R vs Python: What's the Difference?

Posted in /   /   /  

R vs Python: What's the Difference?

Vinay Khatri
Last updated on December 11, 2022

    When it comes to the best programming languages for data science, we have two top contenders that are fighting head to head ‘Python’ and ‘R’. Both are open-source programming languages and serve the cause of data science and analytical models.

    While R could be a new programming language for many computer science students, Python is a widely-known programming language that is suitable for data science. However, let me tell you that Python is not the only programming language that works well with AI, machine learning, and data science.

    Nonetheless, for data science, most professionals prefer working with Python and R languages. However, beginners often find it difficult to decide if they should learn Python or R to get started with their career in data science.

    Well, in this article, we have drawn a detailed comparison between R and Python programming languages. Also, data science and data analytics would be the focal point for the R vs Python comparison. But before we get started with the comparison, let’s have a brief introduction to each programming language.

    What is R?

    R is a programming language employed widely for statistical computing and graphics. Data miners and statisticians primarily use the R language for data analysis and creating statistical software. It is analogous to the S language and comes with several statistical and graphical techniques.

    More interestingly, R is also a free and open-source software environment that runs on Windows, macOS, and Linux platforms. It is a collection of tools that facilitate data manipulation and calculation. The major tools the R environment provides are for carrying out the following tasks:

    • Effective data handling
    • Calculations on arrays
    • Data analysis

    Initially, R was used for academics and research purposes. However, as enterprises required a tool that could help them to handle huge amounts of data, R emerged to be the best option. Also, R comes with a large number of packages that make it quite easy for data scientists to process the data efficiently.


    In 1995, Ross Ihaka and Robert Gentleman created an open-source programming language and named it R, which is an implementation of the S programming language. The goal behind the creation of R was to develop a new programming language that would be ideal for statistics, data analytics, and graphical models. The name of the language was named after the initials of the developers' first name.


    Let us now throw light on the features of the R language and the R environment.

    R Language Features

    • Basic Statistics: R facilitates the computation of 'Measures of Central Tendency. There are three measures of central tendency, namely mean, mode, and median, which are the fundamental statistics terms.
    • Probability Distribution: R makes it easy to manage various sorts of probability distributions, such as Normal Distribution, Binomial Distribution, Chi-squared Distribution, and many more.
    • Static Graphics: R is replete with features that encourage the development of static graphics. It entails functionality for creating various types of plots, including maps, mosaic plots, etc.
    • Data Analysis: You will find a plethora of tools in R for data analysis.

    R Software Features

    • R Packages: The R software environment comes with an exhaustive repository of 10,000 packages called Comprehensive R Archive Network (CRAN).
    • Distributed Computing: R provides two new packages, namely ddR and multidplyr, for distributed computing. Distributed computing is a model where a software system shares its components across multiple computers to improve its efficiency.


    The following are the advantages of R:

    • Free and Open-Source: R is a free and open-source language and software environment for statistical analysis.
    • Cross-Platform: The R software environment is compatible with multiple platforms, including Windows, macOS, and Linux.
    • Machine Learning Operations: The comprehensive repository of R packages includes packages for machine learning and data analysis.
    • Data Wrangling: R enables you to perform data wrangling using the packages: dplyr and readr.
    • Supports Various Data Types: With R, you can carry out operations on a variety of data types, including arrays, matrices, and vectors.
    • Active Community: R has an active community of developers across the globe who are always ready to contribute their skills to the community.


    Here are some major drawbacks of R:

    • Steep Learning Curve: The syntax of R is completely different than other programming languages. Hence, you may find learning R difficult in the beginning.
    • Slow Speed: R is slower than its counterparts, such as MATLAB and Python. The reason is that it has its functions spread across various packages of CRAN.
    • Poor Memory Management: Due to poor memory management, R can consume all the available space on the system.
    • Low Security: R is not as secure as other languages. It lacks security features.
    • Big Data: R is not suitable for managing big data as it becomes extremely tedious to do so.

    What is Python?

    It is a general-purpose and object-oriented programming language that is suitable to use in a variety of fields, including web development , AI development, and data science. The language's built-in data structure, dynamic typing, and a vast collection of libraries make it a popular language for the development of applications, data analysis, data visualization, and task automation.

    Python is also one of the most preferred languages among data scientists as it offers functionality to deal with statistics, mathematics, and scientific functions. Like R, it can perform various data science operations using libraries like NumPy and SciPy. It even has libraries like matplotlib, which is capable of visualizing graphs.

    The language provides us with simple syntax and amazing libraries so we can run complex data science algorithms with ease. Though Python does not contain as many statistics packages as R, each update for Python is intended to make it more powerful and feature-rich.


    Guido Van Rossum, in 1991, released Python 0.9.0, the first version of Python. He released it as a successor to the ABC language. Later, in 2000, he released Python 2.0, which was a more improved version. It included new features, such as list comprehensions, garbage collection, and reference counting.

    The next version of Python, Python 3.0, was a major release with many new features. The latest and stable version of Python is Python 3.10 as of August 2022.


    • Object-Oriented: Being an object-oriented language, it supports all OOPs concepts, such as classes, objects, inheritance, encapsulation, abstraction, and polymorphism.
    • Interpreted Language: Python is an interpreted language, i.e., it reads and executes a program line by line and stops the execution if there is an error in the code.
    • High-Level Language: When you write Python programs, you do not have to worry about memory management and system architecture. Python manages it all for us.
    • Extensible: It is an extensible language. You can embed Python code in C and C++ programs. Also, you can compile the Python code along with C and C++ programs.
    • Standard Library: Python's standard library is so vast that it provides several modules and functions for a variety of tasks. Hence, it is called the 'batteries included' language.
    • Dynamically Typed: When declaring Python variables, there is no need to define their data types. The Python interpreter automatically assigns data type at runtime based on the value of variables.


    • Python is the most simple and easy-to-to understand and implement language.
    • As Python allows developers to focus on developing business logic with its simple syntax, it is a productive language.
    • It is a free and open-source language, and hence, anyone can download it easily.
    • Python is a versatile language used for developing a variety of applications.
    • The code you write once in Python on a specific platform, you can run it on other platforms without making any changes.


    • As it is an interpreted dynamically-typed language, Python programs are slow in execution than other languages.
    • It is not ideal for developing mobile applications.
    • Python consumes a large amount of memory. So, it is not suitable for building applications that prioritize memory optimization.
    • The database access layers of Python are underdeveloped when compared to JDBC and ODBC. So, it is not good in database connectivity.

    R vs Python: Head-to-Head Comparison

    The following table describes the key differences between R and Python:

    R Python
    Programming Type
    R is a multi-paradigm programming language. Python is a multi-paradigm: object-oriented programming language.
    Suitable For
    Data Science and analytics Software development and production, web development, data science, AI & ML development.


    Mostly data scientists and analysts. Programmers and developers.

    Learning Curve

    R has a steep learning curve; thus, it is difficult to learn. Python has a gradual learning curve; thus, it is easy to learn.

    Libraries and Packages

    It contains a large number of libraries. Libraries are Python assets.

    Data Science Libraries

    It contains more data science libraries as compared to Python. Python has many libraries for data analytics and statistics.


    As R is limited to data science and analytics, it is not that popular Python is useful in many fields, which makes it more popular than R.

    Average Salary

    $99,000; vary according to experience and skills $100,000; depends upon developer skills and experience

    Storage Handling

    R is capable of handling huge amounts of data. Python can also handle huge amounts of data.


    When it comes to data analysis, R provides better performance than Python Python lags behind R when it comes to performing data analysis quickly and efficiently.

    Famous Data Science Libraries

    • Tydiverse
    • ggplot2
    • caret
    • zoo
    • Pandas
    • Scipy
    • scikit-learn
    • TensorFlow
    • caret


    • More packages for data analysis and statistics.
    • Huge community
    • Data experts' first choice
    • Better visualization of graphs
    • Easy to learn
    • Its clear and indented syntax makes it easy to read and understand the Python code.
    • It allows the implementation of complex algorithms.
    • Supports object-oriented programming


    • Hard to learn
    • Slow performance
    • Limited libraries for data analysis and statistics compared to R.

    R vs Python - Which One To Choose?

    Both the programming languages, R and Python, have their own set of features, pros, and cons. While Python is a general-purpose programming language, R is specially designed for statistical computing and graphics. Python is a good-to-go language for web and desktop applications development, task automation, data analysis, and data visualization. Meanwhile, R is ideal for statistics, data science, and analytics.

    Therefore, the choice between Python and R entirely depends on your project's requirements. Choose R for statistical learning as its offers unmatched libraries for data exploration. On the other hand, go with Python if you wish to build machine learning models and large-scale applications.


    There are data science experts who use both Python and R programming languages for data science. However, many developers stick with one programming language, and that’s why most of them choose Python over R because it provides more flexibility. By learning Python, an individual will be not only able to work in the field of data science but also in other fields, such as software development and web development.

    However, developers with a keen interest in data analysis and statistics always suggest choosing R because of its packages. Apparently, it is the choice of an individual whether to go with Python or R programming language.

    We hope that this Python vs R article has helped you understand all the crucial differences between the two so that you can easily choose the one that seems the best as per your requirements.

    People are also reading:


    If your only requirement is data analysis and visualization, R is a perfect choice. Python is a versatile language and can be employed for a variety of purposes, including data analysis, task automation, software development, and web development.

    Yes, Python is easier to learn than R because of its simple syntax that eliminates the need for delimiters, like curly brackets and semicolons. Meanwhile, R has a completely different syntax than other programming languages and hence, has a steep learning curve.

    Yes, it is possible for Python to do everything R can do and vice-versa.

    Yes, it is absolutely worth learning Python as it is a versatile language and ranks top in the list of popular programming languages. When you learn Python, you open up yourself to a variety of job opportunities.

    Yes, it is worth learning R since it is the most common language among data scientists. It is a language used for working with data, and as data is the new oil, learning R would be definitely beneficial.

    Leave a Comment on this Post