Python Vs. R: Which Should Be The Go-To For Beginners Looking To Get Into Data Science?

Discover the basics of the two most popular programming languages in data science, the key differences between them and how to choose the right one for you

More and more people are breaking into data science every day. The discipline is booming and there is no sign of slowing down in the coming future. If you are considering starting a new career in data science, at one point you will need to learn how to code. 

Programming is crucial in every data role. Whether you are performing exploratory data analysis, visualizing data to find hidden patterns, or building a machine learning model to predict housing prices next year. Everything is done with programming. 

But what exactly is programming? 

It’s a technique that allows tasks to be executed in a computer system. Or more simply put, to communicate with computers, we use programming languages. 

There are hundreds of programming languages out there. You can think of a programming language as a toolbox designed to create, fix, and analyze things in the digital world. Building a house is not the same as performing a medical operation. Each activity requires a completely different set of tools. The same thing applies in the digital world: depending on the task and domain at hand, some programming languages will work better than others. 

What languages do you need for data science? 

In the field of data science, the two most popular programming languages are Python and R. Both languages are well suited for any data science tasks you may think of. It’s very common to hear about “Python vs R”, which suggests the idea that you have to choose either Python or R. That may be true for beginners, but the question that matters is how to make the best use of both languages for your specific use case.

So what makes R and Python the perfect candidates for data science? We will also study the main differences between Python and R and will provide some factors to consider to choose the right language for your needs.

What is Python?

Python is a general-purpose, open-source programming language that can be used for various applications, including software development, gaming, and data analysis. 

Launched in 1991, Python is one of the most popular programming languages in the world, occupying the top position in several programming language popularity indices, such as the TIOBE Index and the PYPL Index. 

Popularity is closely associated with the community size of a programming language. And here Python is unbeatable. Python is backed by a vast community of users and developers, who ensure the smooth growth and development of the language.

Python is an easy language to read and write due to its high similarity with human language. In fact, high readability and interpretability are at the heart of the design of Python. For these reasons, Python is often cited as a go-to programming language for newcomers with no coding experience. 

Over time, Python has been gaining popularity in the field of data science thanks to its simplicity and the endless possibilities provided by the hundreds of specialized libraries and packages that support any kind of data science task. In addition, Python is particularly well placed for some of the most powerful data techniques, including machine learning and deep learning.

What is R?

R is an open-source programming language specifically created for computing and statistical analysis. 

Developed in 1992, R enjoys wide popularity in scientific research and academia. And today it remains one of the most popular analytics tools used in both traditional data analytics and the rapidly-evolving field of business analytics.

R includes functions that support, to name just a few:

• Linear modeling
• Non-linear modeling 
• Classical statistics 
• Classifications
• Clustering

The extensive possibilities R offers are mostly due to its huge community. It has developed one of the richest collections of data-science-related packages. All of them are available via the Comprehensive R Archive Network (CRAN).

Another feature that made it particularly remarkable was its ability to generate quality reports with support for data visualization and its available frameworks to create interactive web applications.

Python vs R: a Comparison

Now that you’re a little more familiar with Python and R, let’s compare them from a data science perspective to assess their similarities, strengths and weaknesses. 

Purpose

While Python and R were created with different purposes –Python as a general purpose programming language, and the R for statistical analysis–, nowadays both are suitable for any data science task. However, Python is considered a more versatile programming language than R, as it’s also extremely popular in other software domains, such as web development, gaming, and blockchain.

Type of Users

As a general-purpose programming language, Python is the standard go-to choice for software developers breaking into data science. Plus, Python’s focus on productivity makes it a more suitable tool to build complex applications. By contrast, R is widely used in academia and certain sectors, such as finance and pharmaceuticals. It is the perfect language for statisticians and researchers with limited programming skills. 

Learning curve

Python’s intuitive syntax is considered one of the closest programming languages to English. This makes it a very good language for new programmers, with a smooth and linear learning curve. Although R is designed to run basic data analysis easily and within minutes, things get harder with complex tasks, and it takes more time for R users to master the language. So, Python is an easier programming language, especially for beginners.

Popularity

In terms of popularity, Python has consistently outranked R, especially in recent years. Python ranks first in several programming language popularity indexes. This is due to the widespread use of Python in multiple software domains, including data science. By contrast, R is mostly employed in data science, academia, and certain sectors.

Common Libraries

For Python:

• NumPy: provides a large collection of functions for scientific computing.
• Pandas: perfect for data manipulation.
• Matplotlib: the standard library for data visualization.
• Scikit-learn: is a library in Python that provides many machine learning algorithms.
• TensorFlow: a widely used framework for deep learning.

For R:

• dplyr: It is a data manipulation library for R.
• tidyr: a great package that will help you get your data clean and tidy. 
• ggplot2: the perfect library for visualizing data.
• Shiny: It is the ideal tool for creating interactive web apps directly from R.
• Caret: one of the most important libraries for machine learning in R. 

Common IDEs

An IDE, or Integrated Development Environment, enables programmers to consolidate the different aspects of writing a computer program. They are powerful interfaces with integrated capabilities that allow developers to write code more efficiently.

In R, the most commonly used IDE is RStudio. Its interface is organized so that the user can view graphs, data tables, R code, and output all at the same time.

As for Python, the most popular IDEs in data science are Jupyter Noteeboks and its modern version, Jupyter Lab, and Spyder.

Advantages

For Python:

• Multipurpose language, with a vaster user base than R
• Python is better suitable for machine learning, deep learning, and large-scale web applications.
• One of the best programming languages to learn for beginners

For R:

• Better suited for statistical analysis.
• Considered the best language for data visualization.
• Large collection of powerful data science libraries.

Disadvantages

For Python:

• Not as many data science libraries as R.
• It requires rigorous testing times, especially when developing complex applications
• The process of data visualization is not as pleasant and elegant as in R.

For R:

• More difficult to learn for people with no software development background.
• Limited user community compared to Python
• R is considered a computationally slower language compared to Python, especially if the code is written poorly.
• Finding the right library for your task can be tricky, given the high number of packages available in CRAN

Python vs R: Which Language Is Best for You?

Despite their strengths and weaknesses, the truth is there is no single programming language that is best for every problem that may pop up during your data science journey.

Plus, it is always important to assess the context. Before making any choice, you should ask yourself several questions: Do you have programming experience? What programming language do your colleagues use? What kind of problems are you trying to solve? What are your areas of interest within data science? 

Once you have answered these questions, you can choose one of the two. In any case, don’t panic: both R and Python are excellent options for data science. That’s why in Sololearn we have prepared several courses to help you through. Check out our Python for Data Science course and our R course and start your data science journey right now!