R Vs Python – Which One Is Best For Data Science?

Hello Everyone! Today, we will discuss the solution to the most common problem that all programmers, data science enthusiasts, and machine learning beginners face.

The problem is what to choose, Python programming language or R programming Language. And where to use these languages? Ready carefully till the end to get all doubts cleared.

R vs Python

R vs Python

Choosing R vs Python:-

It’s up to the individual data scientist or data analyst or machine learning engineer or programmer to choose the language that best fits their unique needs. The following questions may help with that decision.

1. Which language do your colleagues use?

– The benefits of being able to share code with your colleagues and maintaining a simpler software stack outweigh any benefits of one language over another.

2. What are the net costs of learning a language?

– It will take time to learn a new system that is better aligned for the problem you want to solve, but staying with the system you know may not be a fit for that problem.

3. What are the commonly used tool(s) in your field?

Who It’s used by?

Python: Python is used by programmers who want to delve into data analysis or apply statistical techniques, and by developers and programmers who turn to data science.

R: R has been used primarily in academics and research and is great for exploratory data analysis. In recent years, enterprise usage has rapidly expanded.

Python: Python is a production-ready language, meaning it has the capacity to be a single tool that integrates with every part of your workflow.

R: This language is used by Statisticians, Engineers, and Scientists without computer programming skills. It’s popular in academia, finance, pharmaceuticals, media, and marketing.

Usability of both languages:-

Python Usability:

  • People with a software engineering background may find Python comes more naturally to them than R.
  • Coding and debugging are easy because of the simple syntax.
  • The indentation of code affects its meaning.
  • Any piece of functionality is always written the same way with Python.

R Usability:

  • If you have no coding experience, then R may be easier to learn.
  • Statistical models can be written with only a few lines.
  • The same piece of functionality can be written in several ways with R.


  • Either language is suitable for almost any data science task, from data manipulation and automation to ad-hoc analysis and exploring datasets.
  • Users may leverage both languages for different purposes, e.g., conducting early-stage data analysis & exploration in R, then switching to Python when it’s time to ship some data products.

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Python Ecosystem:

  • Python has a robust ecosystem and is commonly considered one of the easier programming languages to read and learn. Its programming syntax is simple and its commands mimic the English language.

E.g. print(“Hello world!”)

  • Python code is syntactically clear and elegant, easily interpretable, and easy to type.
  • It’s great for building data science pipelines and machine learning products integrated with web frameworks at scale. But watch out for dependencies and installing Python libraries!
  • It’s great for building data science pipelines and machine learning products integrated with web frameworks at scale. But watch out for dependencies and installing Python libraries!
  • The Python Package Index (PyPi) and Anaconda are repositories of Python software with all libraries. Users can contribute to these repositories, but it’s a bit complicated in practice to do so.

R Ecosystem:

  • R has a rich ecosystem of cutting-edge interface packages available to communicate between open-source languages.
  • This allows users to string their workflows together, which is especially useful for data analysis.


  • Python: Python is flexible for creating something that has never been done before. Developers can also use it for scripting websites or other applications.
  • R: It’s easy to use complex functions in R. All kinds of statistical tests and models are readily available and easily used.

Ease of Learning:-

  • Python’s focus on readability and simplicity means its learning curve is relatively linear and smooth.
  • R is easier to learn when you start out, but the intricacies of advanced functionalities make it more difficult to develop expertise.
  • Python is considered a good language for beginner programmers.
  • R is not hard for experienced programmers to learn.

Advantages of R vs Python:-

Advantages of Python programming are as follows

  • Python programming language has gained popularity for its code readability, speed, and many functionalities.
  • It is great for mathematical computation and learning how algorithms work.
  • It has high ease of deployment and reproducibility.

Advantages of R Programming language are as follows

  • R language is widely considered the best tool for making beautiful graphs and visualizations.
  • It has many functionalities for data analysis.
  • R is great for statistical analysis.
  • Built around a command line, but the majority of R users work inside of RStudio, an environment that includes a data editor, debugging support, and a window to hold graphics as well.

Disadvantages R vs Python:-

Disadvantages of Python language are as follows

  • Python doesn’t have as many libraries as R, and there are no module replacements for the hundreds of essential R packages.
  • Python requires rigorous testing as errors show up in runtime.
  • Visualizations are more convoluted in Python than in R, and results are not as eye-pleasing or informative.

Disadvantages of the R Language are as follows

  • For people with no software engineering experience, base R can be more difficult to learn because it was developed by statisticians, not to make coding easier. But R has a set of packages known as the Tidyverse, which provides powerful yet easy-to-learn tools for importing, manipulating, visualizing, and reporting on data.
  • Finding the right packages to use in R may be time-consuming.
  • There are many dependencies between R libraries.
  • R can be considered slow if the code is written poorly.
  • This language is not as popular as Python for deep learning and NLP.

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Use Case: Data Analysis:-

Data Analysis with Python

  • Python is generally used when data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database.
  • Since it’s a full-fledged programming language, Python is a good tool to implement algorithms for use in production.

Data Analysis with R

  • R is mainly used when data analysis tasks require standalone computing or analysis on individual servers.
  • For exploratory work, R is easier for beginners. Statistical models can be written with a few lines of code.

Data Handling Capabilities:-

Data Handling Capabilities of Python

  • Python requires users to install packages for data analysis, and these packages have greatly improved in recent years.
  • NumPy and pandas, among others, are popular for data analysis.

Data Handling Capabilities of R language

  • R is great for data analysis because of its huge number of packages, readily usable tests, and the advantage of using formulas.
  • It can handle basic data analysis without needing to install packages. Big datasets require the use of packages such as data.table and dplyr.

IDEs of both languages:-

Python IDEs:

  • There are many Python IDEs to choose from which drastically reduce the overhead of organizing code, output, and notes files. Jupyter Notebooks and Spyder are popular, and Jupyter Lab is gaining traction. Tip: Also try Rodeo, the data science IDE for Python.”


  • RStudio is the most popular R IDE. Its available in two formats: RStudio Desktop for running locally as a regular desktop application and RStudio Server for access via a web browser while running on a remote Linux server.

For Python programming language, Popular Libraries and Packages are

a. Pandas – To easily manipulate data
b. SciPy and NumPy for scientific computing
c. Scikit-learn for machine learning
d. Matplotlib and seaborn to make graphics
e. Stats models to explore data, estimate statistical models, and perform statistical tests and unit tests

For R programming language, Popular Libraries and Packages are

a. Dplyr, tidyr, and data.table to easily manipulate data
b. Stringr to manipulate strings
c. Zoo to work with regular and irregular time series
d. ggplot2 to visualize data
e. caret for machine learning

So, that’s all about r vs python. I think now your doubts will be cleared.

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