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R vs Python

Last Updated : 20 Dec, 2023
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R Programming Language and Python are both used extensively for Data Science. Both are very useful and open-source languages as well. For data analysis, statistical computing, and machine learning Both languages are strong tools with sizable communities and huge libraries for data science jobs. A theoretical comparison between R and Python is provided below:

R-vs-python

R vs Python

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 and disadvantages in R Programming and Python Programming
  • R and Python usages in Data Science
  • Example in R and 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.

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 of general-purpose data science. One of the most basic uses for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas and NumPy are one of those packages that make importing and analyzing, and visualization of 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. 

R-vs-Python

R vs Python

Features 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

Statistical Analysis and Machine Learning In R and Python

Statistical analysis and machine learning are critical components of data science, involving the application of statistical methods, models, and techniques to extract insights, identify patterns, and draw meaningful conclusions from data. Both R and Python have widely used programming languages for statistical analysis, each offering a variety of libraries and packages to perform diverse statistical and machine learning tasks. Some comparison of statistical analysis and modeling capabilities in R and Python.

Capability

R

Python

Basic Statistics

Built-in functions (mean, median, etc.)

NumPy (mean, median, etc.)

Linear Regression

lm() function and Formulas

Statsmodels (OLS)

Ordinary Least Squares (OLS) Method

Generalized Linear Models (GLM)

glm() function

Statsmodels (GLM)

Time Series Analysis

Time Series packages (forecast)

Statsmodels (Time Series)

ANOVA and t-tests

Built-in functions (aov, t.test)

SciPy (ANOVA, t-tests)

Hypothesis Tests

Built-in functions (wilcox.test, etc.)

SciPy (Mann-Whitney, Kruskal-Wallis)

Principal Component Analysis (PCA)

princomp() function

scikit-learn (PCA)

Clustering (K-Means, Hierarchical)

kmeans(), hclust()

scikit-learn (KMeans, AgglomerativeClustering)

Decision Trees

rpart() function

scikit-learn (DecisionTreeClassifier)

Random Forest

randomForest() function

scikit-learn (RandomForestClassifier)

Advantages in R Programming and Python Programming

R Programming Python Programming
It supports a large dataset for statistical analysis General-purpose programming to use data analyze
Primary users are Scholar and R&D Primary users are Programmers and developers
Support packages 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

Disadvantages in R Programming and Python Programming

R Programming

Python Programming

R is much more difficult as compared to Python because it mainly uses for statistics purposes.

Python does not have too many libraries for data science as compared to R.

R might not be as fast as languages like Python, especially for computationally intensive tasks and large-scale data processing.

Python might not be as specialized for statistics and data analysis as R. Some statistical functions and visualization capabilities might be more streamlined in R.

Memory management in R might not be as efficient as in some other languages, which can lead to performance issues and memory-related errors

Python visualization capabilities might not be as polished and streamlined as those offered by R’s ggplot2.

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

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)


R




# R program to add two numbers
numb1 <- 8
numb2 <- 4
 
# Adding two numbers
sum <- numb1 + numb2
 
print(paste("The sum is", sum))


Output

The sum is 12


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