R vs Python - GeeksforGeeks (2024)

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

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.

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

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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FAQs

R vs Python - GeeksforGeeks? ›

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.

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Which is better, R or Python? ›

Python is much more straightforward, using syntax closer to written English to execute commands. However, R makes it easier to visualize and manipulate data if you have other languages under your belt. It's statistics-based, so the syntax here is more straightforward for analysis.

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What are the disadvantages of Python vs R? ›

Disadvantages of Python

Python performs poorly in statistical analysis compared to R due to a lack of statistical packages. Sometimes developers may face runtime errors due to the dynamically typed nature. The flexible data type in Python consumes a lot of memory, causing tasks requiring heavy memory to suffer.

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Can Python do everything that R does? ›

R can't be used in production code because of its focus on research, while Python, a general-purpose language, can be used both for prototyping and as a product itself. Python also runs faster than R, despite its GIL problems.

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Why is Python better than R and SAS? ›

Python has a simple and easy-to-learn syntax, making it a good choice for beginners. R has a more expressive syntax and is more suitable for advanced users, as it allows for more complex programming. SAS has a proprietary and non-standard syntax, which can make it difficult for users to switch to other languages.

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Should I learn both Python and R? ›

Both languages are well suited for any data science tasks you may think of. The Python vs R debate may suggest that you have to choose either Python or R. While this may be true for newcomers to the discipline, in the long run, you'll likely need to learn both.

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Is R programming in demand? ›

Data Visualization Expert

R's popularity can be attributed in part to its extensive selection of visualization libraries. Consequently, there is currently a high demand for professionals who specialize in Data Visualization using R programming.

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What is Python not good for? ›

Python is a popular programming language that offers many benefits: ease of use, readability, and a large community of developers. However, it also has some limitations, such as slower performance compared to compiled languages, memory management issues, dynamic typing, and version compatibility.

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Is R or Python better for finance? ›

R: R is mostly used by data scientists as it is used only for data analysis. But compared to Python, it has been outraced. As finance involves the calculation and analysis of data R would be best for you. Python: Python is being used in almost all industries for data science, machine learning, and developing.

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Do people still use R? ›

R is popular among data analysts and research scientists as it helps them import and clean data and perform effective quantitative analyses. Moreover, R coding was among the top five programming languages of the year as of August 2021. Apart from this, many reasons make R so useful.

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Will R overtake Python? ›

If the question is "will data scientists all stop using R in favor of Python?" then I would say no. There are a lot of people who prefer to use R, and a number of specialized R packages that have no easy Python equivalent.

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Can you replace R with Python? ›

Whereas, R is limited to statistics and analysis. Many data scientists and software developers select python over R because of its: Readability: Python is extremely easy to read and understand. Popularity: One of the most popular open-source programming languages for data scientists.

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Should I learn R or Python for economics? ›

In conclusion, both R and Python have their strengths and weaknesses for economic and econometric analysis. R has a more specialized focus on statistical analysis, making it an excellent choice for those who need to perform more complex econometric analyses.

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How difficult is R compared to Python? ›

Overall, Python's easy-to-read syntax gives it a smoother learning curve. R tends to have a steeper learning curve at the beginning, but once you understand how to use its features, it gets significantly easier. Tip: Once you've learned one programming language, it's typically easier to learn another one.

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What is the main difference between Python and R? ›

The main difference between these two languages is that Python is a general purpose, and R comes from statistical analysis. Another considerable distinction is its approach to data science. Both have supportive communities and are constantly working on making their libraries and tools more valuable.

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Which software is better, R or Python? ›

Python: Python tends to offer better performance than R in certain scenarios, particularly for CPU-bound tasks and algorithms. Python's ability to integrate with lower-level languages like C and C++ through libraries like Cython allows developers to optimize performance-critical sections of code.

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Is R difficult to learn? ›

R is considered one of the more difficult programming languages to learn due to how different its syntax is from other languages like Python and its extensive set of commands. It takes most learners without prior coding experience roughly four to six weeks to learn R. Of course, this depends on several factors.

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Is Python enough for data science? ›

Is Python Necessary in the data science field? It's possible to work as a data scientist using either Python or R. Each language has its strengths and weaknesses. Both are widely used in the industry.