R vs Python for Data Science: Which Should You Learn?

Regarding the best programming languages for data science, we have two top contenders fighting head-to-head - ‘Python’ and ‘R’. Many data professionals prefer working with Python and R for various data-related tasks, such as data analysis, data visualization, etc.

If you have just put your foot forward in data science or plan to put it in the future, one common question may arise: Which programming language should I learn - Python or R?

Both are open-source programming languages. While R could be a new programming language for many computer science students, Python is widely-known. These languages help engineers create ground-breaking innovations through artificial intelligence and machine learning .

Python and R are similar in many ways, such as both being open-source and widely used in data science tasks. The primary difference is that the former is general-purpose, whereas the latter has its roots only in statistical computing and analysis.

Rather than choosing between these two languages, it is better to learn both to stay benefited in the long run. After learning them, you can use any one based on their use cases.

In this article, we have drawn a detailed comparison between R and Python programming languages. Also, data science and data analytics would be the focal point for the R vs Python comparison.

R vs Python: Head-to-Head Comparison

The following table describes the key differences between R and Python:

What is R?

R is a programming language employed widely for statistical computing and graphics. Data miners and statisticians primarily use this language to analyze data and create statistical software. It is analogous to the S language and has several statistical and graphical techniques.

More interestingly, R is also a free, open-source software environment that runs on Windows, macOS, and Linux platforms. It is a collection of tools that facilitate data manipulation and calculation. The major tools available in the R environment are intended for carrying out the following tasks:

• Effective data handling
• Calculations on arrays
• Data analysis

Initially, R was used for academic and research purposes. However, as enterprises required a tool that could help them handle huge amounts of data, R emerged as the best option. Also, the language comes with many packages, making it quite easy for data scientists to process it efficiently.

History

In 1995, Ross Ihaka and Robert Gentleman created an open-source programming language and named it R, which is an implementation of the S programming language. The goal behind creating the language was to develop a new programming language ideal for statistics, data analytics, and graphical models. The name of the language was named after the initials of the developers' first name.

Features

Let us now throw light on the features of the R language and the R environment.

Language Features

• Basic Statistics: R facilitates the computation of 'Measures of Central Tendency’ . There are three measures of central tendency, namely mean, mode, and median, which are the fundamental statistics terms.
• Probability Distribution: The language makes it easy to manage various sorts of probability distributions, such as Normal Distribution, Binomial Distribution, Chi-squared Distribution, and many more.
• Static Graphics: It is replete with features that encourage the development of static graphics. It entails functionality for creating various types of plots, including maps, mosaic plots, etc.
• Data Analysis: You will find a plethora of tools for data analysis.

Environment Features

• R Packages: The R software environment has an exhaustive repository of 10,000 packages called Comprehensive R Archive Network (CRAN).
• Distributed Computing: It provides two new packages: ddR and multidplyr, for distributed computing. Distributed computing is a model in which a software system shares its components across multiple computers to improve efficiency.

Pros

• Free and Open-Source: R is a free and open-source language and software environment for statistical analysis.
• Cross-Platform: The language’s software environment is compatible with multiple platforms, including Windows, macOS, and Linux.
• Machine Learning Operations: The comprehensive repository of packages is intended for machine learning and data analysis.
• Data Wrangling: The language enables you to perform data wrangling using the packages: dplyr and readr.
• Supports Various Data Types: You can carry out operations on a variety of data types, including arrays, matrices, and vectors.
• Active Community: The language has an active community of developers across the globe who are always ready to contribute their skills to the community.

Cons

• Steep Learning Curve: R syntax is completely different from other programming languages. Hence, you may find learning it difficult in the beginning.
• Slow Speed: The language is slower than its counterparts, such as MATLAB and Python. The reason is that its functions are spread across various packages of CRAN.
• Poor Memory Management: Due to poor memory management, the language can consume all the available space on the system.
• Low Security: The language is not as secure as other languages. It lacks security features.

What is Python?

It is a general-purpose and object-oriented programming language suitable for use in various fields, including web development, AI and ML, and data science. The language's built-in data structure, dynamic typing, and a vast collection of libraries make it a popular language for developing desktop and web apps, data analysis, data visualization, and task automation.

Python is also one of the most preferred languages among data scientists as it offers functionality to deal with statistics, mathematics, and scientific functions. Like R, it can perform various data science operations using libraries like NumPy and SciPy. It even has libraries like matplotlib, which is capable of visualizing graphs.

The language provides simple syntax and amazing libraries to easily run complex data science algorithms. Though Python does not contain as many statistics packages as R, each update for the language is intended to make it more powerful and feature-rich.

History

Guido Van Rossum, in 1991, released Python 0.9.0, the first version of the language. He released it as a successor to the ABC language. Later, in 2000, he released Python 2.0, which was a more improved version. It included new features like list comprehension, garbage collection, and reference counting.

The next version of the language, Python 3.0, was a major release with many new features. The latest and stable version is Python 3.11.3 as of April 2023.

Features

• Object-Oriented: Being an object-oriented language, it supports all OOPs concepts, such as classes, objects, inheritance, encapsulation, abstraction, and polymorphism.
• Interpreted Language: Python is an interpreted language, i.e., it reads and executes a program line by line and stops the execution if there is an error in the code.
• High-Level Language: When you write Python programs, you do not have to worry about memory management and system architecture. The language manages it all for us.
• Extensible: It is an extensible language. You can embed Python code in programs written in C and C++ languages . Also, you can compile the Python code along with C and C++ programs.
• Standard Library: The language’s standard library is so extensive that it provides several modules and functions for various tasks. Hence, it is called the 'batteries included' language.
• Dynamically Typed: When declaring variables, there is no need to define their data types. The Python interpreter automatically assigns a data type to variables at runtime based on their values.

Pros

• Python is the most simple language, making it easy for beginners to understand and implement.
• As the language allows developers to focus on developing business logic with its simple syntax, it is a productive language.
• It is free and open-source. Hence, anyone can download it easily.
• Python is a versatile language used for developing a variety of applications.
• The code you write once in Python on a specific platform can run on other platforms without making any changes.

Cons

• As it is an interpreted, dynamically-typed language, Python programs are slower in execution than other languages.
• It is not ideal for developing mobile applications.
• The Global Interpreter Lock (GIL) does not support threading.
• Python consumes a large amount of memory. So, it is not suitable for building applications that prioritize memory optimization.
• The database access layers of the language are underdeveloped compared to JDBC and ODBC. So, it is not good for database connectivity.

R vs Python - Differences

Let us dive deeper into the differences between Python and R.

• Purpose

Though both languages are ideal for performance data-related tasks, Python is general-purpose, and R is specific to statistical computing and graphics. Hence, Python is more versatile than R and is also used in web, software, and game development. It tops the list of popular programming languages, according to the TIOBE Index of May 2023 .

• Type of Users

Being a versatile language, Python is used by software developers, web developers, and data professionals, including data scientists and data analysts. The language’s easy syntax and extensive set of libraries enable developers to be more productive while creating applications.

Conversely, R is specifically designed for statistical analysis. Hence, statisticians and researchers primarily use this language.

• Learning Curve

Python’s syntax is straightforward and uses English keywords, making it easy to remember and use. It eliminates the need for complex structures, like delimiters. This makes it possible for programmers to focus completely on developing business logic rather than remembering syntax. Hence, the language has a very low learning curve.

On the other hand, R has a pretty complex syntax and is not similar to the syntax of other programming languages. Hence, beginners find it difficult to learn the syntax. They require more time to understand, learn, and master the language.

• Popularity

In the past few years, Python has gained immense popularity and outranks every other programming language, including R. The TIOBE Index has ranked Python the top language multiple times (also ranks currently). This is due to its versatility and ease of use. Various domains, including web development, software development, and data science, leverage Python. Conversely, R is especially popular in data science, academia, and some other domains.

• Libraries

Both languages have extensive libraries and packages, but Python outperforms R. Python offers more than 30,000 packages hosted in the Python Package Index (PyPi). Meanwhile, R has 19,000+ packages in its Comprehensive R Archive Network (CRAN).

• IDEs

IDE stands for Integrated Development Environment . It is a software application that consolidates at least a source-code editor, an interpreter or compiler, and a debugger. It combines all essential developer tools under one roof, eliminating the need for using multiple tools during development.

You can find a plethora of IDEs for Python, such as PyCharm, Spyder, Jupyter, etc. Also, online IDEs are available that you can access in your browser and do not require local installation. Regarding R, the popular IDE is RStudio. You can download this IDE on your website or access it within your browser.

R vs Python - Which One To Choose?

There is no one-size-fits-all programming language; you may need more than one language in your data science journey.

Both the programming languages, R and Python, have their own set of features, pros, and cons. Python is a general-purpose programming language. It is useful for web and desktop applications, task automation, data analysis, and data visualization. Meanwhile, R is specially designed for statistical computing and graphics. It is ideal for statistics, data science, and analytics.

Therefore, the choice between Python and R entirely depends on your project's requirements. Choose R for statistical learning as it offers unmatched libraries for data exploration. On the other hand, go with Python if you wish to build machine-learning models and large-scale applications.

Additionally, before making a choice, you must ask yourself a few questions:

• What is the experience with the language?
• What language do competitors use?
• What kind of problems do you need to solve?
• What are your areas of interest and expertise in data science?

You can effortlessly choose your programming language based on the answers to the above questions.

Conclusion

Data science experts use both Python and R programming languages. However, many developers stick with one programming language, so most choose Python over R because it provides more flexibility. By learning Python, an individual will be able to work in the field of data science and other fields, such as software development and web development.

However, developers working with data analysis and statistics always suggest choosing R because of its packages. It is an individual's choice to go with Python or R programming language.

We hope this Python vs R article has helped you understand all the crucial differences between the two so that you can easily choose the one that best fits your requirements.

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