Introduction of R:

R is Basically a Programming Language as well as an environment for statistical computing and graphic purposes. It is very similar to the S language and environment which was in point of fact developed at Bell laboratories. However, R is a GNU project. Some experts consider R as a different implementation of previous language S.

However, there are some crucial differences which make the coding process of S utterly different from the coding process of R. Wide variety of statistics including linear and nonlinear model, time series analysis, classification, clustering, classical testicle test and many other variations along with graphic designing techniques are offered by R.

R provides an open-source route to participate in the activity of choice for research in statistical methodology. R comes with a lot of strengths, but the biggest one is the ease of excellent design Publication quality plots. Users can include and use mathematical symbols and formulas whenever they needed. Every minor design has been taken into consideration, and great care and attention have been provided to every graphic in the environment of R.

R is free software, and it comes under the terms of free software foundation’s GNU general public license in source code form. It runs smoothly on a Variety of UNIX Platforms that may include free BSD, Linux, Windows, and Mac OS.

Introduction of Python:

A python is an object-oriented high-level programming language easily interpreted, including Dynamics semantics with a high-level build-in data structure for the users. Python comes with dynamic typing and dynamic binding, which helps in making the Rapid Application Development very attractive for the users. It also focuses on scripting or glue language to connect existing components together smoothly.

Users described Python as easy, simple to use, convenient to learn, syntax emphasized readability that leads to the reduction of cost of program maintenance. Python also happens to spot modules and packages, which helps the users in reusing the code and modules for future purposes. The extensive standard library and the python interpreter are available in a source or binary form without any sort of additional charges for all major platforms. And it can be freely distributed according to the wish of the users.

Python works on increasing the productivity of the procedure for its programmers, which acts as a reliable source of attraction for the programmers. Python does not work in the form of compilation steps, the edit test debug cycle is the primary work frame of Python, and that is incredible is fast.

Difference between R and Python:

Both are open source programming languages and has a huge fan following. A new set of tools and libraries are added on a regular basis to update these open source programming languages for users.

Programmers looking for statistical analysis go towards R whereas a user whose primary approach is towards data science is inclined towards Python. R and Python are considered as Art in Terms of Programming Language. The learning procedure of both of these programming languages is considered as ideal solutions. But these programming languages require a specific time investment. And most of the programmers do not have this luxury to offer.

Python is a general-purpose language that comes with syntax readable. On the other hand, R is built by statisticians, and they encompass their specific language for the programmers.


It’s been two decades that Academics and statisticians have developed R for regular use purposes. In these two decades, R has reached its full potential, and it is now considered to be one of the richest ecosystems to perform any sort of data analysis. There are around 12000 packages which are being offered by R, and it is available in open source repository CRAN.

Un-imaginary and the considerable number of modules and libraries available in the R it is quite possible to find any sort of library for whatever analysis you are performing. The wide variety of libraries that are being offered by R makes it the first choice of programmers for any sort of statistical analysis. The output of the product of any statistical analysis is the main cutting-edge difference between R and all other products available in the market.

There’s a fantastic set of tools that are available in the R, and they are being used for communication purposes, and that makes the results produced by R even more acceptable and functional for the programmers. The creators have created R with specific libraries, and they wrote the packages with Elegance and ease of the users had been the main priority. The process of documenting and as well as presenting the findings is straightforward using the programming language of R.


Engineering feature, selection web scraping application, data wrangling, and many other features which are being covered by R are also the main task offered by Python. Python actually works by helping in deploying and implementing machine language at an immense scale for the users. Python codes are relatively easy to maintain and are more convenient to robust than R.

In the past Python was not doing so well and it did not have many options for data analysis and machine learning libraries. But with the constant additions in the libraries and the modules provided by the Python, now it is catching up in the race.  It gives some cutting-edge APIs for machine learning or artificial intelligence. It just not makes Python one of the most popular programming languages, but it also helps it in reaching a higher state in the market as well.

Most of the data science jobs can be done quickly done with the help of Python and libraries such as seaborn, sci-kit learn spicy Pandas and numpy. Python makes accessibility and reliability extremely easy as compared to R.  If you are in need to use the results of your analysis in an application or any website then using R is not going to be very helpful for you. However, Python can fall into the category of the best choice.

Significant differences between R and Python:

In the 21st century, there has been a massive growth in the importance of substantial data machine learning. As well as data sciences in the software industry of the software service companies have boomed a lot. Two languages are being considered as the most favorite of all the developers. R and Python are considered to be the most popular and quite favorite languages of data scientists and as well as a data analyst for doing the work in peace and ease.

In terms of satisfaction, R is considered to be the best programming language. Because it has an extensive catalog of statistical and graphical methods, which gives a lot of options to the users to work with the best.

On the other hand, Python also does not fall back; it even a possessive great deal for a data scientist or data analyst. Because of its simplicity and high performance, all the data scientists are more inclined towards Python now. Both the programming languages are free and open-source, and there have been two decades since they are working on their credibility.

Even though both programming languages are considered to be the best, but still there are some differences between R and Python, which makes the languages different and unique for different users.

Status Index:

The IEEE spectrum ranking is a primary parameter that actually defines in quantity about the reputation of a programming language in the market. According to the statistics of 2017 Python was on number one whereas in 2016 it was 3rd on the third position whereas the ranking of R is on the 6th place.

Job opportunities:

According to the research done in 2012 to 2016, SQL stays at number one. Whereas it is followed by Python and then Java in terms of data sciences by programming languages related jobs whereas, the number of R falls at number 5.  Comparison relationship between the job trends and opportunities for the programmers and the users for Python and R has shown that Python has always been on top as compared to R.

Comparison of analysis between R and Python:

If you look into the relationship between the percentages of data analysis jobs related to R and Python, then it quite clear that R is always a step ahead. Apparently, R is considered to be one of the best tools.

Percentage of people switching:

Python users are considered to be more loyal than R users. As the percentage of R users switching to Python is almost twice as large as compared to the number of the people switching back from Python to R

Ease of learning:

R has a steep curve when it comes to the comfort of learning. People without experience and appropriate knowledge find it extremely difficult in the beginning. However, once you get the hold of the language, it is not that hard to use learning. Python is predicted to be relatively easy than R. as it Emphasis on productivity and code readability, which makes it one of the simplest programming languages available for the users.

Beginners and the people with less experience preferred Python over R. because R is for more experienced developers, and that just comes with learning and understand-ability.

Speed of programming languages:

The difference between R and Python is also represented in the form of speed of these languages. Speed of R is almost twice as much as Python. Because it takes a longer time to load 4.5 gigabytes.CVS files as compared to the python Pandas. However, Python is a High-Level Programming language, and it has been the choice of most of the developers to develop and build any critical and fast application.

Data handling capability:

There are several packages, readily practical test, and the advantage of using mathematical formulas available in the libraries of R for the purpose of data handling. As well as for the use of fundamental data analysis, no sort of installation is required of any package. That acts as a massive plus for the users of R.

Python also doesn’t fall really back; it offers readymade initial stage packages for data analysis. It has also improved over the period of time in the recent versions Numpy and Pandas. With those updates, Python has become an imperative programming language for suitable parallel computations and as well as proper data handling capabilities.

Visuals and graphics:

All the programmers and designers accept this thing that one picture is equal to thousands of words, and visual representation attracts the audience in way better manner. Visual is understood more effectively and more efficiently than any sort of raw value provided in the context for the users. R offers numerous packages that contain advanced graphical capability and relatively less costly modules that attract users. Consumers, as well as programmer, have the potential of using the customized graphs and other tools according to their own wishes.

Now visualization is considered to be essential for any sort of data in data analysis software, and Python has also understood that need of the time. It has also updated its visual libraries. Python is now providing many more modules, and it has more libraries as compared to the R., but if we dig deep, we will come to know that they are very complex and but eventually give tidy output.

Deep learning support:

Now the world is focused on deep learning, and it has hit the new Heights of popularity.  With this outline, the R community is also being updated, and they have introduced two new packages to fulfill the Desire of deep learning for its users. Now both the packages which are provided by R interface to the python deep learning packages.

It is a high-level neural network API, and they have the capacity of running either on tensor flow or Microsoft cognitive toolkit. And these are being written by Python for users. In order to get familiar with deep learning, Python has introduced Keras, which is the easiest way to get them familiarized. This also explains why kerasR and Keras package is so popular among the users.


Difference between R and Python is relatively clear, but when it comes to flexibility, both of the languages are easy to use. However, they are some complicated formulas in the R and also statistical test, which may make some of the users nervous. But for the people who are here just for the purpose of using the language to its full potential, they find this module and readily available libraries really easy to use and very favorable for them.

Python is also one of the flexible languages available, and when it comes to something new or building form from scratch, Python is your go-to. It is also one of the most commonly used languages for scripting a website or any other application by the programmers.

Code repository and libraries:

Comprehensive R archive network CRAN is one of the best repositories of R packages available for the users. It is effortless to contribute to code repository and libraries. The package which is being introduced by R has different functions, data, and compiled code which can be easily installed in just one line. And then the users have the power of using it was full potential. It also has a very long list of popular packages which can have the programmers to take advantage of R.

Python is also very popular, and it consists of the pip package index for data repository and libraries. All users can contribute to pip. However, it is a complicated process for them. So it is not being preferred by most of the users as the task can be really tiring as well as intricate, so the users prefer R over Python here.

The popularity of Python and R:

When you are digging deep on the differences between r and Python, looking at the popularity of both of the language can make the distinction very clear for the users. Both of this language started at the same level almost two decades ago. However, the things that happened in favor of Python as compared to R. As Python was ranked as Number 1 in 2017, whereas the ranking of R was 6th.

It has just not made the python language more desirable for the users, but the percentage of people switching from R to Python has also been increased very largely.


After looking into the difference between R and Python, it is your choice to decide which programming language falls under the category of your choice. You need to ask yourself a few questions before getting into any sort of dangerous statically function in the data sciences.

Do you want to learn how the algorithm works, and do you want to deploy the model? If the answer to both of your questions is yes, then I would suggest Python would help you a lot. Because it is relatively effortless to use for the beginners. And it will also provide you a significant number of libraries to manipulate Matrix or the code the algorithms according to your own wish. It is also the preferred programming language for the programmers and the coders to develop any sort of model from scratch. As it offers all the helping functionalities for the program.

If you are willing to have your hands-on data analysis right away then both R and Python are going to be very helpful for you. If the primary purpose of your project is the statical method than focusing on R is going to be a huge plus. Writing any sort of report, creating a dashboard will also be a considerable help offered by R.

But on the other hand, the deployment and reproducibility offered by Python are very quiet helpful for the users. The statical gaps between R and Python are really close. This makes the difference between R and Python not that clear for the beginners. But if you know what you are doing and you have the grip over your task, then you will be good to go.

In the end, the choice is always yours. But ask yourself questions before diving into the selection of any of the programming language for your project. The questions might be

  • The objective of your mission, either it is for study analysis or deployment
  • What is the time limit you want to invest in your project?
  • And what sort of set of tools your company or industry mostly use

After asking these questions, the selection of any of the programming language, i.e., R or Python, would be apparent in your mind.