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R vs Python

  • Last Updated : 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. 

In this article we will cover the following topics:

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:

FeatureRPython
IntroductionR 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
ObjectiveIt 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
WorkabilityIt has many easy to use packages for performing tasksIt can easily perform matrix computation as well as optimization
Integrated development environmentVarious popular R IDEs are Rstudio, RKward, R commander, etc.Various popular Python IDEs are Spyder, Eclipse+Pydev, Atom, etc.
Libraries and packagesThere are many packages and libraries like ggplot2, caret, etc.Some essential packages and libraries are Pandas, Numpy, Scipy, etc.
ScopeIt 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. 

FeaturesPython
Data collectionIt 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 explorationIt optimized for statistical analysis of large datasetsYou can explore data with Pandas
Data modelingIt supports Tidyverse and it became easy to import, manipulate, visualize and report on dataUse can you NumPy, SciPy, scikit-learn
Data visualizationYou 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 ProgrammingPython Programming
It supports a large dataset for statistical analysisGeneral-purpose programming to use data analyse
Primary users are Scholar and R&DPrimary users are Programmers and developers
Support package like tidyverse, ggplot2, caret, zooSupport 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



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