Technology

Python vs. R for Data Science: Which Language is Better?

Python is now the #1 programming language. It is growing exponentially and is becoming one of the preferred technologies for data science. It is a powerful, scalable, and dynamic language with the capabilities to write clean code and handle massive requests simultaneously. Python development offers several data science libraries and tools for numerical computation, statistical, scientific computing, and other applications of Machine Learning. 

R is an open-source programming language that focuses primarily on data analytics, statistics, and graphical models. The academic and research community is the main user of R. However, in recent years, enterprises have started exploring R as a feasible programming language for building solutions in data science. It is highly useful for manipulation and visualization of data to build analytical models and solutions. 

Both Python and R are important programming languages for data science. They are powerful and offer the scientific computing capabilities important for advanced solutions. Choosing between both the programming languages might be a challenge. Understand from the beginning that there is no right or wrong choice between Python app development and R development for data science. It is only a matter of what best suits your requirements. 

Read more: 10 real-world Python Use Cases and Applications

Let us understand a little more in detail the differences between Python and R for data science. 

Python vs. R: Data Science Language Comparison 

The comparison of Python vs. R doesn’t suggest one language is better than the other. However, it showcases how both technologies have evolved over time and what capabilities they offer to enterprises and developers. Here’s a comparison between the programming languages based on different parameters:-

Popularity

In terms of popularity, Python is the #1 programming language with a market share of 11.27%. It is continuously growing in terms of usage and evolving rapidly. Enterprises, large or small scale, are using the technology to build all kinds of solutions. Python has one of the largest active communities in the world. Programmers contribute to the open-source language and offer frequent updates that keep the language updated and powerful.

R has grown tremendously in popularity. In Oct 2021, it was the 9th most popular programming language, an upward jump of 5 places from 14th rank in Oct 2020. It has a massive community of researchers and statisticians who utilize the programming language for various scientific projects. It has a robust community, and its usage is multiplying day by day as enterprises realize the capabilities of R for enterprise applications.

Utility

Both programming languages have different utilities. Python web development services are highly versatile. There’s collection, storage, analysis, visualization of data, development of Machine Learning solutions, Deep Learning algorithms, and much more. Whether the solutions need to be implemented on the website or into production, Python has the capability to achieve different development goals.

On the other hand, R offers a structured approach to statistical computation and data analysis. It offers amazing intuitive customization capabilities using the #ggPlot2 library using the Grammar Graphics approach. R is the perfect programming language if someone wants to become a data analyst. Some even say that it is a more specialized language than Python for statistics and scientific analysis.

Ecosystem

Python has tons of libraries and tools for data science operations. NumPy, SciPy, Pandas, Keras, TensorFlow are 5 of the most popular libraries that help with scientific computing, numerical and statistical analysis, data visualization, graphical analysis, and much more. Matplotlib is one of the best libraries for visualizing data that simplifies Python application development when working with massive datasets.R is a language developed specifically for statistical and data analysis. It has a dynamic set of libraries for specialized purposes of analytics. The R ecosystem includes RStudio (a popular IDE), CRAN (Comprehensive R Archive Network), Tidyverse, dplyr, R packages, ggPlot2, and several other libraries to perform statistical operations. While it mostly has uses in big data, R is increasingly being used for enterprise application development. 

Robustness

Needless to say, Python offers dynamic capabilities to build data science-based solutions. For data collection, Python supports different kinds of data formats. For data exploration, it has libraries like Pandas that simplify data analysis. There’s NumPy and SciPy for data modeling. Data visualization is Python’s strength, but there is Matplotlib that is extensively used for visualizing different data points and providing a presentable view.

R is useful for basic web scraping and extracting data from excel and csv files. It is useful for creating probability distributions, applying different statistical tests, and using basic standard Machine Learning techniques. One of the most significant aspects of R is that it was built for representing the results of statistical analysis with the base graphic module for creating basic charts and plots. ggPlot2 is a significant library to achieve the same.

Applications

Enterprises use Python development services for a wide variety of reasons. The programming language is useful for web development, mobile application development, game development, enterprise applications, desktop GUI, and much more. Python is highly versatile, ensuring that all types of enterprises can use it for development. However, Python web development solutions are the top application of the technology.

In essence, R is not primarily useful for web application development. It is not a general-purpose language and finds most applications in statistical analysis and computational capabilities. While R supports TensorFlow, Keras, and Torch in production environments, it is mainly used to analyze data and represent it visually through data visualization packages and tools.

Read more: How Much Does it Cost to Build a Python Application?

Wrapping Up

There are limited tools that support Machine Learning, Artificial Intelligence, and Data Science development. Python and R are two of the essential programming languages for solutions that involve analyzing and processing massive amounts of data. 

R is useful for statistical and data analysis. However, enterprises are now exploring the programming language for other applications as well. Python, being a general-purpose programming language, is important for Machine Learning and Artificial Intelligence solutions as well. It is the enterprise’s choice to decide which programming language they should work with. In most cases, a custom software development company can achieve the desired outputs as the technology has a large serving community that keeps the technology updated at all times. 

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