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Python vs R: Which is the most popular language for Data Science in 2022

Nayeem Rahman Chowdhury
8 min read
August 28, 2022
Do you want to become a data scientist but are having difficulty distinguishing between Python and R? Each language comes with its pros and cons. Regardless, you have to know their fundamental differences to choose them for the perfect spot.
Python vs R
Python vs R

Every sector has a big public discussion, like Messi vs. Ronaldo. Or, who sings better, Taylor Swift or Katy Perry? A pretty similar debate exists in the field of data science regarding Python and R. Both are widely used programming languages for a multitude of activities, particularly in the data science industry.

The discipline of data science focuses on identifying, representing, and extracting meaningful relevant data from various information sources so that it can be applied to the operation of various business logic. In order to extract meaningful data, the data scientist employs machine learning, statistics, probability, linear and logistic regression, and other techniques.

R, Python, SQL, SAS, Tableau, MATLAB, and other data science toolkits are among the most useful, with R and Python being the most widely used. However, it can be difficult for a beginner to determine which of the two, R or Python, is convenient. What are the differences between these two? Which one to choose? When? You will get all the answers in this article.

R and Python are two of the most popular languages used in data science; in addition to being open-source, they are also very good at data analysis. Despite their popularity, R and Python are very distinctive, and one may be better suited to certain situations than the other. Even though both languages are introducing the vision to life through artificial intelligence, deep learning, and data-driven advancement, there are advantages and disadvantages.

In this article, we will look at the basics of these two open-source programming languages, the vital differences that distinguish them, as well as how to choose the best one for your requirements.

What is Python?

Python, which was released in 1989, is a language that is easy to pick up for people who are just starting out. It is an object-oriented, interpreted, open-source programming language designed for high performance and conciseness. As a result, it is one of the world's most popular programming languages, following only Java and C. Python's popularity in the data science emerges from its ease of implementation, large open-source community, and simple syntax. It has firmly established itself as the primary language for data scientists.

Python is used by data scientists for web scraping, data wrangling, and a variety of other tasks. It includes a number of specialized libraries for machine learning and deep learning, allowing data scientists to quickly dispatch powerful data models. Scipy, Pandas, Seaborn, and Numpy are among its most popular libraries.  

What is R?

A rising star in the field of data science is R, a programming language that is quickly gaining the popularity. It is a free and open-source programming language designed for statistical analysis and data visualization. Ross Ihaka and Robert Gentleman became the first to develop this alternative approach to statistics, which they formally open-sourced as R in 1993.

R has become the preferred data science language for many organizations and industries. Unlike Python, the R language was created specifically for data analysis and the development of applications and software solutions capable of performing statistical analyses and data mining. R allows for compelling data visualization, making report generation much easier.

R's frameworks allow building fantastic web applications. Because it divides complex procedures into multiple steps, this programming language makes building data models relatively easier.

R is widely used by academics and researchers in the field of data science. It offers a comprehensive collection of libraries and tools for the following:

  • Cleansing and preparing data
  • Creating visualizations
  • Training and evaluating machine learning and deep learning algorithms

You should be aware of their basic differences while selecting between Python and R for data science. which comprises:

Differences in Purpose

The primary distinction between these two languages is most likely their purpose. R's primary intent, as previously stated, is statistical analysis and data visualization. It heavily relies on statistical models and does not require several lines of code to demonstrate its analytics capabilities. This is also why it is so relevant to researchers, engineers, statisticians, and other professionals who do not have coding skills.

Python, on the other hand, is a more general-purpose language with a strong emphasis on production and implementation. Despite the fact that it requires computer programming skills, Python is relatively simple to learn due to its readable syntax. This language is primarily used by developers or programmers to perform data analysis and machine learning in production environments.

Furthermore, because Python can be incorporated at any stage of development, it provides the necessary adaptability to create new models from scratch.

Differences in Data Collection

With Python, you can get data right from the internet. You can attain this by leveraging the request library. You can use data from Wikipedia's tables through requests and beautiful soup. Python also supports data sources such as JSON and CSV files.

R, on the other hand, supports data import from Excel and CSV files. When it comes to grabbing data from the web, R is just not as straightforward as Python. It is not as efficient at web scraping as Python, but Rvest and Magritte help to mitigate this issue. They are similar to requests and beautiful soap.

Differences in Data Exploration

Python's Pandas, a data analysis library, allows you to locate data. Pandas is most likely Python's primary data analysis library. It organizes information into data frames. It enables users to easily filter, sort, and display data. Data frames are simple to clean. Pandas allow you to store a large amount of data and provide numerous features for efficiently displaying the data.

R is more powerful in data exploration because it was designed for it. R can be used to run statistical tests, create probability distributions, and perform data mining. R is an excellent programming language for optimization, signal processing, predictive analysis, and random number generation.

Differences in Data Visualization

The IPython Notebook and Matplotlib libraries are required for data visualization in Python. This library can generate graphs based on the data you provide.

If you want to create advanced graphs, you can use In terms of data visualization, R outperforms Python. It includes a number of packages that allow you to create visually appealing data visualizations. It includes a graphics module that allows you to generate basic plots for all modes allows. In R, you can also use ggplot2 to create more advanced plots.

Differences in Data modeling

Data modeling is the process of constructing models to determine how data will be stored in a database. On the one hand, Python provides multiple data modeling solutions based on the specific purpose of each data. As an example:

  • SciPy for scientific computing
  • NumPy for numerical modeling
  • SciKit-learn for machine learning algorithms

On the other hand, the R language might need to look for extra packages to conduct more specific modeling (e.g., Tidyverse). However, Base-R, the free software that comes with the R language, handles the fundamental data modeling.

Differences Data Manipulation

A variety of data manipulation strategies can be accessed through the use of a variety of libraries. For example, R users can use either the integrated information frame type or dplyr to aggregate data. The tidyr library (part of the Tidyverse package) is also a good R alternative for shape manipulation.

Python users, on the other hand, can use Pandas, a single library, to perform a variety of data manipulation tasks. Pandas is a popular open-source tool that is convenient for data analysis and data structure management.

Which one to learn?

Python is regarded as relatively simple to learn due to its easy-to-read syntax. It thrives in readability and simplicity, so the skill set is not particularly steep. Furthermore, it is a complete language that is ideal for beginner data science enthusiasts.

R, on the other hand, is easier to learn for those who are unfamiliar with coding. It allows users to instantly start analyzing data, but it can become complicated as it employs more advanced analytics and functionalities.

Moreover, R is widely used by data scientists as well as scientists from other fields (e.g., biology, physics, management, engineering, and so on) who want to instantly analyze data and create graphics with data from experimentations and other research. So, which one to learn depends hugely on an individual’s purpose of usage.

Despite these benefits, R has some drawbacks, including slow performance and a lack of web frameworks.


Python and R each have their own characteristics. So, which is better compared? It's entirely reliant on your level of skill and purpose. While R excels at visualization, Python thrives at scraping. R may be a better choice for the statistical background.

However, for those with a Computer Science background or who are just starting out, Python is the best option. Nevertheless, it is preferable to have a solid understanding of both which may be useful at times in a data science career.

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