Python vs R: Impartial Comparison Between The Two Best Languages

Quick Summary:

In a short time, Python and R gained a lot of momentum and love from the developer community. Yet, Python vs R is an ongoing debate. Even though both languages are significantly used in machine learning, artificial intelligence, and data science projects; both have different strengths, limitations, and weaknesses.

If you are confused about choosing the best open-source language for your new data science project, then our unbiased Python vs R Comparison will help you escape your misery.

Introduction to Python and R

Python and R are similar in many ways; both are open source, free to download, and play a dominant role in bringing data science projects to life. Which programming language is best for data science tasks – is not the right question. But how to use and derive value out of both Python or/and R is the right question to ask.

What is Python?

According to python.org, Python is a high-level, interpreted programming language. It is developed with a vision to convert a long-lasting possibility of rapid application development into a reality. It combines the best of dynamic semantics and dynamic typing so that we can use it as a simple scripting language and connect existing components.

Python’s readable syntax and its extensive standard library provide a high-level programming language that non-programmers can easily understand. Due to the extended support offered by Python through its, modules and packages, it enables significant code reuse and advocates program modularity. Being an open-source language, developers can download and use it on all platforms for free.

Advantages of Python

• Python is open-source and freely downloadable.
• You can change, customize, and contribute to Python libraries.
• Python is used in diverse tasks, including embedded systems, data science, machine learning models, robotics, and more.
• Python offers cutting-edge APIs like TensorFlow, PyTorch, Keras, NumPy, etc., useful in neural network development.
• Python is a user-friendly programming language.
• Python is fully secure, and its web frameworks are dominantly used for the development of web apps.
• Python is capable of handling large datasets, ensuring faster data file loading, and works seamlessly with the Big Data ecosystem.

Disadvantages of Python

• Python is slower than other programming languages like C, C++, and Java, as it is an interpreter-based language.
• Python performs poorly in statistical analysis compared to R due to a lack of statistical packages.
• Sometimes developers may face runtime errors due to the dynamically typed nature.
• The flexible data type in Python consumes a lot of memory, causing tasks requiring heavy memory to suffer.

What is R?

According to r-project.org, R is a programming language that offers a comprehensive environment for statistical computing and graphics. John Chambers and colleagues at Bell Laboratories (now Lucent Technologies) developed the language R keeping the GNU project similar to the S language.

Equipped with multiple graphical and statistical techniques, R is one of the few fast, and flexible programming languages. As R offers an open source route, it is predominantly used by statistical researchers and programmers to conduct research in different methodologies.

Advantages of R

• R is an open-source language freely available for use.
• R allows developers to customize, contribute, and improve its libraries, source code, and features.
• Packages such as readr and dplyr help in the seamless conversion of unstructured data into structured data.
• It enables the easy creation of appealing graphs with notations and formulas with the use of ggplot and plotly.
• R has versatile packages that make the development of machine learning, data science, and statistical projects easy.
• It has an active and engaging community and forums offering endless support and development help to the R developer community.

Disadvantages of R

• R offers limited support for dynamic 3D graphics, as it needs critical modern features of a current and comprehensive programming language.
• R has a tendency to store all objects in its physical memory; thereby, increasing its memory usage. Hence, using it for Big Data is not convenient.
• Security is not that lucrative in R; hence, it cannot be embedded in web applications or used as a backend computation language.
• R has an exorbitant and complex learning curve.
• The majority of packages in R could be faster compared to Python and MATLAB

Python vs R Comparison Table

The comparison parameters between Python vs R vary 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 based on the objective of design, user base, learning curve, usage flexibility, technical limitations, features, etc.

To help you reach a quick conclusion, here is an image comparing Python vs R in a tabular form.

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Python vs R: Realistic Comparison

Post-COVID, we are moving to a new technological frontier led by data science, artificial intelligence, and machine learning. These three front liners of the modern technology evolution play a significant role in bringing so many things into a reality that we seldom imagined. If you are aware of these technological evolutions, then you would be mindful that Python vs R for data science is a matter of interest and feud for many.

Both languages have many similarities and dissimilarities, many advantages and disadvantages, and strengths and weaknesses. Yet, you need clarification when it comes to choosing one between them. Let’s differentiate Python vs R on a more comprehensive level.

🟠 Popularity

Python: According to The Importance Of Being Earnest (TOIBE) index, Python scaled the highest position from the day it was released. It reached No-1 in December 2022 with an overall rating of 16.36%, representing an increase in demand of 2.78% over the last year. Besides, according to Stack Overflow Developer Survey, Python became the third most popular language.

R: According to the Same TIOBE report, R is not as popular as Python, yet it does its career best by reaching No-13. R received a 1.04% rating; but slipped in demand and popularity by –0.21%. It is interesting to note that R was at No-8 in the year 2020, but it is decreasing in popularity year-on-year.

🟠 Speed and Performance

Python: As a high-level programming language, Python makes a perfect choice for building critical applications quickly. Although Python has earned more praise than R, they differ minutely in execution time and speed.

R: Conversely, R is a complex language where you need to write lengthy code even for simpler processes, increasing the development time. Similar to Python, even R is capable to handle larger and more robust data operations.

Both languages are slower than compiled programming languages like C or C++. Yet, they overcome this issue by allowing specific C or C++-based extensions to achieve the speed matching to the C language.

🟠 Libraries and Packages

Python: Python libraries and packages are collections of related code modules used repeatedly by different programs. Python libraries such as Matplotlib, Seaborn, and Plotly are used for plotting numerical data (Visualization). The Pandas library provides flexible high-level data structures and tools that are useful in data analysis, cleaning, and manipulation. The NumPy library doubles up as an ML tool that supports multidimensional data and large matrices. It has in-built mathematical functions for computations.

R: On the contrary, R is also equipped with various libraries and packages, like the ggplot2 library, often useful for data visualization, and the shiny package, widely utilized for building interactive web applications. Additionally, the RCrawler package is well known for web scrapping and domain-based web crawling, whereas tidyverse is excellent for data manipulation. CRAN is another alternative package for R, which is less standardized with a varied API and usage, making it hard to learn and combine.

R vs Python for data science is not a subject of debate, as they both offer thousands of packages and libraries you can use for free in your project. Python is an excellent choice for building full-fledged applications, and R would be a better option if you want to manipulate data from popular data stores. Suppose you can create, use, destroy, and manipulate various environments with multiple or different packages installed. In that case, archiving similar performance from R is a challenge due to the limitations of its packages.

🟠 Graphics and Visualization

Python: Several libraries in Python have been made available recently to create beautiful, stunning, and engaging data visualization and graphics. Libraries such as Matplotlib, Plotly, Seaborn, GGplot, Altair, Bokeh, and many more come with unique advantages to create stunning graphics and visualizations. You can use these libraries to create line charts, bar graphs, scatter plots, heat maps, and similar visualizations.

R: You must write a few lines of code in R to create beautiful and visually stunning graphics and visualizations. There are a series of packages available in R like Plotly, ggplot2, tidyquant, taucharts, ggiraph, geofacet, and many more for data visualization.

Some feel that visualization in R is more straightforward than extensive libraries in Python, rather than helping make data visualization a complex process. However, Seaborn and Matplotlib have debunked this myth, and Seaborn enables you to create stunning visualizations by writing few lines of code than what you will write in ggplot2.

🟠 Learning Curve

Python: Python has a smoother learning curve due to its simple syntax, which is similar to English. Many programmers find it easier to code in Python due to the less tie consumption in writing the code.

R: Many feel R has a longer or more complex learning curve. Due to the non-standardized code, R is a little difficult to master. Even some experienced programmers think that R could be more convenient, which has often made them awkward.

Although R is difficult for beginners and entry-level developers, statisticians and people with prior experience in statistics find it easier to understand. R developers often feel that Python focuses on less essential things. Yet, go with R, if you are looking for an easy and time-efficient programming language.

Python vs R: Which is Right For You?

When to Use Python?

• Prefer using Python when you want to delve into data analysis or want to apply statistical techniques.
• Python is a good choice when you want to create something unique and innovative. You can use it for scripting websites and other applications.
• Python is suitable for projects wherein you need to incorporate statistical code into a production database or when you want to integrate data analysis projects with web apps.

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When to Use R?

• When your focus is academics and research, use R. You can also use it for exploratory data analysis.
• When creating complex statistical functions, use R. Leverage versatile statistical tests and models to create complex statistical analysis.
• Use R when your data analysis projects need a standalone computing analysis or an individual server.

R vs Python Use Cases

Python Use Cases

R Use Cases

Key Takeaways

Frequently Asked Questions (FAQs)

Which open-source language is better, Python or R?

The significant difference between the two is Python is a general-purpose programming language, while R is a statistical programming language.

However, according to recent trends and the data scientist community, Python is more versatile than R making it a default choice for data science projects. Besides, you can use Python for data manipulation, web app development, and building ML algorithms. At the same time, R has limitations in these fields but is a dominant choice for statistical research and data visualization.

Which is more in demand, Python or R?

Python is in more demand compared to R due to its readability, simplicity, ability to build complex projects, and reliable performance.

Here are some of the major reasons for increasing Python demand

• Python libraries such as sci-kit-learn, TensorFlow, and Keras significantly build ML models from scratch
• Python is easy to integrate with other languages.
• In terms of memory usage, Python outsmarts R.
• Python is preferred more due to execution speed and more straightforward syntax.

Can I use Python for data science?

Yes, you can use Python for data science projects. In fact, Python is one of the widely used and preferred open-source languages by data scientists to execute various data science projects. Besides, the functionality of Python to deal with mathematics, statistics, and scientific functions is impressive, including the availability of multiple libraries and packages, making it incredibly productive and versatile.

What are other languages used in data science?

Apart from Python and R, JavaScript, Scala, SQL, Julia, C/C++, MATLAB, and SAS are some of the programming languages wisely used in data science.

Can I use Python and R for the same project?

Yes, you can use Python and R together in the same project. For example, use Python for data collection and cleansing and R for data visualization. The rpy2 library allows calling R functions from within Python. Else, you can use tools such as Jupyter Notebook to mix codes from both languages to get the desired outcome.