December 7, 2022
December 21st, 2022
The world of Data Science is incomplete without its set of programming languages, Python and R being the two most popular and talked of, among them all. Both, R and Python have been in the limelight of the future, through modern-day technologies like AI, ML, data analytics, data science, etc.
When we look at R vs Python, we see that Python is a general-purpose language with a readable syntax. It is flexible in data analysis tasks and has a more general approach to data science. Whereas R is created by statisticians, focuses on statistical parts of a project, and performs statistical analysis.
There has been a constant comparison between R vs Python, based on their characteristics, pros, cons, integrations, user groups, and organizations using them. Before we go into the details of understanding the difference between Python and R, let us get introduced to them individually.
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment.
Released in 1995, R was created by Ross Ihaka and Robert Gentleman, two statisticians in New Zealand. Their objective was to come up with a language that offers a user-friendly experience for data analytics and graphical modeling. Soon, R became one of the fastest-growing statistical languages in the IT world.
It is a free, extensible, and interpreted language in which there is no requirement of executing it via a compile prior to the code run. It offers an enriched and robust suite of tools for graphing, data modeling, data reporting, and statistical modeling. As we compare Python vs R for data analysis, R has been gaining traction rapidly.
R is an implementation of S – the language that was developed in the 1970s. Developers usually write and edit the R code through RStudio – the Integrated Development Environment (IDE) for coding. This simplifies reporting, visualization, and statistical analysis.
There is huge community support through user-focused documentation and mailing lists. There is a big repository called CRAN (Comprehensive R Archive Network) for curated R packages that make it simple to avail the latest functionalities.
Being a statistical and command line language, R offers a lot of statistical methods and a huge variety of libraries for coming up with static graphs, interactive and dynamic graphs. There are 10000+ packages that are supported by R.
R comes with a wide variety of statistical methods like linear and non-linear modeling, clustering, statistical tests, etc. It can easily represent mathematical formulas and notations. It works well on UNIX, Windows, and macOS.
R has been a favorite for data scientists and researchers for data cleaning and preparation, building visualizations, and creating ML and deep learning algorithms.
Python is a programming language that lets you work quickly and integrate systems more effectively.
Released in 1991 by Guido van Rossum, Python has been a popular general-purpose programming language that focused on increased productivity, simplicity, and code readability. It was meant for developers who wished to apply statistical methods for data analysis and has been ranked amongst the top programming languages.
Python is a free, flexible, interpreted language that has a standardized library for simplistic programming of common activities without the need for the installation of extra libraries. There are multiple robust and sophisticated coding libraries in Python especially for data science – NumPy, Pandas, TensorFlow, Keras, PyTorch, and StatsModels to name a few.
Python has packages like PyPi that consist of multiple libraries that can be utilized for many purposes and great community support. When we compare R vs Python for data science, Python has been exponentially increasing in popularity with effective data science applications floating around.
As a multi-paradigm language, it is leveraged in web application development, system automation/scripting, software development, data science, and data analysis, executing efficiently on multiple platforms.
It offers a simple, less confusing syntax that has similarities to English Grammar which helps in increasing code efficacy. Its intuitive syntax mimics natural language and hence it is adored by developers and has a small learning curve.
It offers features such as object-oriented and high-level programming, inbuilt data structures, dynamic typing, and late binding that binds variables and methods during runtime. There is Jupyter Notebook – an open-source web application for sharing documents that contain equations, live code, and visualizations, making the data science descriptions better.
Google, IBM, Facebook, Spotify, NASA, Instagram, Dropbox, Pixar, Quora, Netflix, Industrial Light and Magic, Intel, JP Morgan Chase, The North Face, Yahoo Groups, Shopzilla, etc.
Uber, Facebook, Google, Airbnb, Bank of America, Twitter, HP, Ford, Microsoft, ANZ, IBM, Novartis, Roche, BCG, The New York Times, Mckinsey, Accenture, Capgemini, etc.
Ruby, Python, MATLAB, Python, Java, Golang, Julia, SAS, Rust, Mathematica, Scilab, SciPy, Numpy, etc.
PHP, Ruby, NodeJS, Rust, Golang, Java, Kotlin, Elixir, Scala, C#, R, Lua, Perl, Haskell, Nim, Clojure, etc.
As we compare Python and R, we see that there are certain attributes that are present in both. Here are the similarities that can be observed in R and Python:
Though both look similar and have a lot in common, there are interesting characteristics that are observed in both that separate their usage criteria and help us analyze the difference between R and Python.
|Overview||A statistical language for analysis and visual representation of data, with powerful libraries. Used when data analysis needs standalone computation.||A general-purpose language for the deployment and development of projects. Used when data analysis needs to be integrated with web applications.|
|Inspired By||S programming language||Modula-3, ABC, and C languages|
|Suitable For||Ideal for data exploration, statistical processing, specialized analysis, representation||Ideal for deep learning, machine learning, embedded systems, large-scale web apps|
|Utilization||For modeling, simulation, complex data analysis in data science, visualization, music production, etc.||Streamlined approach for data science, scientific computing, games, user interfaces, scripting, client-side AJAX apps, etc.|
|Key Objective||Data analysis, graphical models, and user-friendly statistics||Deployment and production with code readability and productivity|
|User Base||Research teams, scientists, etc.||Developers, programmers, etc.|
|Learning Curve||Easy to learn in the start but develops complexities as you go ahead, tough for novices||Linear and smooth learning curve because of its readability and simplicity, easy for novices|
|Libraries and Packages||Hosts packages like caret, dplyr, tidyr, zoo, ggplot2, stringr, R-forge, randomForest etc. Has lesser libraries than Python.||Hosts packages and libraries like pandas, SciPy, NumPy, Scikit-learn, Matplotlib, seaborn, statsmodels etc.|
|Applied When||Majorly applied when analysis needs independent computing and independent servers||Majorly applied when data must be integrated with a web app or statistics to be used in production|
|Development||Can be used to develop simple web apps, REST APIs, ML models, statistical summaries, data visualization, etc.||Can be used to develop apps from the beginning like desktop/mobile/web apps, web servers, etc.|
|Data Collection||Can import data from Excel, CSV, and text files||Supports CSV, JSON, SQL tables, and data from online datasets|
|Syntax||Relatively difficult syntax and hence tough to learn||Simple and easy syntax and hence preferred by developers|
|IDE||RStudio, StatET, RKward, R Commander etc.||Jupyter Notebook, Spyder, Atom, Eclipse+PyDev, etc.|
|Code||R code demands more maintenance||Python code is robust and easy to maintain|
|Programming Style||Supports procedural programming for a few functions and object-oriented programming for others||Supports multiple programming styles like object-oriented, functional, aspect-oriented, structural|
|Data Exploration||Optimized for statistical analysis of huge datasets||Optimized for exploration of data with Pandas|
|Data Modeling||Supports Tidyverse, easy to manage||Supports NumPy, SciPy, scikit-learn, etc.|
|Repository||Comprehensive R Archive Network (CRAN) contributed by the users||PyPi, Python’s software repository and package index accompanied with libraries|
Python is recommended for –
R is recommended for –
Whenever you must choose the better out of the best, it is a tough call. The same is the case while comparing R vs Python. Both have their own popularity quotient and fan base across the globe, as one of the best programming languages. Both possess multiple features that can be of great advantage to businesses.
Yes, when we compare Python and R, they do have their own share of typicality in terms of the execution of work as mentioned above. Hence, the final call is in the hands of the organization on which one to choose – Python or R.
It depends upon major parameters like key objectives of your projects – be it statistical analysis or deployment, organizational skills in the selected technology, project timelines, number of resources readily available, etc. Evaluating these parameters helps us choose between Python vs R.
There are differences between Python and R. But, on a general note, based on the above-detailed study, you will see system developers, software engineers, web developers, and system admins choose Python and scientists, data engineers, academicians, and researchers choose R.
Finally, both, R and Python, are par excellence and carved a niche for themselves in the industry. Based on the applications and use cases, businesses must choose either, depending upon the need, functionality, and objectives. Be it R language vs Python, either way, you are at a winning point!
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