R vs Python for Data Science – Who will win the battle?

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.

R vs Python

So, let’s quickly start with our tutorial on R vs Python.

Difference between R and Python


R language is an open source programming language. R is an integrated suite that contains various software facilities for data analysis, management and graphical analysis. It is used by data scientists to facilitate data operations on unstructured as well as structured data. It is maintained by the R core-development team – a team of volunteer developers from across the globe. Also, we use this language to perform statistical operations. And, it is available on the R-Project website www.r-project.org. R is also a command line driven program.


Python is very easy to learn language. Its feature itself is modest. They didn’t require too much time in investment. Also, its syntax is easily readable.

Moreover, Python is an ideal teaching language because of this simplicity. Thus, it also allows newcomers to pick it up quickly. Python is an excellent language for rapid prototyping. This is the main reason as to why it is becoming popular among developers. As these developers spend time in analysing the problem, Python proves to be an excellent tool that allows them to implement the solution without dwelling in the complexities of the programming language.

R Quiz

R vs Python for Data Science

As we are well known that both the languages are gaining height in the data analyst community. Moreover, results show that both the languages are fighting to become data scientist’s language of choice. This is the main point in R vs Python.

Introducing the Opponents of R vs Python

1. Current Versions

  • R – The current version of R is 3.6.0 that was released in April 2019. 
  • Python – The current version of Python is 3.7.0 that was released in June 2018.

2. History

  • R

Creators: It was created by Ross Ihaka and Robert Gentleman.
Release Year: 1995

  1. Basically, R is an implementation of S programming language.
  2. Also, it’s design and evolution is being handled by the R-core group. 
  3. Moreover, it’s software environment was written in C, Fortran, and R.
  • Python

Creators: Python was created by Guido Van Rossum.
Release Year: 1991

  • There is one important thing about Python that was inspired by C, Modula-c and particularly by ABC.
  • This language gets its name from the “Monty Python’s Flying Circus” comedy series.
  • Basically, there is one software present – Python Software Foundation(PSF) that was responsible to take care of Python advances. 

3. Purpose

  • R: Its main focus is on statistics, data analysis, and graphical models.
  • Python: It highlights productivity as well as code readability.

You must definitely check the R Graphical Models tutorial

4. Used By

  • R: Used for a long time in academics and research although, it is expanding itself into the business market.
  • Python: It has been used by the programmers as they want to move into data analysis. 

“Someone working in an engineering environment might prefer Python”

5. Community

  • R: It has been getting support from the huge communities. And that support is coming in the form of:
  1. Different mailing-lists.
  2. Documents that are contributed by different users.
  • Python: It is getting very good support for general purpose coding. Python support has been found:
  1. Mainly from StackOverflow.
  2. More and more adoption from developers and programmers.

6. Usability

  • R:
  1. In this, with the help of only a few lines, you can write statistical models.
  2. It has different R style sheets but it can’t be used by everyone.
  3. It has a very good feature which includes several ways to write the same piece of functionality.
  • Python:
  1. Performing coding and debugging in Python is a very easy task as it has a very easy to use syntax.
  2. Particularly in Python, we can write a piece of functionality in the same way.

Do you know about Python Debugger Commands for Command Prompt

7. Flexibility

  • R: We can easily use complex formulas. Also, we can use a kind of statistical tests and models that is available in R.
  • Python: It is an important feature of Python that it is used for doing something unique. Also, it is used for scripting a website by developers.

8. Ease of Learning

  • R: At the start, it is having a steep learning curve. But as soon as you know the basics, you can learn advanced stuff. And, a good thing about R is that it’s not difficult for experienced programmers.
  • Python: It is best for readability and it’s simplicity. It is taught mostly to the beginners who are new to programming. 

The Case for Python and R

1. Why is Python great for Data Science?

  • Python provides a wide range of APIs like TensorFlow, PyTorch for deep learning.
  • Python is capable of wrangling a large amount of data.
  • Python has an easy learning curve, making it possible to implement data science operations without any hassle.

2. Why is R great for Data Science?

  • R is a specific programming language that is oriented towards statistical modeling. It is the lingua franca of statistics which makes it an ideal option for data science. 
  • R has an extensive set of packages that can be used for a variety of functions. 
  • R is most famous for its data visualization libraries like ggplot2 that has made it popular among data scientists.

Don’t forget to check the tutorial on using R for Data Science

R and Python for Data Analysis War

After learning about both the technologies, let us now see the comparison between R and Python.

We’ll compare Python and R language on the following attributes:

  • Availability/ Cost
  • Ease of learning
  • Data handling capabilities
  • Graphical capabilities
  • Advancements in tool
  • Job scenario
  • Deep Learning Support
  • Customer service support and Community

R vs Python Infographic

1. Availability/ Cost

Both are completely free owing to their open-source nature.

2. Ease of Learning

As R has the steepest learning curve, it’s necessary to learn and understand coding. In it, simple procedures can take longer codes as it is a low-level language.

Python is known just because of its simplicity. Also, it has excellent features for documentation and sharing.

Explore the unique features of Python programming

3. Data Handling Capabilities

In R, the data is saved on the local memory. This limits the amount of data that R can process. Furthermore, R takes longer to load data as compared to Python. Python is much better at handling large amounts of data. 

4. Graphical Capabilities

R is most popular due to its aesthetic and visually appealing graphical libraries. ggplot2 is the most popular graph plotting library. Many data scientists prefer R over Python due to its graphical capabilities.

5. Availability of Packages

R has over 10,000 packages in its CRAN repository. These packages have been developed solely with open-source contributions and tend to all the areas that require data analysis. The abundance of packages keeps R ahead of the curve when compared with Python.

6. Job Prospects

Both R and Python have become the two primary languages for Data Science. Since Data Science is exponentially increasing, its job prospects have increased greatly. Therefore, both Python and R enjoy equal prestige in terms of job opportunities.

Wait! Have you checked the Lucrative Career Opportunities in Python

7. Support

Both languages, being open-source in nature rely on online sources, journals and manuals for providing support to the user. Furthermore, the massive online community support has boosted the popularity of these two languages. However, they both lack in online customer support which is present in closed source tools like SAS and SPSS.

R (Lingua Franca of Statistics) and Python (A Multi-Purpose Language)

  • R

It was created by statisticians. We can use R packages to communicate ideas and methods for statistical analysis. Hence, engineers, statistician, and scientist those are not having knowledge of computer programming skills find it easy to use.
We can also use R in different fields such as finance, pharmaceuticals, media, and marketing. R’s on the rise as a business analytics tool.

“The number one value to a business in using R is access to talent”

R is experiencing rapid growth. It holds the third place as software, right after SAS and SAP.

  • Python

Python is a common and easy language, said by many programmers. It always brings people from different backgrounds together.

Hence, Python is a production ready language. It has the capacity to be a single tool that integrates with every part of your workflow!

R vs Python in terms of Speed

  • R
  1. It is slow, on purpose. That was designed to make data analysis and statistics easier to do. But not to make life easier for your computer.
  2. It requires defining how it’s implementation works.
  3. As R is poorly written, a lot of R code is slow.

Must Learn – Statistical Programming in R

  • Python

“Visualisations are important criteria in choosing data analysis software”

Python has some nice visual visualisation libraries.

  • Seaborn Library based on matplotlib
  • Bokeh Interactive visualization library
  • Pygal – To create dynamic SVG charts.

Common Features of both R and Python

Let us see features that are common to both Python and R programming.

1. Open Source

Both languages are free to download for everyone. That is, in comparison to SAS and SPSS, both are commercial tools.

2. Advanced Tool

R and Python are having advanced tools as new developments and changes appear first in R and Python, before making their way to commercial platforms.

3. Online Communities

Both dispose of online communities, thus, offering support to their respective users.

Have a look at the Latest Features of R in R tutorial

Advantages and Disadvantages of R and Python

Let us finally see the advantages and disadvantages of Python and R to get a clear understanding of both technologies.

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 customisation options to the users. 
  • R 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.
  • R 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 decentralisation 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. 
  • Python 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. 

This was all about the R vs Python Tutorial. Hope you liked our explanation.


We have studied all the core features and differences in this R vs Python tutorial. Along with this, we have also learned why R and Python are good for data science and data analysis. After learning all this, we have also focused on the advantages and disadvantages of R and Python. I hope now you are clear with the difference between the two programming languages.

Now, it’s time to choose the best tool for Data Science Learning – R, Python or SAS

Still, if any doubts regarding R vs Python tutorial, ask in the comment section.

2 Responses

  1. Abdul Rawoof says:

    Excellent article for the people who wants to know about Data Science

  2. Kondisetti Anjanadevi says:

    really awesome your website all in one together really i just admire your work my request you to prepare tutorial for the shiny application too in r programming and NLTK,nlp implementation in R-programming like if i want to do text classification and video detection using R-programming ,if we have tutorials on these it will be very helpful to the people who use R -programming.

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