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.

Python vs R - The Right Choice For Data Science in 2025

Before we proceed with the Python vs R comparison, it is crucial to know that both Python and R are similar in many ways, open-source, and crucial in bringing your data science projects to life. Both programming languages are equally efficacious and efficient when creating business projects. Let us look at the circumferential understanding and advantages of the two.

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

Disadvantages of Python

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

Disadvantages of R

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 object oriented 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.

Parameter	Python	R
General	Being a general-purpose programming language, Python is widely used for data analysis and scientific computing.	R is predominantly used for statistical computing and graphics due to its functional programming environment.
Release Date	First Released in 1991 as Python 0.9.0	First released in August 1993
Designed By	Guido van Rossum	Ross Ihaka and Robert Gentleman
Objective	A general purpose language used for Data Science, Web Development, and Embedded Systems	A statistical programming language used for Data Science and Statistical Modeling
IDE	PyCharm, Spyder, Thonny, IPython	RStudio, Eclipse, StatET, R KWARD
Packages and Libraries	Numpy, Pandas, Pytest, Matplotlib, Requests, TensorFlow, sci-kit-learn, PyTorch, Theano	Ggplot2, data.table, dplyr, Plotly, tidyr, readr, stringr, lubridate, shiny
Syntax	Python has a relatively simple syntax and is easy to learn.	R has a complex syntax and a relatively large learning curve.
Workability	Python consists of many easy-to-use packages	R easily performs matrix computation and optimization.
Integration	Programs that Run Locally	Well-integrated with web apps
Database Handling Capacity	Handles huge database size	Handles all database sizes
Community	Python has a more robust community for ongoing support and development.	R Community is comparatively smaller.
Learning Curve	Linear and smooth	Difficult at the beginning
Machine Learning	Excellent for machine learning with libraries such as Scikit-learn and TensorFlow	Equally good for machine learning with libraries such as Caret and H2O.
Data Handling Capabilities	Python handles structured data effectively and the libraries are efficient for data manipulation, data cleaning, visualization, and for importing and exporting the data.	R is well suited to handle both, structured and unstructured data. With different syntax, it provides similar data manipulation and cleaning functionality along with visualization.

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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 data from the TIOBE Index for May 2024 Python held the first place as it had with the previous year of May 2023. It ranks top with a rating of 16.33%, representing an increased demand of +2.88% from a year earlier. Also, according to the latest data from the Stack Overflow Developer Survey 2023, Python is the third most popular programming language, with 49.28% of the votes being for it.

According to the Same TIOBE index, the R programming language falls behind in popularity and preference among developers, with 24th position at 0.75% ratings. However, popularity is not the only dedication metric to your right choice of business application development. But, in terms of popularity, the R programming language has seen a declining graph compared to Python.

? 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 to read and time-efficient programming language.