R and Python are the most popular programming languages that match well for all kinds of data science tasks. If you are a beginner who wishes to start your career in the data science field, then it is common for you to get confused about which language to learn first. In case, you are in a dilemma of what language to learn or choose for your data science project, just get to know the difference between R and Python. Knowing the comparison will give you an idea of what language to learn first. Also, the comparison will assist you in identifying the right programming language for your project needs. If you wish to know how R is different from Python, continue reading this blog post. For your comprehension, here we have presented a comparative analysis of R vs. Python.
Before we move on to see the major differences between R and Python, first, let us have a look at a brief overview of R and Python.
What is R?
R is an open-source programming language that was created in the year 1993 by Ross Ihaka and Robert Gentleman for statistical computing and graphics. Initially, this programming language was used only for scientific research and academia. But, now, it is a popular analytics tool used for both data analytics and business analytics. Moreover, one can use R for machine learning algorithms, time series, linear regression, statistical inference, etc.
In general, R comes with the command line interface and is available to use widely across all platforms like Windows, macOS, and Linux. Mainly, R is used as a statistical software and data analysis tool. This is because, with the help of R, the users can perform all kinds of statistical tests that include linear modeling, classifications, non-linear modeling, and clustering.
Furthermore, because of its large community, R offers extensive possibilities. Especially, R has produced a wide range of data science-related packages that can all be accessed through the Comprehensive R Archive Network (CRAN).
Another aspect that distinguishes R is its ability to generate high-quality reports with data visualization tools, as well as its frameworks for developing interactive web applications. In this regard, R has often been considered the greatest tool for creating attractive graphs and visualizations.
What is Python?
Python is an open-source, general-purpose programming language initially developed in the year 1991 by Guido van Rossum and later developed by the Python software foundation. It was created with code readability in mind, and its syntax lets programmers communicate their notions in fewer lines of code. Currently, programmers in various software domains have been using Python for activities relevant to data science, gaming, and web development.
Python is considered a simple language to read and write because of its close resemblance to human language. Moreover, Python’s great readability and interpretability is the key to its design. Particularly, because of all these reasons, Python is regarded as a go-to programming language for beginners with no coding experience.
Over time, Python has been earning more popularity in the data science field, due to its simplicity and the countless options offered by the hundreds of specialized libraries and packages that support all types of data science tasks including machine learning, deep learning, and data visualization.
Another reason for the popularity of Python is its active community of users. Python is supported by a large community of users and developers who ensure the language’s steady growth and refinement, as well as the constant release of new libraries tailored for a variety of uses.
Also read: Python Language Advantages and its Applications
R vs. Python: Get to Know the Key Differences
By now, you will have gained a little knowledge of what R and Python mean. Next, let us have a look at the difference between R and Python in various aspects.
R vs. Python: Purpose
Python is a general-purpose programming language whereas R is a statistical analysis tool. However, both are suitable for all kinds of data science tasks. Basically, R and Python were developed for different purposes. But when compared to R, Python is considered a more versatile programming language that is used in various software domains including web development, gaming, and software development.
R vs. Python: Types of Users
R is helpful in academics and other fields such as finance and pharmaceuticals. Moreover, it is an ideal programming language for statisticians and researchers with little programming experience. On the other hand, Python is the primary language of choice for software developers venturing into data science because it is a general-purpose language. Furthermore, Python’s emphasis on productivity makes it a better tool for developing complex applications.
R vs. Python: Learning Curve
The syntax of Python has a close resemblance to the English language. As a result, it is an excellent language for new programmers, with a smooth and linear learning curve. On the other hand, R is suitable for performing basic data analysis quickly and effectively, but things become more difficult with complex jobs. So, it takes more time for R users to understand the language. Python is generally the best language for beginners. R is easier to learn at first, but the complexities of advanced functionalities make it more difficult to build expertise.
R vs. Python: Common Libraries
Both Python and R have broad ecosystems of data-science-specific packages and libraries. The Python Package Index (PyPI) hosts the majority of Python packages, whereas R packages will be available in the Comprehensive R Archive Network (CRAN). Some popular data science libraries and packages in R are ggplot2, Caret, tidyr, and Shiny. On the other hand, Python has different data science packages such as NumPy, Pandas, TensorFlow, Scikit-learn, and Matplotlib.
R vs. Python: Speed
R is a low-level programming language. Hence, it requires longer codes for simple procedures, which in turn takes more time to execute and thus reduces the speed. On the contrary, Python is a high-level programming language with which users can create essential but speedy applications. Generally, Python codes are too simple and short in length and therefore they run faster.
R vs. Python: Graphics and Visualization
In general, it is more effective and efficient to understand the visualized data rather than the raw data. Several R programs offer advanced graphical features. Usually, when selecting data analysis tools, it is essential to consider visualizations. Basically, Python has several fantastic visualization libraries. However, they are complex and do not produce clear results. R’s advanced graphical features make it superior to Python.
Also read: Java vs. Python: A Comparative Study
R vs. Python: A Comparison Table
In the table presented below, we have shared a few more differences between R and Python. To understand the comparison between the two programming languages, take a look below.
| R | Python |
| R is a programming language used for statistical computing and graphics. | Python is a general-purpose programming language used for data analysis and scientific computing |
| It contains features for statistical analysis and representation. | It contains essential features for developing GUI applications, web applications, and embedded systems. |
| Performs data science tasks with many easy-to-use packages. | Performs matrix computation and optimization. |
| Popular R IDEs are RStudio, RKward, R Commander, etc. | Popular Python IDEs are Spyder, JupyterLab, Eclipse+Pydev, Atom, etc. |
| It supports a large dataset for statistical analysis | It is general-purpose programming for data analysis. |
| Primary users are Scholar and R&D | Primary users are Programmers and developers |
| Contains nearly 19,000 packages in the Comprehensive R Archive Network | It contains 300,000+ packages in the Python Package Index |
| Its main objective is to perform data analysis and statistics. | Python plays a key role in deployment and production. |
| R runs locally. | It supports integration with other apps. |
| In R, it is easy to obtain the primary results. | Python is excellent at deploying algorithms. |
| Supports RStudio and has a wide range of statistics and general data analysis and visualization capabilities. | Supports Conda environment with Spyder and Ipython Notebook |
R vs. Python: Which Language Should You Learn?
The answer to this question depends on your interest and project needs. If you want to work on any data science or statistical analysis project, then R is the better choice for you. However, for web development, software development, and gaming, you can choose to learn Python. In general, both R and Python are excellent options for activities related to data science.
In terms of ease of learning, R has a steep learning curve whereas the learning curve of Python is smooth and linear. R is easy to learn at first but becomes more challenging as you use sophisticated features. On the contrary, Python is a beginner-friendly language with English-like syntax. If you are a beginner with no or less coding experience, then it will be difficult for you to learn R in the beginning. But once you get a grip, it will become easy for you to use R. Python emphasizes productivity and code readability. So, it will be easy for you to learn and understand Python.
Actually, it is best to learn both R and Python. New programmers can start the journey with Python, while the ones with some experience in Programming can learn either R or Python depending on their project requirements.’
Also read: LUA vs. Python: Know the Key Differences
Conclusion
Hopefully, by now, you will have gained a better understanding of the difference between R and Python. In case, you have any query related to R and Python or if you need R or Python project help from programming experts, feel free to contact us right away.
