The world is headed for the next technological marvel. The first is data science and artificial intelligence. These two bring to life things we could never have imagined. If you know this world, you also know two programming languages that are always interesting and debated.
R vs Python are programming languages and these two languages are similar in a few ways. It can be downloaded and used for free; It is used majorly in data science. Let’s see what these languages are and how they are used.
What is Python?
Python is a definable, high-level object-oriented programming language. It comes with built-in data structures, dynamic typing(performing type checks at runtime), and binding(mapping different objects to each other), making it the top language for creating applications Python syntaxes are simple, not complicated as readable and easy to learn, so you learn Python It is a good resource for beginners and experienced users.
Advantages of Python
- Versatility: Language is one of the most versatile. It’s stylish, easy to use and well laid out. The simplicity of Python makes exploratory data analysis hassle-free. Python is object-oriented, but it evolves into departments that allow models of programs.
- Open source: Python is easy to download. It has an active support forum, and anyone can contribute to improving the libraries and their operations.
- Libraries: Python has many libraries needed to perform basic data science tasks.
- Performance: Increases ability to integrate and manage and saves a lot of time.
- Embeddable: Python codes are embeddable. Python codes can be integrated with other programming languages like C++.
Disadvantages of Python
- Speed: Python is an interpreted language and therefore slower than other programming languages.
- Mobile Environment: Python is not suitable for Android and iOS environment. The developers say that this is an easy language in such a situation. However, more effort can be used.
- Memory usage: Python consumes significant amounts of RAM. If more resources are needed, the process is slower.
- Database Access Layers: Python database Access Layers are underdeveloped compared to Java Database Connectivity(JDBC), and Open Database Connectivity(ODBC), making it an underused database connection
- Threading: Threading or streaming multiple tasks simultaneously is a downside in Python due to its Global Interpreter Lock(GIL).
What is R?
R is a programming language for statistical analysis or computers and graphics. R introduces a wide variety of statistical techniques such as linear modeling, nonlinear modeling, statistical testing, clustering, etc. R One strength is the ease with which plots can be generated using statistical notation and formulas
R is available as free software. It compiles and runs on UNIX, Windows, and macOS. R allows programmers to add additional functionality by defining the specific tasks used. For complex projects, the user can combine C and C++ code at runtime. R can be extended with packages in other languages such as C++.
Advantages of R Programming
- Open source: R is an open source language and can be downloaded and used for free. Editing its source code can also help.
- Platform Independent: R is platform-independent and can run on all operating systems including UNIX, Windows, and Mac.
- Data Wrangling: With packages like readr and dplyr, R has the ability to turn messy code into structured.
- Plots and Graphs: Creates interesting graphs with R scripts and formulas using ggplot and plotly.
- Package availability: R has many packages dedicated to developing machine learning, data analysis, and statistical functions.
Disadvantages of R
- Memory: While all objects are stored in physical memory, R consumes a lot of memory. Over time, as the program contains more data, the process slows down.
- Security: R lacks special security features that make it difficult to install in web applications.
- Difficult to learn: Unlike Python, R is a complicated language and difficult for beginners to learn.
- Slow Runtime: R is a slow runtime language. It takes more time to give output compared to other languages like MATLAB and Python.
- Data Handling: Data handling in R is tedious because it requires all data processing to be in one place. Not ideal for Big Data. However, it does have integration that makes control a bit easier.
Python vs. R: Full Comparison
The comparison difference between r programming and python depending on the purpose for which you want to differentiate between the two. You can compare the use of R vs Python for data analysis or on a related factor such as design, user base, learning curve, flexibility of use, technical limitations, features and more.
To help you come to a quick conclusion, here is a graphic comparing difference between r and python in table form.
| Python | R Programming |
| Python is a general-purpose language that is used for the deployment and development of various projects. Python has all the tools required to bring a project into the production environment. | R is a statistical language used for the analysis and visual representation of data. |
| Python is better suitable for machine learning, deep learning, and large-scale web applications. | R is suitable for statistical learning having powerful libraries for data experiment and exploration. |
| Python has a lot of libraries. However, it can be complex to understand all of them. | R has fewer libraries compared to Python and is easy to know. |
| Python can be used for various purposes like building a graphical user interface, develop games, etc., despite being an object-oriented language. | Along with object-oriented programming, R can also be used to develop music. |
| Python has a simple syntax and is easy to learn. | R has a relatively complex syntax and the learning curve is not straightforward. |
| Python’s statistical packages are less powerful. | R’s statistical packages are highly powerful. |
| Python is mainly used when the data analysis needs to be integrated with web applications. | R is generally used when the data analysis task requires standalone computation(analysis) and processing. |
| Python can be used to build applications from scratch. | R can be used to simplify complex mathematical problems. |
| There are many Python IDEs available to choose from, a few of them are Jupyter Notebook, Spyder, Pycharm, etc. | A few IDEs for the R language are RStudio, StatET, etc. |
| Python is more popular and has a vast user base. Primary users of Python include developers and programmers. | R is less popular among users. Its users include scientists and Research and development who frequently rely on data analysis. |
Key Differences
Data Science, Data Analysis, Machine Learning etc. Prefer R vs Python. Although used for similar purposes, they are different from each other. R primarily focuses on the mathematical side of the project while Python is flexible in its implementation and data analysis tasks.
R is a powerful tool for visualizing data in graphs. R is difficult to use in a production environment due to the fact that product development is still in progress, while Python is easily integrated into a complex workstation
From a functionality perspective, Python is a good choice because it runs faster in all areas than R. However, both languages are fan favorites of people to work with as they are used.
Conclusion
It’s debatable when it comes to using R vs Python. These two languages come with advantages and disadvantages. Python is widely used by many people, but R is also used. Python is used for a wide variety of things, while R is mainly used for statistics. It is up to the user to choose the language based on the requirement.
Whether you opt for Python or R depends on your project’s needs and your familiarity with each language. Regardless of your choice, ensuring you have skilled developers proficient in your chosen language is crucial. If Python is your preferred language, don’t hesitate to Hire Python Developers who can bring your vision to life with expertise and efficiency.