Python vs. R for Data Science 2023: Which is better?

Python vs. R for data science: Understand the key differences between these popular programming languages to pursue a lucrative career in data science.

Python vs. R for Data Science 2023: Which is better?
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Wondering which programming language to choose for your next data science project? This one-stop guide on Python vs. R for data science will help you understand the difference between R and Python and make the right decision for your data science learning path. 


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With every industry generating massive amounts of data –  crunching data requires more powerful and sophisticated programming tools like Python and R language. Python and R are among the popular open-source programming languages that a data scientist must know to pursue a lucrative career in data science.

 

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Python vs. R for Data Science: The Basics 

Python is popular as a general-purpose programming language, whereas R is popular for its great features, such as data visualization and statistical computing. At ProjectPro, our project experts often get questions from prospective learners about what they should learn, Python or R? Which is better for data science, R or Python? You are on the right page if you are still determining which programming language to learn first.

Python and R language top the list of essential statistical computing tools among data scientist skills. Data scientists often debate on the fact that which one is more valuable, Python or R. However, both programming languages have their specialized key features complementing each other.

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Data Science with Python Programming Language 

Data Science with Python

 

Data science consists of several interrelated but different activities, such as data analysis, statistical analysis, building predictive models, accessing and manipulating data, computing statistics, building explanatory models, visualizing data, and integrating models into production systems. Python programming provides data scientists with a set of libraries that helps them perform all these operations. 

Python is a general-purpose language for data science that has gained wide popularity because of its readable syntax and operability in different ecosystems. Python programming can help programmers play with data by allowing them to do anything they need with data - data analysis, data munging, data wrangling, website scraping, web application building, data engineering, and more. Python makes it easy for programmers to write maintainable, large-scale, robust code. 

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Unlike R language, Python does not have in-built packages. Still, it supports libraries like Scikit, Numpy, Pandas, Scipy, and Seaborn that data scientists can use to perform practical statistical and machine-learning tasks.

Why Learn Python for Data Science? 

Find below the most compelling reasons to learn python for data science

Why Python for Data Science

 

  • Beginner-Friendly: Python is user-friendly because of its easy-to-understand syntax and linear, smooth learning curve. With a focus on code readability, Python reads like the English language and is simple to understand for beginners. 

  • Multi-Purpose Language: The use of python is not limited to the data science community. Many developers use it to build a  wide range of applications, making it usable in various tasks within the computer science background ranging from CGI and web development, system testing and automation, and ETL to gaming.

  • Scalable: Python is a highly scalable language for vast, complex operations such as compiling large datasets and executing deep learning and machine learning algorithms.  

Data Science with R Programming Language 

Millions of data scientists and statisticians use R to get away with challenging problems related to data analysis and statistical computing. R language has become essential for finance and business analytics-driven organizations like LinkedIn, Twitter, Bank of America, Facebook, and Google.

R is an open-source programming language widely used for statistical analysis and visual representation of data. It has a robust ecosystem for use in typical machine learning and data mining techniques. R programming performs statistical analysis on massive datasets and provides a range of options for exploring data. It also facilitates the usage of probability distributions and the application of various statistical tests.

R language has an innovative package system that allows developers to extend the functionality to new heights by providing cross-platform distribution and testing data and code. With more than 5K publicly released packages available for download, it is a great programming language for exploratory data analysis. It can easily be integrated with object-oriented programming languages like C, C++, and Java. R language has array-oriented syntax making it easier for programmers to translate math to code, particularly for professionals with a minimal programming background.

Why Learn R for Data Science? 

Find below the most effective reasons to learn R for data science: 

Why R for Data Science

 

  • Best for Data Visualization: R is one of the best tools for data scientists in data visualization. It has everything a data scientist needs- statistical models, data manipulation, and visualization charts. With R programming, data scientists can draw meaningful insights from data in multiple dimensions using 3D surfaces and multi-panel charts.   

  • Perform Complex Statistical analysis: Statisticians and data analysts use R for statistical analysis and effectively manage massive datasets using typical machine learning models and data mining.

  • Best for Data Analysis Tasks: The R language is designed particularly for data analysis with the flexibility to mix and match various statistical and predictive models for the best possible outcomes. R programming scripts can further be easily automated to promote production deployments and reproducible research.

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Python vs. R Programming for Data Science : Key Differences

The key differences between Python and R are listed below based on factors including speed, learning curve, popularity, use cases, and integrated development environment. 

Python vs. R: Speed 

  • Python: Python, being a high-level language, renders data significantly faster. So, when it comes to speed, python appears to be faster with a simpler syntax.

  • R: R is a low-level programming language, which means lengthy codes and increased processing time. Thus, working with R is relatively slower than python or other programming languages with poorly written code. However, there are solutions to this, like the FastR package, pqR, and Penjin. 

Python vs. R: Learning curve 

  • Python: Python is the most basic programming language, emphasizing simplicity and code readability, resulting in a smooth learning curve. It is appropriate for beginners who are new to programming and data science. 

  • R: R programming has a steep learning curve for developers who do not have prior statistical language programming skills or a data science background. But, if you are already familiar with programming languages, R is not too difficult to grasp. 

Python vs. R: Popularity 

  • Python: Python language has gained wide popularity because of its readable syntax, making it easy to learn under expert guidance. Data scientists can gain expertise and knowledge and master programming with Python in scientific computing by taking industry expert-oriented Python programming courses.

  • R:  R language is less popular when compared to python. However, the usage of this language is increasing exponentially for business applications. It is popular with people passionate about the statistical calculation and data visualization aspects of data analysis. 

Python vs. R: Use Cases 

  • Python:  Python is best suitable for deep learning, machine learning, and large-scale web applications and is also used for other things such as testing, web development, and software development. The following are the most popular applications of python: 

  • Dropbox is completely written in Python code, which now has nearly 150 million registered users.

  • Python programming is used by Mozilla to explore its broad code base. Mozilla releases several open-source packages built using Python.

  • Walt Disney uses Python to enhance its creative processes' supremacy. 

  • Some other exceptional products written in Python language are Cocos2d, Mercurial, Bit Torrent, and Reddit. 

 

  • R: R is suitable for statistical learning and is used to build projects involving statistical analysis and visualization. The following are the applications of R: 

  • Ford uses open-source tools like R programming and Hadoop for data-driven decision support and statistical analysis.

  • The popular insurance giant Lloyd’s uses R language to create motion charts that provide analysis reports to investors.

  • Google uses R programming to analyze the effectiveness of online advertising campaigns, predict economic activities and measure the ROI of advertising campaigns.

  • Facebook uses R language to analyze the status updates and create the social network graph.

  • Zillow uses R programming to promote housing prices.

Python vs. R: Integrated Development Environment 

  • Python: Python offers a variety of IDEs, the most popular of which are Jupiter Notebooks, Spyder IDE, and PyCharm. 

  • R:  The R language is also compatible with Jupiter Notebooks. However, RStudio is the most used R software. R users can use RStudio in two different ways: RStudio Server (through a web browser) and RStudio Desktop (runs as a regular desktop application).   

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Python v R for Data Science 

Let us look deeper at the key differences between Python and R for data science in terms of data collection, exploration, modeling, and visualization. 

Python: Python supports all kinds of data formats (such as CVS. and JSON files). You can also import SQL tables into your Python code. The Python requests library makes it simple to get data from the web for building datasets in web development.

R: This programming language helps data analysts to import data from Excel, CSV, and text files. Files in SPSS or Minitab format can also be converted into R data frames. However, unlike Python, R is not versatile enough to get data from the web. 

Python: Python lets you explore data with Pandas, a data analysis library for python. It enables users to filter, sort, and display data easily. Pandas helps you store a large quantity of data and provides multiple features for efficiently displaying it.

R: R also gives users a wide range of options for data exploration and applying data mining techniques. It can handle basic data analysis without the need for the installation of other programs. It also includes easily accessible statistical tests and algorithms.

Python: Python has standard libraries for data modeling, such as Numpy for numerical modeling analysis, scikit-learn for machine learning algorithms, and 

SciPy for scientific computing and calculations. 

R: Data scientists sometimes rely on packages outside R's core functionality for specific modeling evaluation in R. However, there are certain packages, such as Tidyverse, that make it easy to visualize, manipulate and report on data.

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Python: Python does not have extensive capabilities for complex data visualizations when compared to R. However, python users make use of libraries such as Matplotlib, Pandas, and Seaborn for generating basic charts and graphs

R: R is much better than Python in terms of data visualizations. R was designed to display statistical analysis results, with the fundamental graphics module making it simple to build basic charts and plots. ggplot2 may also be used to create more advanced plots, such as complex scatter plots with regression lines.

Why is Python Better than R for Data Science?

Python, a general-purpose language, can be used for many different things, such as data science, web development, gaming, and more. 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. 

  • Simplicity: Python is known for its simplicity and readable syntax. 

  • Ability to build quality projects: Most deep learning and data science projects are done in python codes. 

  • Reliable Performance: Python ensures reliable performance at each stage of its development. 

R vs. Python for Machine Learning 

Both python and R are excellent for machine learning and artificial intelligence. But experts claim that python offers a slight advantage over R in machine learning. This is because of the following reasons: 

  • The python libraries for machine learning, such as scikit-learn, TensorFlow, and Keras, make it simple to build models from scratch. 

  • With python, integration with other languages is easier. 

  • Python is also better in terms of memory use. 

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Difference Between R and Python in Tabular Form

 

 

Python 

R

Purpose of Existence 

Python is a general-purpose language for data science that has gained wide popularity because of its readable syntax and operability in different ecosystems.

R is an open-source programming language widely used for statistical analysis and visual representation of data.

Speed 

Python appears to be faster with a simpler syntax.

R is relatively slower than python or other programming languages with poorly written code. 

Learning 

Curve 

Python emphasizes simplicity and code readability, resulting in a smooth learning curve. 

R programming has a steep learning curve for developers who do not have prior statistical language programming skills. 

Popularity 

Python has gained wide popularity because of its readable syntax, making it easy to learn under expert guidance. 

R is less popular when compared to python. However, the usage of this language is increasing exponentially for business applications.

Features 

  • Open-source

  • Beginner-friendly 

  • Multi-Purpose

  • Scalable 

  • Open source

  • Best for data visualization

  • Complex statistical analysis

  • Data analysis tasks 

Libraries & Packages 

  • NUMPY/SCIPY

  • PANDAS

  • SCIKIT-LEARN

  • STATSMODELS

  • MATPLOTLIB

  • CARPET

  • GGVIS, GGPLOT2

  • STRINGR

  • ZOO

  • PLYR, DPLYR

Applications 

Mozilla uses Python programming to explore its broad code base. Mozilla releases several open-source packages built using Python.

Dropbox is completely written in Python code which now has nearly 150 million registered users.

Walt Disney uses Python to enhance its creative processes' supremacy. 

Some other exceptional products written in Python language are Cocos2d, Mercurial, Bit Torrent, and Reddit. 

Ford uses open-source tools like R programming and Hadoop for data-driven decision support and statistical analysis.

The popular insurance giant Lloyd’s uses R language to create motion charts that provide analysis reports to investors.

Google uses R programming to analyze the effectiveness of online advertising campaigns, predict economic activities and measure the ROI of advertising campaigns.

Facebook uses R language to analyze the status updates and create the social network graph.

Zillow uses R programming to promote housing prices.

Python vs. R for Data Science: Key Takeaways 

Having understood the differences between these two programming languages, the bottom line here is that it is difficult to choose to learn any one language first -Python or R to crack data scientist jobs in top big data companies. Thus, the best solution is to make a smart move based on your requirements in terms of speed, learning curve, etc, and decide which language you should learn first that will fetch you a job with a big data scientist salary and later add to your skill set by learning the other language.

FAQs on Python vs. R for Data Science 

Both R and Python are considered the most popular languages for data analysis and data science. But, experts advise learning python before R as the language is easy to learn and beginner friendly. 

Python is a popular object-oriented programming language because of its easy-to-learn nature, and its multi-purpose structure makes it suitable for many requirements. On the other hand, R was built for specialized purposes, such as statistical techniques, making it difficult to learn for beginners.   

Most of the data analysis and data science tasks that can be performed in R can also be performed in Python, and vice versa. Also, various data science and deep learning algorithms can be written in both languages. However, performance, syntax, and implementations may change across the two languages for particular algorithms.  

 

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