R Vs Python: Which One Is Dominant for Machine Learning and Data Science?

In the world of data science, R and Python are two of the most popular languages. Both languages have their strengths and weaknesses, making them useful for different types of tasks. There is no single best language for machine learning and data science, however there are better choices based on individual needs and preferences. This article will provide an overview of the two languages and how they can be used in Machine Learning & Data Science together and separately.

Both R and Python excel in different areas; R is a scripting language that specializes in statistical analysis while Python has a larger set of libraries designed for general-purpose programming. R’s packages are abundant when it comes to statistical analysis and it can perform complex mathematical procedures with ease. On the other hand, Python has robust library support for scientific computing, web development, GUI development as well as large-scale software engineering projects.

While both languages have many advantages, there are also some downsides that should be discussed: R’s strength lies in Statistical Analysis but its syntax is quite verbose while Python is concise but lacks options when it comes to Statistics functions; thus making certain operations more difficult to run in Python than in R. Additionally, support options and capabilities vary depending on the language being used which could potentially limit your project scope when using one or the other exclusively.

When considering these factors carefully it becomes clear that using both languages in conjunction could be beneficial as each language offers something unique to the table allowing for greater flexibility when constructing data science pipelines or tackling sophisticated problems that require more than one resource to solve satisfactorily.


R Vs Python: Which One Is Dominant for Machine Learning and Data Science?

R Vs Python: Which One Is Dominant for Machine Learning and Data Science? 1



R is a programming language that has been around for over 25 years and is one of the most popular languages for data science and machine learning. It is a great language for performing statistical analysis and can also be used for manipulating data.

R has various features that make it well-suited for data analysis and machine learning, such as a wide range of libraries, an easy-to-use syntax, and an active community.

Let’s look at some of the benefits of using R for machine learning and data science.



R and Python are both popular programming languages for machine learning and data science. Each has their own advantages, and the choice between them depends on a variety of factors such as what kind of project you are working on, your level of experience with coding, the types of data sources you are using, and which platform you prefer.

When deciding between R and Python, one barrier may be the lack of familiarity a programmer has with either language. Experienced coders will find that learning either language is straightforward as they share many similarities in terms of structure, syntax and functionality.

R is considered to be the dominant language for statistics due to its specialized tools; most notably its wide range of packages for statistical modeling. It has a rich set of libraries that can help scientists analyze large datasets quickly— from visualizing data to fitting sophisticated models— helping uncover patterns, correlations, trends and anomalies both easily and accurately. Additionally R Studio provides an integrated software suite that supports the entire workflow from data import through analysis to visualization.

Python has a similarly large number of libraries making it advantageous for tasks such as web scraping or reading API feeds into powerful analytics database engines like Hadoop or Apache Spark which makes Python a general-purpose language well suited to projects requiring complex integration with other applications. In addition Python code is often cleaner than equivalent code written in R which means debugging can be much more straightforward as well compared to other languages like Java or C++ due to its readable syntax. For example typing two lines will produce an output in Python while it can take several lines in some other scripting languages like Matlab or Java Script (JS). As a result overall time spent developing programs in machine learning or data science using Python can be far quicker when compared to some other programming languages thereby offering great cost-benefit especially in larger projects where time equals money!



R and Python both have strengths and weaknesses when it comes to data science and machine learning. R is primarily a statistical programming language, so it is better suited for specific types of data analysis tasks; however, its robust library of packages and ease of use make it a powerful tool for exploratory data analysis. Python, on the other hand, is a general-purpose programming language with specific libraries dedicated to machine-learning tasks. While powerful and versatile, Python can require more code to achieve certain tasks than R; consequently, there is an increased potential for errors or bugs while coding.

When assessing the disadvantages of each language, it’s important to consider the time investment required to learn each one. Becoming proficient in either language requires significant time spent coding as well as using pre-defined packages and libraries. Additionally, R’s parsing algorithm tends to be slower than that of Python because each line of code is translated into several internal steps before being executed, which can limit speed. Finally, some functionality may not be available within one platform or the other (e.g., deep learning). Ultimately when deciding between R vs Python for machine learning projects takes careful consideration in your goals and you should weigh out possible pros and cons prior making your decision.



Python has become one of the most popular programming languages for machine learning and data science. It is simple to understand and use, and it has a wide range of libraries and frameworks to help you develop powerful algorithms.

Let us explore why Python is so popular for machine learning and data science and what it can do for you.



Python is becoming increasingly popular in the field of machine learning due to its easy-to-learn, high-level programming language. It has a wide range of libraries and packages that make coding simple and efficient. With Python, data scientists don’t need to worry about learning the complexities of lower level programming languages. The result: you can quickly create complex algorithms and iterate faster than with any other language.

Python also has an advantage when it comes to code readability, allowing for faster debugging cycles than R. Moreover, Python is comparatively easier to read compared to R as it has a consistent syntax that allows for more concise code. There are also plenty of community tools you can use or create in Python that make development faster and easier.

In addition, Python offers numerous advantages over R when it comes to data analysis tasks such as time series analysis, natural language processing (NLP), text analytics and predictive modeling. This makes it better suited for developing AI applications like intelligent chatbots or market forecasting systems than R. Furthermore, its ability to run on multiple servers makes scaling big data processing much easier compared with R’s single server approach.

Overall, both languages have their pros and cons but for machine learning and data science projects there are good reasons why Python is so dominant over R.



Many times, data scientists and machine learning developers consider Python to be superior to R in terms of user-friendliness, deep learning capabilities and statistical libraries. However, it is important to note that there are certain disadvantages for using Python for deep learning or machine learning projects related to data science.

First and foremost, Python does not offer any native way of dealing with missing data points or variables. Secondly, R offers far more specialized libraries than those available in Python; this makes it easier to perform specific functions in R. Thirdly, the plotting package in Python is generally agreed upon as being subpar compared to what’s available in R. Finally, while there are many more tutorials and resources available on the Internet involving issues around Python and data science than there are for R; this may be also attributed to the fact that there is a larger installed user base.



When it comes to choosing a programming language for building Machine Learning or Deep Learning models, both R and Python are common contenders. R is a statistical programming language oriented towards data visualization, while Python is a general-purpose programming language and has a wide range of libraries and packages for building Machine Learning models.

Let’s take a look at how these two languages compare in terms of their suitability for Machine Learning and Data Science.



R and Python both have their own strengths and weaknesses when it comes to machine learning and data science. Given that, the popularity of either language depends on the specific needs and preferences of the user.

When comparing overall popularity, Python has been steadily gaining more worldwide use compared to R in the past few years. The TIOBE index which tracks programming language popularity rates Python at 4th while R stands at 11th, and StackOverflow’s 2020 survey puts Python in 1st place while R is 7th. In addition, many universities now teach both languages, but Python is getting more attention due to its wider use cases outside of data analysis and machine learning.

However, within the data science community specifically, R remains relatively popular as opposed to other programming languages. Many leading companies such as Uber and Microsoft employ large teams of statisticians utilizing R for their deep analytics work. According to Simply Statistics “Python is increasing in popularity within academia but it has not yet overtaken R” .

In conclusion, both languages are great options given that they are extended with numerous libraries and packages available for free download online; therefore allowing users to access powerful tools with detailed statistical capabilities for powerful analyses. Ultimately, each user can make a comparative assessment based on their own specific individual needs when deciding which language will dominate their projects’ demands.


Community Support

Community support is an important factor in making an informed decision while selecting a programming language. In the comparison between R and python, both have their own distinct active communities.

Python has an extensive user base on Stack Overflow and Reddit forums that allow developers to acquire answers quickly. As per the latest Stack Overflow Developer Survey, Python is the fastest growing programming language worldwide with around 56.4% of developers using it currently. Similarly, Github’s Octoverse report 2020 indicates that Python tops the list of most used languages on its platform with over 56 million Pull requests completed in 2020 alone.

R also has a large and vibrant community which continues to grow rapidly each year with plenty of useful resources available for new hackers like tutorials and courses for getting started with R programming language on various online platforms. One such resource is “Coursera” which lists approx 200+ courses related to R only as compared to around 700+ courses related to Python . Hundreds of packages are shared by expert users every month along with plenty of Q&A forum like StackOverflow and Quora where users can ask questions about any problems they have encountered while creating an R project.


Learning Curve

When it comes to the learning curve, both languages have slight differences. R is an interpreted language, meaning users type commands and functions directly into their console and see immediate feedback without having to compile code. As a result, users can spend more time experimenting (but also debugging) with their code as the interpreter provides output line by line. Conversely, Python is a compiled language and so users have to write their code first before running it, which requires more attention to detail but can help with runtime optimization.

Ultimately, depending on the user’s background and experience with either language, both can take anywhere from a few days to weeks or even months to build up proficiency in them. What helps is that compared to other programming languages used for machine learning like Java or C++, R and Python come with intuitive syntaxes that make the learning process easier and faster. Also, the relative availability of comprehensive libraries predefined functions in R and Python can dramatically reduce development time by eliminating much of the overhead related to writing custom functions from scratch.



When it comes to syntax, there are several differences between R and Python. Python is designed with a cleaner, more consistent syntax that many programmers find more intuitive than R’s, which can have unpredictable results. As a result, it is much easier for those with a little coding knowledge to quickly write and execute code with Python. R’s syntax has much less of a learning curve but relies heavily on brackets and semi-colons that are less predictable than Python’s syntax.

In addition to the structure of their syntax, the language also has some major differences in its built-in programming elements. For example, Python offers tools such as NumPy (for numerical operations), SciPy (for scientific computing) and Matplotlib (for plotting) that make it ideal for data science tasks. In contrast, R offers packages like Tidyverse for data wrangling or AI in action for machine learning tasks — both of which provide advantages in specific disciplines.

Finally, there are some big differences in how you interact with each language as well. With Python you can interact directly from the interpreter or write your code in an external editor such as Visual Studio Code or Sublime Text. On the other hand, R uses an interactive command line interface that allows users to see their output as well as modify variables directly from the environment without having to use any external tools or editors.



When it comes to libraries, Python is the clear winner. While R has some libraries designed for data science and machine learning, such as the caret package and ggplot2 for data visualization, the sheer number of useful libraries available to a Python user dwarfs what is available for R. For example, scipy (a collection of scientific computing algorithms) and numpy (focusing on efficient numerical computations) are two of the most important packages available in Python; in comparison, R’s base installation comes with very few data analysis tools.

By integrating many different types of information and providing a range of powerful features, Python offers easier access to more packages than does R.scikit-learn is a tool for machine learning featuring regression algorithms (e.g., linear regression) and support vector machines; TensorFlow focuses on deep learning; Keras performs rapid experimentation with neural networks; and PySpark provides large-scale distributed data processing using core Spark concepts. For artificial intelligence (AI) applications such as natural language processing, NLPython’s NLTK suite has been shown to be superior to that of R’s Natural Language Toolkit in terms of both speed and accuracy.

Overall, the diverse array of expertise-level libraries available for Python enables data scientists to focus on tasks more likely to propel their projects forward rather than spend time rewriting basic code for APIs made exclusively with R.



When it comes to speed, R is generally slower than Python as it is an interpreted language. This means that the code written in R has to be interpreted at runtime line by line. On the other hand, Python is a compiled language, i.e., it uses a compiler before any code runs which makes it faster compared to R.

Python also provides support for libraries such as NumPy and Pandas which helps in faster computation than one could perform with R due to its lower level of abstraction and presence of vectorization. This vectorization helps significantly with numeric computations, especially matrix operations. The parallelized operations also make this language more powerful for machine learning (ML) algorithms as compared with R which can only be done serially.

Apart from being able to use libraries like scikit-learn for ML tasks, python also has various packages optimized for Apache Spark deployments over multiple nodes or distributed computing that makes bigger datasets easier to process than they would be in R while using the same hardware resources.



Both R and Python have advantages and disadvantages when it comes to machine learning and data science. Ultimately, it’s up to you decide which programming language is right for you based on your individual skillset and needs.

It can be argued that for advanced users, R provides more powerful tools for data analysis, whereas Python does a better job of creating web applications through advanced frameworks like Django or Flask.

When choosing between the two languages, think about what your end goals are and the types of projects you’ll be working on. This will dictate which language you should learn. If you’re looking to build a web application then consider starting with Python while if you plan on doing statistical analysis then start with R.

Both languages offer a vast array of packages that encompass data manipulation and predictive modeling. However, where the difference lies between them is in terms of coding style: Python is an object-oriented language allowing for object-oriented programming (OOP), whereas R relies heavily on functional programming (FP) techniques such as functional filters/maps/anonymous functions to manipulate datasets. Ultimately, it comes down to personal preference in terms of which coding style works best for a project or data task at hand.

Leave a Reply