that is used for the deployment and development of software applications – right from scratch. Python has all the tools required to bring a project into the production environment. | R is a great tool for statistical computing and graphics. It is a statistical language used for the analysis and visual representation of data. It can also be used to simplify complex mathematical problems. |
| Being flexible and versatile, Python is an ideal choice for building large-scale machine learning and deep learning-related web applications. | Being home to powerful libraries for data experiments and exploration, R is an ideal choice for projects learning towards statistical analysis. |
| Usually, Python is used when developers have to create web applications on the back of data analysis. | In instances where any data analysis task requires standalone computation (analysis) and processing, developers prefer to work with R. |
| This programming language features a simple syntax. As such, it is easy to learn. | Comparatively, R has a complex syntax. As such, mastering this language can be challenging. |
| Most professionals in the software industry are familiar with the ways of Python. It is primarily used by developers and programmers. | The users of the R language are fewer in comparison. It is mainly leveraged by Scientists and R & D teams who have to analyse data. |
| Python is home to a wide range of libraries. Hence, it can be time-consuming to learn how to make the most of them all. | R has fewer libraries compared to Python. This makes it easier to navigate the world of the R and solve problems using the libraries. |
| Developers can choose from multiple python IDEs, to name a few – Spyder, Jupyter Notebook, Pycharm, and more. | The R language features a lesser number of IDEs, for example – RStudio, StatET, and such. |
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