Python Vs R – Which should I learn for Business Data Analytics?

R Vs Python – Which should I learn for Business Data Analytics?

Now, it’s the time for a battle of two most demanding programming languages that is R vs Python. We will go deep in understanding the differences between the two languages. And, I assure you that you will not have any confusion left after completing this article i.e. R vs Python – the most trending debate of aspiring data scientists.

R vs Python

To explore everything about R vs Python, first, you must know what exactly R and Python are. Below is the brief introduction to both the languages. Have a look –

What is R?

R is a programming language that is specifically for statistical computing and graphical computation. R project is an extension of the S language that was developed at Bell Laboratories by John Chambers and his colleagues. It is a language and environment for statistical computing and graphics.

Data Analysts, Data Scientists, Statisticians make use of R programming language to analyze the data and carry out statistical analysis through graphs and other types of visualizations. R allows the users to perform analysis on large datasets. This language is continuously growing with thousands of packages that can be used readily for many applications.

There are various statistical and graphical techniques that R provides such as linear and non-linear modeling, statistical testing, time-series analysis, classification, regression, clustering and many more.

The main strength of R is its well-publicized quality plots that we can produce using its many functionalities and packages. The user also retains full control over the design choices in their graphics.

R Environment

R is a comprehensive suite of various facilities and functionalities that we can use for data manipulation, calculations as well as graphical display. These facilities comprise of –

  • It provides efficient data handling as well as a storage facility.
  • It provides a suite of operators which we can utilize to perform calculations on arrays and in particular higher-dimensional matrices.
  • The abundance of graphical facilities and functionalities geared towards data analysis provide various display exports in different formats.
  • It provides a large, coherent and integrated collection of several tools that we can utilize for data analysis.
  • It is a well developed, simple as well as an efficient programming language that involves loops, conditions, recursive functions as well as several input-output functionalities.


What is Python?

Python is an interpreter based, high level and an object-oriented programming language that consists of dynamic semantics. Python is built-in high-level data structures that we can combine with dynamic typing as well as dynamic binding to make it very attractive for Rapid Application Development. We can also avail it for scripting or an adhesive language to combine several components into one framework.

Python is simple, easy to learn and also provides emphasis on readability that significantly reduces the overall cost of program maintenance. Python has an easy learning curve and advocates for program modularity as well as code reusability. The Python interpreter, as well as the extensive standard library, are present in the binary form that do not charge for major platforms and provides free distribution.

Programmers who are new to programming often fall in love with Python due to its vast functionalities and ease of learning. Since Python has no compilation step, the provision of the edit-test-debug cycle is significantly fast. In Python, the process of debugging programs is incredibly easy. Unlike other languages like C and C++, an inappropriate input will never lead to a segmentation fault. When an interpreter finds an error, it raises an exception to the user right after we enter the input. If however, the program does catch the exception, the interpreter provides a stack trace. All of these features added with the massive libraries for a variety of purposes make Python an efficient programming language. Using a source-level debugger one can easily inspect local as well as global variables.

Difference between R and Python

Below we will discuss R vs Python on the basis of definition, responsibilities, career opportunities, advantages, and disadvantages –

R Vs Python – Definition


  • It was in particular, geared towards addressing the statistical techniques.
  • R has several graphical libraries like ggplot2 and plotly which make it highly popular owing to quality reports and images that we can generate.
  • We can use R for several statistical functions like regression, clustering, classification, statistical testing, etc. Therefore, R has a variety of supported operations for machine learning and statistical tasks.
  • While R suffers from a steep learning curve, it proves to be an ideal language for expressing statistical methodologies, allowing the data scientists to wrangle the data as per their requirements.
  • R is an open-source programming language meaning that users can actively participate in contributing towards the growing language as well as avail the many benefits without any license.
  • Being an interpreter based language, R can run the script immediately and provide output to the user. This allows ease of code debugging.


  • It is a multi-purpose programming language that we can utilize for a variety of tasks.
  • Python has a library for every task. From web application development to penetration testing and data science, Python has a solution for everything.
  • It provides an ample number of libraries that we can avail for various machine learning operations as well as advanced deep learning methodologies.
  • Python provides a smooth learning curve that makes it highly popular among novices as well as experienced programmers who want to transition into development using Python as their primary language. Due to the vast support libraries, it is becoming the go to choice for many operations as the Python users perceive the language to be the one solution to many problems.
  • Python is also an open-source programming language that has many open-source projects in which the users can actively contribute.
  • We can parse the Python scripts and can also run them immediately. Furthermore, we can save Python as compiled programs that can be referenced by other programs.


R Vs Python – Responsibilities

Responsibilities of an R Developer are as follows –

  • An R professional is responsible for providing simulations, carry out data analysis, visualization using a variety of R packages.
  • An R developer provides assistance in the design and development of the core architecture that will be used in the statistical model.
  • The developer also coordinates with the end-user for the design and development of the solution to the analytics problems that address the project proposals.
  • Another key responsibility is to provide data access, manipulate information and report the functions.
  • Creation of key procedures for integrating R programming with data sources and delivery systems that export the data file in several formats.
  • An R developer is responsible for providing assistance to the developers for executing their plans while keeping in mind the client requirements.
  • R programmers have to build a code that consists of a qualitative variable as well as manipulation tasks with several statistical procedures.

Want to become the next Data Scientist? If yes, then you can’t afford to miss these top R Interview Questions.

Responsibilities of a Python Programmer are as follows –

  • A Python Developer must be able to implement server-side logic to carry out server-based computations.
  • Development of asset management software is another key responsibility of a Python programmer.
  • In order to integrate different software systems, a Python developer must be able to develop a scalable adhesive code.
  • Writing reusable and testable code for better operational quality is one of the main functions of a Python developer.
  • Data Security and protection of software is also one of the requirements of a software solution. Therefore, a Python developer must be able to address that.
  • A Python programmer must be able to implement event-driven programming.
  • Another important job role is to carry out efficient unit testing and debugging.

R Vs Python – Career Opportunities

Some of the industries that hire R developers are –

  • Academia
  • Finance
  • Banking
  • Healthcare
  • Manufacturing
  • E-commerce

Some of the companies hiring R developers are IBM, Airbnb, Uber, Twitter as well as several public statistical institutes.

Some industries that are hiring Python developers are –

  • Software Development
  • Manufacturing
  • Robotics
  • Embedded Systems
  • Automation and Testing
  • Ethical Hacking

Some of the companies hiring Python developers are Google, Amazon, Dropbox, Quora, Reddit, Pinterest and many more.

R Vs Python – Advantages and Disadvantages

Advantages of R

  • The main advantage of R is its open-source nature. You can, therefore, work with R without any licence or payment of fees. Being open-source, you can also contribute towards the customisation of R packages, newer development as well as resolution of issues.
  • R provides exemplary support for data wrangling. The packages like dplyr, readr are capable of transforming messy data into a structured form.
  • With over 10,000 packages in its CRAN repository, R facilitates libraries that are diverse and every field that uses data can make use of them.
  • R has some essential features for graph plotting and aesthetic enhancement of graphs. There are popular libraries such as ggplot2 and plotly that offer a wide range of graph customization options to the users.
  • It is a platform independent language that can execute programs on Windows, Linux and Mac.
  • R is a specific language that is used for statistical modeling. It is the primary tool for creating statistical tools for data science. This gives R an essential advantage over other programming languages like Python.
  • R is constantly evolving. It provides various state of the art features that keep on updating it whenever any new algorithm is released.
  • It has an active and engaging community. There are various online forums in R that provide help and support to the R programmers. Furthermore, there are various bootcamps and online seminars that provide active education to aspiring R programmers.

Disadvantages of R

  • The R programming language shares its roots with a much older programming language called S. Because of this, R lacks most of the features of a modern programming language like support for dynamic or 3D graphics.
  • R requires its objects to be stored in physical memory. As compared with other statistical tools, R requires more memory for its programs. Since R requires the entire data to be loaded into its memory, it is not a good option when dealing with Big Data.
  • Since R stems from much older technology, the basic capabilities like security were not native to R. This restricts R as web applications cannot embed it or its usage as a backend computation language like Java, Python or Node.js.
  • R poses a steep learning curve. People who had a background in statistics would find it ideal to use R. Therefore, for people who are starting afresh in data science may find R a difficult language to adapt to.
  • Packages in R tend to be slower than other competing languages like Python and MATLAB.
  • Most of the R algorithms are implemented on different packages. This decentralization of packages makes it difficult to apply algorithms on problems without prior knowledge of the required package.

Advantages of Python

  • Just like R, Python is open-source. You can use Python for free. Furthermore, you can change, customise and contribute towards Python libraries.
  • Python is a general-purpose programming language that facilitates its usage over diverse tasks. Areas software development, robotics, embedded systems, automation, etc. make heavy use of python.
  • Python comprises of state of the art APIs like TensorFlow, pytorch, keras, numpy that are extremely useful in building artificial neural networks.
  • It is a user-friendly programming language. This is one of the main reasons as to why Python is the standard programming language in universities.
  • Python is secure. These server-side computations involve Python as it provides various frameworks for the development of web applications.
  • Python is apt at handling large datasets. It can load data files much faster and can also work with Big Data ecosystems.

Disadvantages of Python

  • Being an interpreter based language, Python is slower than other languages like C, C++, and Java.
  • Python lags behind R when it comes to statistical analysis. Python though may have improved a lot but it still lacks certain statistical packages as compared to R.
  • The dynamically typed nature of Python makes it vulnerable to runtime errors.
  • Python, when compared with JDBC has an underdeveloped database access layer.
  • Tasks that require heavy memory suffer from Python. The flexible data-types in Python contribute towards its high memory consumption.


Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!