R vs Python

R programming Language and Python are both used extensively for Data Sciences. Both are very useful and open source languages as well. 

In this article we will cover the following topics:

• 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 Programming Language

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 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.

Difference between R Programming and Python Programming

Below are some major differences between R and Python:

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.

Ecosystem in R Programming and Python Programming

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. 

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 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
Data visualization	You can use ggplot2 and ggplot tools to plots complex scatter plots with regression lines.	You can use Matplotlib, Pandas, Seaborn

Advantages in R Programming and Python Programming

Advantage
R Programming	Python Programming
It supports a large dataset for statistical analysis	General-purpose programming to use data analyse
Primary users are Scholar and R&D	Primary users are Programmers and developers
Support package like tidyverse, ggplot2, caret, zoo	Support packages like pandas, scipy, scikit-learn, TensorFlow, caret
Support RStudio and It has a wide range of statistics and general data analysis and visualization capabilities.	Support Conda environment with Spyder, Ipython Notebook

R and Python usages in Data Science

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.

Example in R and Python

Program for the addition of two numbers

R

# R program to add two numbers
numb1 <- 8
numb2 <- 4

# Adding two numbers
sum <- numb1 + numb2

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

Python

# Python program to add two numbers

numb1 = 8
numb2 = 4

# Adding two numbers
sum = numb1 + numb2

# Printing the result
print("The sum is", sum)

Output

The sum is 12