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

Habban Raza
6 min readSep 7, 2023

You are probably starting your data science adventure if you read this post. You are undoubtedly well aware that learning to code is essential for every aspiring data professional. Additionally, you could already know the Python vs. R controversy and need assistance selecting which one to study. Don’t panic if you find yourself in this predicament; most data professionals have been there.

The two most used programming languages for data science are Python and R. Any data science task you can imagine can be accomplished using either language. The Python vs. R argument could lead you to believe you must select Python or R.

Although this might be the case for those who are new to the field, you’ll probably need to learn both in the long run. Instead of considering the two languages mutually exclusive, think of them as complementing tools that you may combine based on your unique use case.

Why are R and Python the ideal programming languages for data science? This article will discuss the main similarities and differences between R and Python and some considerations for selecting the best language for your needs.

Now that we’ve established that Python and R are both respectable, well-liked options, there are a few things to consider that might influence your choice.

Why Choose Python?

Data research, web development, and gaming are just a few software industries that employ the open-source, general-purpose language Python.

Python, first released in 1991, currently holds the top spot in some popularity indices for programming languages, including the TIOBE Index and the PYPL Index.

The Python user base is one of the factors contributing to the language’s widespread ubiquity. Python is supported by a sizable user and developer community that ensures the language’s seamless development and refinement and the ongoing release of new libraries made for various uses.

Python is a simple language to read and write because it resembles human speech. In actuality, Python was created with excellent readability and interpretability in mind. For these reasons, Python is frequently mentioned as a programming language that beginners with no prior coding experience should start with.

Due to its versatility and the hundreds of specialized libraries and packages that support all data science tasks, including data visualization, machine learning, and deep learning, Python has steadily gained popularity in data science.

Why Choose R?

The open-source programming language R was developed primarily for statistical analysis and graphics.

R has been frequently used in academic settings and scientific research since its initial release in 1992. It continues to be one of the most often used analytics tools in conventional data analytics and the quickly developing business analytics discipline today. The TIOBE Index and the PYPL Index hold the 11th and seventh positions, respectively.

With R, created with statisticians in mind, you only need a few lines of code to use sophisticated functions. Several publicly available and simple-to-use statistical tests and models exist, including classifications, clustering, non-linear modeling, and linear modeling.

R’s large community is largely responsible for its wide range of possibilities. It has built one of the most comprehensive sets of data science-related programs. They are all accessible through the CRAN (Comprehensive R Archive Network).

The capability of R to produce high-quality reports with support for data visualization and its available frameworks to develop interactive web apps are two further features that set it apart from other statistical software. R is the best tool for creating stunning graphs and visualizations in this sense.

R vs. Python: Key Differences

Now that you are more comfortable with Python and R, let’s compare them to determine their similarities, advantages, and disadvantages from a data science standpoint.


Both Python and R are appropriate for any data science work, even though they were developed for quite different purposes — Python as a general-purpose programming language and R for statistical analysis today. On the other hand, Python is regarded as a more flexible programming language than R because it’s also very well-liked in other software industries, including game development, web development, and software development.

Type of Users

Python is frequently used by programmers venturing into data science since it is a general-purpose computer language. Additionally, Python is a better tool for creating complicated applications because of its productivity-focused approach.

On the other hand, R is widely used in academics and some industries, including banking and the pharmaceutical industry. It is the ideal language for statisticians and researchers with little programming experience.

Learning curve

Python is regarded as one of the programming languages with the most intuitive syntax and the closest to English. A smooth and linear learning curve makes it a very useful language for beginning programmers. Although R is made to perform simple data analysis quickly and effectively, it becomes more difficult when dealing with complex jobs, and it takes more time for new R users to become proficient in the language. Overall, Python is regarded as a good programming language for newcomers. R is simpler to learn at first, but it’s more challenging to become knowledgeable due to the complexity of advanced functions.


Python and R reign supreme in data science despite the recent growth of other programming languages like Julia. The discrepancies are startling in terms of popularity, which is a tricky topic in general. R has continuously fallen short of Python, especially in recent years. Several popularity indices for programming languages place Python at the top. Python is widely used in many software disciplines, including data science, which explains why. R, on the other hand, is mostly used in academics, some industries, and data science.

Common Libraries

The ecosystems of packages and libraries created expressly for data science are strong and diverse in both Python and R. The Python Package Index (PyPI) hosts the majority of Python packages, whereas the Comprehensive R Archive Network (CRAN) often hosts R products. Here is a list of some of the most well-liked R and Python data science libraries.

R packages:

  • dplyr: It is an R library for data manipulation.
  • tidyr: It is an excellent software that will assist you in cleaning up and organizing your data.
  • ggplot2: It is the ideal library for data visualization.
  • Shiny: It is the best tool for using R to create interactive web apps directly.
  • Caret: It is one of the most significant R machine learning libraries.

Python packages:

  • NumPy: It offers a broad range of computational functions for scientific applications.
  • Pandas: It is ideal for manipulating data.
  • Matplotlib: The industry-standard data visualization library
  • Scikit-learn: It is a Python package that offers a variety of machine-learning techniques.
  • TensorFlow: It is a popular deep-learning framework.

Common IDEs

The various components of building a computer program can be consolidated by programmers using an IDE, or integrated development environment. Strong user interfaces with integrated features let programmers create code more quickly.

The most well-known Python IDEs for data research include Spyder, Jupyter Notebooks, and its more recent iteration, JupyterLab. In terms of R, RStudio is the most often used IDE. The user can view graphs, data tables, R code, and output simultaneously, thanks to the setup of the interface.

Python vs. R: A Comparison

Below, you can find a table of differences between R and Python:

R vs Python: Which Language Should You Learn?

No programming language is best for every issue that can arise along your data science journey, despite each one’s advantages and disadvantages.

Furthermore, it’s crucial to consider the context at all times. Before choosing, consider the following factors: Have you ever programmed before? Which programming language are your coworkers using? What types of issues are you attempting to resolve? What topics of data science most interest you?

You can select one of the two options after responding to these questions. In any case, relax since Python and R are both great choices for data research. We have created a broad selection of courses and tracks for your benefit.