R vs. Python for Data Science: A Detailed Guide for Beginners

R vs. Python for Data Science A Detailed Guide for Beginners

The world is about to see the next technological marvel. Artificial Intelligence (AI) and Data Science (DS) are the two variables that are creating events that we never expected to occur. Consequently, the demand for AI experts and data science developers has surged.

Python and R are two computer languages that are frequently viewed as essential for data scientists.

Both Python and R are open-source programming languages that may be used on Windows, Mac OS X, and Linux. Both languages can handle practically any data analysis work and are considered to be quite easy to learn, especially for beginners. So, which should you focus on initially (or foremost)? Before we go into the distinctions, let’s have a look at each language in general.

Understanding the Programming Language: R

R is a domain-specific programming language for data analysis and statistics. It is a significant aspect of the research and academic data science sector since it employs specialized syntax used by statisticians.

R utilizes a procedural development paradigm. It divides down programming operations into a sequence of stages and subroutines rather than arranging data and code into groups as object-oriented programming does. These methods make it easy to figure out the processing of complex operations.

R has a large community, similar to Python but with an emphasis on analysis. R does not provide general-purpose software development like Python, but it is superior at handling specific data science tasks because that is all it does. The R ecosystem consists of the following components:

  • An R-based IDE – RStudio
  • The Comprehensive R archive network – CRAN
  • A popular R package collection – Tidyverse
  • A set of functions that enables data frame manipulation – dplyr
  • R packages, duplicable R scripts, and R functions
  • A free and open-source data visualization tool – Ggplot2

Advantages of R

  • R is a free-to-download and an open-source programming language to use. Even programmers can contribute to optimizing the source code.
  • R is platform-agnostic, which means it can operate on Windows, UNIX, and Mac OS X. 
  • Using packages like readr and dplyr, R may convert a jumbled code into a well-organized one.
  • R generates appealing graphs using notations and formulas using the Ggplot and plotly packages.
  • R contains a lot of packages for machine learning, data analysis, and statistical tasks.

Disadvantages of R

  • R uses more memory since all of the objects are kept in physical memory. Therefore, as the software accumulates more data, the process slows down.
  • R lacks fundamental security, making it difficult to integrate into online applications.
  • R is a sophisticated language that might be tough to master for a novice, unlike Python.
  • R has a long runtime since it is a sluggish processor. As a result, it takes longer to generate output than other programming languages like MATLAB and Python. 
  • Data processing in R takes time since it requires all of the data to be in one place. Big Data isn’t a suitable fit for it. It does, however, have a function that makes it a bit easier to use.

Understanding the programming language: Python

Unlike C++ and Javascript, Python is also an object-oriented programming language. It provides stability and modularity to projects irrelevant of their size. Even if you are a rookie in the programming language and web development world, it provides a flexible approach to web development and data science that seems natural.

It is a user-friendly language for beginners, yet it provides the type of flexibility that web developers need to build sites like Spotify, Instagram, Reddit, Dropbox, and the Washington Post.

Outside of data science, learning Python provides programmers the abilities they need to work in business, digital goods, open-source initiatives, and a variety of online applications. The Python ecosystem includes a number of popular libraries, including:

  • For numerical analysis – NumPy
  • For predictive analysis – SciKit-learn
  • For scientific computing – SciPy
  • Artificial intelligence and deep learning – Keras
  • For geospatial data visualization – Folium
  • For statistical data visualization – Seaborn
  • For data analysis – Pandas
  • For Python IDE (Integrated Development Environment) – PyCharm
  • Object-Oriented API for embedding plots – Matplotlib

Advantages of Python

  • It is one of the most adaptable languages available. It’s well-structured, clean, and simple to use. Exploratory data analysis is a breeze with Python’s versatility. Python can also be deployed in functional programming prototypes other than being used as an object-oriented programming language.
  • Python is available as an open-source program that may be downloaded without paying any charges. It features one of the most active support forums, and anybody may help improve the library’s usefulness.
  • Python comes with a plethora of libraries that are required to do important data science tasks.
  • Its integration and control features boost productivity while also saving time.
  • Python scripts may be embedded on other websites. In addition, other programming languages, such as C++, can be used alongside Python scripts.

Disadvantages of Python

  • Python is slower than other programming languages since it is an interpreted language.
  • Python is not compatible with the Android and iOS operating systems. In such a setting, developers believe that it is poor language. It may, however, be utilized if extra effort is put in.
  • Python eats quite a lot of memory. So when more items need to be accessible, the procedure slows down.
  • Python’s database access layers are immature as compared to JDBC (Java Database Connection) and ODBC (Open Database Connectivity), resulting in a less popular database connectivity option.
  • Because of the Global Interpreter Lock in Python, threading or the flow of many functions at the same time is a drawback (GIL).

Important distinctions: R vs. Python

In Data Science, Data Analysis, Machine Learning, and other fields, Python and R are the recommended languages. However, despite the fact that they are used for comparable objectives, they are not the same. For example, r focuses primarily on the statistical aspect of a project, but Python is more versatile in terms of usage and data processing jobs.

R is an excellent programming language for creating graphs from data. However, due to its yet-to-be-developed production tools, R is difficult to utilize in a production setting, but Python is readily integrated with a complicated work environment.

Python is a better alternative in terms of performance, as it runs quicker in all contexts than R. Regardless depending on how they are used, both languages are popular choices for individuals to work with.

R vs. Python : In brief

AttributeR programming languagePython programming language
TypeR is a statistical programming language for data analysis and visualization.A general-purpose programming language, Python is used to deploy and create a variety of applications. It includes the tools needed to get a project into production.
UsabilityR is a good choice for statistical learning since it has a lot of libraries for data experimentation and exploration.Deep learning, machine learning, and large-scale web applications are better suited to Python.
Library structureR contains fewer libraries than Python and is simpler to learnPython comes with a plethora of libraries. However, comprehending all of them might be difficult.
ApplicationR may be used to create music in addition to object-oriented programming. It can also be used to solve complicated mathematical issues.Despite being an object-oriented language, Python may be used for a variety of tasks such as creating a graphical user interface, developing games, and so on. It may be used to create new apps from the ground up.
Syntax structureThe syntax of R is rather sophisticated, and the learning curve is not easy.Python has a basic syntax that is straightforward to pick up.
RequirementWhen a data analysis activity necessitates solitary computation(analysis) and processing, R is typically utilized.Python is primarily used for data processing that requires integration with online applications. 
IDE availabilityRStudio, StatET, and others are some of the R language’s IDEs.There are several Python IDEs to pick from, including Jupyter Notebook, Spyder, Pycharm, and others.
PopularityR is a less popular programming language among users. Scientists and R&D personnel who routinely employ data analysis are among its consumers.Python is more well-known, with a large user base. Python is mostly used by programmers and developers.


When it comes to the use of Python and R, there is a dilemma around which is the most appropriate. Both of these languages have their own set of benefits and drawbacks. Many people use Python for numerous reasons, however R is also widely utilized. R is mostly used for statistics, while Python is utilized for a wide range of features. It is up to the user to choose the language that best suits their needs.

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