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R V/s Python

R Programming Language: R Programming Language is used for machine learning algorithms, linear regression, time series, statistical inference, etc. It was designed by Ross Ihaka and Robert Gentleman in 1993. R is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. Also, the R programming language is the latest cutting-edge tool.

Python Programming Language: Python is a widely-used general-purpose, high-level programming language. It was created by Guido van Rossum in 1991 and further developed by the Python Software Foundation. It was designed with an emphasis on code readability, and its syntax allows programmers to express their concepts in fewer lines of code.

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Difference between R Programming and Python Programming

Feature	R	Python
Introduction	R is a language and environment for statistical programming which includes statistical computing and graphics.	Python is a general-purpose programming language for data analysis and scientific computing
Objective	It has many features which are useful for statistical analysis and representation.	It can be used to develop GUI applications and web applications as well as with embedded systems
Workability	It has many easy-to-use packages for performing tasks	It can easily perform matrix computation as well as optimization

Integrated development environment	Various popular R IDEs are Rstudio, RKward, R commander, etc.	Various popular Python IDEs are Spyder, Eclipse+Pydev, Atom, etc.
Libraries and packages	There are many packages and libraries like ggplot2, caret, etc.	Some essential packages and libraries are Pandas, Numpy, Scipy, etc.
Scope	It is mainly used for complex data analysis in data science.	It takes a more streamlined approach for data science projects.

Features	R	Python
Data collection	It is used for data analysts to import data from Excel, CSV, and text files.	It is used in all kinds of data formats including SQL tables
Data exploration	It optimized for the statistical analysis of large datasets	You can explore data with Pandas
Data modeling	It supports Tidyverse and it became easy to import, manipulate, visualize, and report on data	Use can you NumPy, SciPy, scikit-learn, TansorFlow
Data visualization	You can use ggplot2 and ggplot tools to plots complex scatter plots with regression lines.	You can use Matplotlib, Pandas, Seaborn

Example in R and Python�Program for the addition of two numbers

# Python program to add two numbers

numb1 = 8

numb2 = 4

# Adding two numbers

sum = numb1 + numb2

# Printing the result

print("The sum is", sum)

# R program to add two numbers

numb1 <- 8

numb2 <- 4

# Adding two numbers

sum <- numb1 + numb2

print(paste("The sum is", sum))