R Programming Vs Python Programming – Brain Mentors Skip to content

R Programming Vs Python Programming

What do you want to become :

First, understand the difference between the jobs here. If you just want to become a Data Analyst with few concepts of machine learning as well, then you can just go with R Programming without any second thought. But if you want to become a full stack data scientist with deep knowledge of machine learning and deep learning and then how to deploy them or integrate your models into some web or desktop applications then go for Python programming.

In this blog we will learn :


  • R Programming Definition
  • Python Definition

  • Usability

  • Popularity Comparison

  • Jobs and Salary Comparison

  • Features Comparison

  • Libraries and Packages

  • Syntax Comparison

R is one of the most popular languages in data science. But we need to understand the usage and limitations of it. R programming is designed in such a way that even a non-programmer can easily understand it. R is a programming language as well as it is considered as a tool for data analysis. R is mainly designed to fulfill your statistical needs. It is primarily used in academics and research and it is the best tool/programming to perform EDA (Exploratory Data Analysis). It could be used in finance, marketing, media, etc.

Python has become one of the most popular languages in data science as well as in other domains like web development or security. Python is considered as a general-purpose language, it means you can use Python for multiple tasks like software development, web development, networking or security, and data science. Using Python programming you can explore a whole new level of data science. Machine Learning and Deep learning could be easily done using python.

Main Differences between Python and R

  • R is mostly used by statisticians
  • Python can be used by anyone
  • R focuses on data analysis and comes with the best visualization results
  • Python focuses on development and analysis both
  • R is unbeatable when it comes to the representation of data. Packages used in R programming makes your life much easier.
  • But if you want to write down your algorithms than just learn Python programming. It has packages also.


In terms of usability, both languages have their pros and cons. It always depends on the person to person or depends on the requirement. Then accordingly we decide which one we should use. Let’s see :

Python could be used in any domain like web development, software development, gaming, data science, etc.
R is primarily designed for statisticians. R is the perfect tool to perform Exploratory Data Analysis. Machine Learning is also implemented using R.
Python provides a lot of IDE and Editors to work with. Debugging gets easier using those IDE. For example Pycharm, Jupyter, Spyder, VSCode, Atom, Sublime, etc.
R does not have a lot of options in the case of IDE. Generally, we use Rstudio for implementation purposes.
You don’t need any coding experience to learn Python. Even a student of 3rd standard can learn python.
R is also easy to learn. There is no prerequisite to learn R Programming. Anyone from non-technical background can learn R.

Popularity Comparison

In terms of popularity, Python is more popular than R Programming.

Here we can see the graph of Python and R of the last 5 years. Python is too far from R Programming in terms of popularity. 

And now according to Github :

Python is in 2nd place if we compare it with any other programming language, but R is not even in the top 10.

Jobs and Salary Comparison

R Programming
Number of jobs (approx)
20,000 – 30,000
8,000 – 15,000
Average Salary (per year)
400k (Glassdoor)
289k – 900k (PayScale)
Job Titles
Developer, Data Scientist, Analyst, QA Engineer, Full Stack Developer, GIS Analyst, ML Engineer
Data Analyst, Business Analyst, Data Scientist, ML Engineer

Features Comparison

Dynamically Typed
Dynamically Typed
Multi-Paradigm (OOPS + Functional)
Multi-Paradigm (OOPS + Functional)
Supports web + desktop applications. Could be used as a server-side language
Cannot be used as a server-side language
Faster than R
Built for developers and data scientist
Only built for the data scientist
Has a rich set of libraries, but not more than R
More than 12000 packages available. Much better and easier packages than Python.
Suitable for data analysis, visualization, machine learning or deep learning
Only suitable for data analysis, visualization, and machine learning.
Has a lot of IDE and environments like Anaconda, Jupyter, Spyder, Pycharm
Has IDE like RStudio and also supports Jupyert Notebooks
Beginners can easily opt for Python. The Background doesn’t matter.
Anyone from any background can also learn R

Popular Libraries and Packages

Both R and Python has a rich set of libraries. A lot of hard work could be easily done by using these packages. Few packages are good in python but when it comes to data analysis and visualization then there is no competition of R Programming.

We will compare packages for a few tasks like :

  • How to load data in python and R?
  • How to perform Data Manipulation?
  • How to visualize data?
  • How to apply machine learning and deep learning?
R Programming
Load Data
csv, pandas, pymysql
RMySql, xlsx, haven
Data Manipulation
Scipy, Numpy, Pandas
Dplyr, tidyr, lubridate
Data Visualization
Matplotlib, Seaborn, Plotly
Machine Learning
Deep Learning
Keras, Tensorflow, Pytorch
H20, darch, MXNet, deepr
Statsmodel, statistics
Pre-defined methods, No packages required for stats

Syntax Comparison

When it comes to syntax comparison, both languages are quite similar in many ways. Let’s compare a few basic syntaxes :

Variable Assignment and basic calculation :

R Programming
image5 (1)

Basic For Loop

R Programming
image6 (1)

In python when we start a block like if..else or for loop or a function then there is no concept of using {} in python. Python uses indentation (4 spaces standard) from left side.

Import Packages

R Programming


So this was the basic introduction to Python and R Programming. Python and R both have their pros and cons. It a never-ending debate that which one is best. It’s always up to the requirement that what you have to achieve. So you cannot rely on any of the one languages. 

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