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R vs. Python: Which One Is Better for Data Science?

by Hassan 2 months ago in courses / degree / college
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Which One Is Right for You

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Data science is an interdisciplinary field that uses statistical and programming skills to analyze and visualize large amounts of data. It’s an essential skill for any modern professional because most data is unstructured and impossible to understand without some programmatic help.

R and Python are two popular programming languages used by data scientists today. Both offer many benefits that can make life easier on projects involving big data sets, but they also have unique differences in how they operate. This article will cover those critical differences and uses.

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When to Use R Programming Language

It is used for statistical analysis and data visualization. It was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. R is open-source software for all platforms like Windows, Mac OS X, Linux, and Unix-like operating systems. It is also used in many commercial and academic applications for teaching/learning statistics and analytics.

R is Suitable for Business Analytics

R is suitable for business analytics because it’s open source and free, making it accessible to businesses of all sizes. In addition, r is a programming and statistical programming language explicitly designed for data analysis.

R can be used as a programming language for statistical computing and graphics, as an environment into which other languages such as C can be embedded, or through interfaces with other languages such as Python. In addition, R has built-in support for many popular statistical techniques, econometrics, machine learning, visualization (including high-resolution graphics), GIS, databases, time series analysis, network theory, and many more.

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When to Use Python Programming Language

Python is a general-purpose programming language. It was developed and made in the late 1980s by Guido van Rossum, who also wrote its standard library.

Python is a high-level language, which means it’s easy to learn and use compared to low-level ones like C or assembly. It has dynamic typing and garbage collection, so you don’t have to worry about memory management when working with Python. In addition, Python uses duck typing; this lets you change the type of an object at runtime without changing its existing methods and properties.

Python is an interpreted language. This means that programs run directly from source code without going through compilation steps (as in some compiled languages like Java). Because no compilation steps are involved in running a Python program, your development process will generally be faster than if you used R or another compiled language such as C++ or Java.

Python is Machine Learning Friendly

Python is the most popular and used programming language for data science, and it’s easy to see why. It is a general-purpose language used to build web applications, desktop apps, and even games. It’s also one of the easiest languages to learn. This is because Python was designed with simplicity in mind: you can write code with fewer lines than other languages (or write more lines if you want), making it easier to read and understand when working with other developers on a project.

Python makes it easy to build machine learning models by providing many libraries that let you quickly implement algorithms from scratch without worrying about low-level details like memory management or memory leaks. This makes Python especially good for beginners who want an easy way into machine learning without much heavy lifting upfront. However, this flexibility comes at some cost because these libraries may not provide optimal performance compared to specialized implementations written directly in something like C++ or R.

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Critical Differences Between R and Python for Data Science

R and Python are excellent data science tools but have different uses and strengths. R is a programming language designed explicitly for statistics, while Python is a general-purpose scripting language. Nevertheless, both have become popular in recent years due to their flexibility, ease of use, and available software libraries.

The most significant difference between these two languages is how they’re used. Statisticians primarily use R to build models that explain raw data. Python is more commonly used by engineers who want to develop systems or interact with data without formal knowledge of statistics or mathematics.

Libraries and Packages for Data Manipulation and Visualization

Libraries are the building blocks of any programming language.

Python has a large and active community, so many libraries are available for doing just about anything you can think of.

Python’s standard library contains all sorts of utilities and extensions to help you get started with data science right away.

There are tens of thousands of third-party packages on GitHub alone — and new ones appear daily!

This is not true for R, which has fewer libraries than Python but still offers plenty for statistical analysis, machine learning algorithms, plotting charts, graphics, etc.

You can find a comprehensive list of R libraries and packages for data manipulation, visualization, and analysis. Some popular ones include ggplot2 (for creating charts), plotly (for interactive plots), rCharts (for building dashboards), dplyr, and tidyr (for data wrangling), among others.

You can find a great list of libraries and packages for Python. Some popular ones include Matplotlib (for plotting), Seaborn (for statistical graphics), and bokeh (for interactive visualizations), among others.

Statistical Analysis and Machine Learning Algorithms

R and Python have many options for libraries, statistical analysis packages, and machine learning algorithms. The same goes for data visualization, data manipulation, and other software packages.

R has many more general-purpose libraries than Python does — for example, it’s much easier to find an R library that will create a histogram or find the k-nearest neighbors within a set of data points than you’d have luck with Python.

But if you’re looking specifically for something related to machine learning algorithms or neural networks (i.e., artificial intelligence), then Python may have an advantage because these are its specialty areas.

Final Thoughts

R and Python are excellent languages for data science, but they each have their strengths. If you’re looking for a language that’s easy to learn and quick to start, then Python might be the best choice. However, if you want something more complex or need access to specific libraries, then working with R may be better suited for your needs.

The critical thing when choosing one over another is determining what type of work you’re doing (and how much time you have) so that it lines up with the language best suited for your needs! I hope this article helped explain the difference between R and Python and provided you with some guidelines on which language to use.

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About the author


I'm a data scientist by day and a writer by night, so you'll find me writing mostly about Analytics.

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