R vs Python

12 Oct, 2021
R programming Language and Python are both used extensively for Data Sciences. Both are very useful and open source languages as well. 
R Programming Language
Python Programming Language
Difference between R Programming and Python Programming
Ecosystem in R Programming and Python Programming
Advantages in R Programming and Python Programming
R and Python usages in Data Science
Example in R and Python
R 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 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.
Below are some major differences between R and Python:
Python supports a very large community to general-purpose in data science. One of the most basic use for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas and NumPy is one of those packages and makes importing and analyzing, and visualization data much easier.
R Programming has a rich ecosystem to use in standard machine learning and data mining techniques. It works in statistical analysis of large datasets, and it offers a number of different options for exploring data and It makes it easier to use probability distributions, apply different statistical tests. 
Advantage
Python and R programming language is most useful in data science and it deals with identifying, representing and extracting meaningful information from data sources to be used to perform some business logic with these languages. It has a popular package for Data collection, Data exploration, Data modeling, Data visualization, and statical analysis.
Program for the addition of two numbers
R
Python
# R program to add two numbersnumb1 <- 8numb2 <- 4 # Adding two numberssum <- numb1 + numb2 print(paste(The sum is", sum))
# Python program to add two numbers numb1 = 8numb2 = 4 # Adding two numberssum = numb1 + numb2 # Printing the resultprint("The sum is", sum)
Output: 
The sum is 12
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R Language
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