Python vs R: Major Differences You Should Know

Quick Summary: R and Python are the best programming languages that dabble in data science or analytics-powered solutions. Both languages have benefits and drawbacks, even though both can drive our future to a great life. However, there's a constant debate between them on which to choose. So, in this blog, you'll dive into the difference between the two popular programming languages, making things more transparent. Read on!

‘Any sufficiently advanced technology is indistinguishable from magic.’ – Arthur C. Clarke, Science Fiction Writer

As we can notice, the world is headed to the next technological marvel. Artificial Intelligence (AI) and Data Science are the front-runners in this technologically advanced time giving reality to ideas we never thought would be true.

According to Global Data Science Platform Market Research Report, due to the growth in emerging technologies like Big Data, Artificial Intelligence, Machine Learning, analytics, and IoT, the data science market is expected to generate USD 322.9 billion by 2026.

Per se, if you’re familiar with this realm, you would also be familiar with the two programming languages gaining popularity nowadays, a common subject of discussion and curiosity.

So, if you dabble in data science or data analytics-powered solutions, you probably know about R and Python programming languages and the Python vs R debate going around them. Both languages have benefits and drawbacks, even though both can drive our future to a great life. Python R programming languages accomplish this through data-driven innovation, Machine Learning, and AI.

R and Python are open-source, free programming languages that work on Linux, macOS, and Windows. Moreover, they can handle almost any data analytic assignment. Both are regarded as simple languages to learn, especially for beginners.

If you’re starting, there must be several questions: What is Python programming? What is R programming? What is the difference between R and Python? What language will be easy to learn - R or Python? Etc.

All right, let’s take it slow!

In this article, we’ll dive into the difference between the two popular programming languages - R and Python. But before that, let’s take a quick look at each language first.

What is Python?

Python is a multipurpose, object-oriented programming language that strongly emphasizes the easy readability of its code. Introduced in 1989, Python is simple to learn and a popular choice among developers and programmers.

In fact, Python is second on the list of the most widely used programming languages behind Java.

Multiple Python libraries are assisting in data science tasks, including the ones listed below:

• For managing big dimensional arrays, use NumPy
• Data analysis and manipulation utilizing Pandas
• Matplotlib is used to create data visualizations

But what is all the fuss about Python programming? Well, python programming is frequently used for seamless python development to create software and websites, automate processes, and analyze critical data. It is one of the most beloved programming languages utilized today, thanks to its versatility and beginner-friendliness.

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Latest Features of the Python Programming Language

There are several features of Python, the most significant of which are listed below:

1. Free and Open-source
2. Easy to Learn and Read
3. GUI Programming Support
4. Features Extensibility
5. Portable Language
6. Frontend and Backend Development
7. Dynamic Memory Allocation
8. Easy to Debug
9. High-level Language
10. Object-oriented Language

Advantages of Python

Being the third most popular programming language adored by 51% of developers, Python has many advantages. They are:

• Multiple Libraries – Python provides many libraries when compared to other technologies. Moreover, all these libraries can perform the key tasks linked to data science.

• Versatility – The language is among the universal programming languages. It is well-structured, clean, and simple to use and learn. Exploratory data analysis is made simple by Python’s versatility. However, it’s object-oriented, and can transition to functional features to fit into various programming paradigms.

• Efficiency – Its integration and management skills improve and help in saving more time. Python applications can be embedded. Development utilizing other programming languages like C++ can be merged with Python scripts for better efficiency.

• Open-source – Python is an open-source and easily downloadable programming language that helps your python developers in every way possible. Anyone can contribute to enhancing the libraries and functionalities, thanks to its most active help forums.

Disadvantages of Python

Nothing in this world is perfect. Everything comes with a few limitations, and the same is true with Python. So, to decide between R programming vs Python without partiality, we also need to know the limitations.

• Mobile Ecosystem – Python is slightly less appropriate for python mobile app development. According to developers, it is considered a second language choice in such a setting. It can be used, though, with some additional effort.
• Agility – Python is considerably slower than other programming languages because it is an interpreted language.
• Database Access Layers – Python’s database access layers are less developed than those of Open Database Connectivity (ODBC) and Java Database Connectivity (JDBC). This is the main reason why Python is a less popular choice for database communication
• Memory Utilization – Python requires extensive usage of memory. The operation is slowed down as different entities need to be accessed.

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This concludes our discussion on Python. So, let’s move forward to the next part of our article and learn more about R programming.

What is R?

Are you wondering what R is? Or what is R programming?

R is an open-source programming language for data visualization and statistical analysis. R was developed in 1992. It has a robust ecosystem with extensive data models and advanced mechanisms for data reporting. At last count, the Comprehensive R Archive Network (CRAN) offers more than 13,000 R packages for deep analytics.

Woah, that’s a huge number!

R offers a wide range of tools and libraries for the following, making it popular among researchers and scientists who study and work in Data Science.

• Preparation and data cleaning
• Constructing visualizations
• Machine Learning and Deep Learning algorithms’ development and evaluation

Furthermore, in RStudio, an integrated development environment (IDE), R is frequently used for simple statistical analysis, visualization, and reporting.

Latest Features of the R Programming Language

R is among the most popular programming languages for learning Data Science. It comprises unique features that contributed to its wide popularity. Let’s look at some latest and significant features of R.

1. Wide Array of Packages
2. Open-source
3. Use Cases for Different Industries
4. Rapid Calculations
5. Integration with Other Technologies
6. Comprehensive Language
7. Several Graphical Libraries
8. Cross-platform Compatibility
9. No Compiler Required
10. Active Community

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Advantages of R

Let’s talk about some significant benefits of the R programming language.

• Package Availability – R includes a large collection of packages for creating statistical data analysis, and machine learning applications.

• Data Wrangling – R can turn a disordered code into a structured one. It utilizes packages like dplyr and readr for this process.

• Platform Independency – R is independent and compatible with all major operating systems, including Windows, mac, and UNIX.

• Open-source Platform – You can download R for free, as it is an open-source language.

Disadvantages of R

Let’s discuss the limitations of using the R programming language, as we did with Python in the above section.

• Slow Runtime – R is a slow-processing language with lengthy runtime. It requires more time to generate an output compared to other languages like Python and MATLAB.
• Challenging to Learn – It’s challenging for a newbie to learn R because it’s a complex language, unlike Python.
• Poor Security – R lacks fundamental security features, which makes it almost impossible to incorporate when developing web applications.
• Memory Consumption – R occupies more memory because all the objects are stored in physical memory. As the program collects more data over time, the workflow slows down.

In the above sections, we discussed R and Python programming languages, their features, and their pros and cons.

Now, let’s get to another crucial part of our article: understanding the difference and comparing Python vs R based on a set of criteria.

R vs Python: The Fundamental Differences

Even though R and Python are widely used programming languages for data analysis and machine learning (ML), each of them has unique features.

Moreover, there are different benefits and limitations associated with each language. However, both R vs Python are well-liked options available in the market.

So, to determine the best programming language for your project, let’s compare and contrast the top key differences between R vs Python for Data Science:

1. Graphics and Visualization – When data is visualized, it is simpler to understand. The graphical interpretation of data is made possible by several R tools. Custom graphs are available with Ggplot2. Python also provides libraries for visualization, although more complex than R. R has a wonderful printed library that assists in creating publication-caliber graphs.

2. Speed and Performance – Even though big data analytics use R and Python, the latter is a better choice in terms of performance for developing reliable yet quick apps. R is slightly slower than Python but quick enough to handle huge data computations.

3. Statistical Accuracy – R offers superior support and extensive libraries because it was created for data statistics. Python works much better when creating and deploying applications. However, R and its libraries implement a wide range of statistical and graphical approaches for data analysis.

4. Deep Learning – R and Python are widely popular programming languages. While Python has several cleverly crafted libraries available, R has KerasR, an interface to Python’s deep learning package. As a result, both languages currently have a decent collection of deep learning tools. Python, however, stands out when it comes to deep learning and AI.

5. Community Support – The communities for R and Python are both strong. User’s mailing lists, user-contributed articles, codes, and StackOverflow groups are available for both languages. Thus, there’s a tie between both languages in this scenario. However, there is no customer service support for either language.

6. Unstructured Data – Around 80% of all the data in the world is unstructured. The majority of data generated from social media is unstructured. Python provides tools for analyzing unstructured data, including NLTK, PyPI, and scikit-image. Although R provides packages for analyzing unstructured data, its support is not as strong as that of Python.

Summing Up!It’s a close race between Python and R; each programming language has their own advantages and disadvantages.As many organizations do not prefer using R, Python appears to be a little more popular among data scientists and developers. R was specifically created for statistical analysis, and it excels at it. On the other hand, Python is a general-purpose language for creating applications.Both programming languages provide a diverse range of libraries and packages; in certain cases, cross-library support is also available. Thus, the choice of R or Python depends entirely on the user’s needs and requirements.Feeling a bit confused?Do not be worried; we are here to assist you!So, if you are still having a hard time selecting between R and Python or considering switching from R to Python or vice versa, get in touch with the most trusted python application development company, Radixweb.As an experienced industry leader in application development and consulting services, Radixweb has provided rich digital experiences for mobile and web platforms for over 25 years. Our developers are well-versed with both Python and R programming languages and have expertise in developing customized solutions according to clients’ needs and requirements.Reach out to us to know which will be better for next development project, R or Python.