R vs Python

R Vs Python: What’s the Difference?

R and Python are both open-source programming languages that have a big user base and a significant developer community. Libraries and tools are constantly being updated and added to their respective catalogues. Data science is primarily accomplished through the use of R, although Python provides a more generic approach to data science.

Data science programming languages such as R and Python are considered to be the cutting edge of their respective fields. Of course, learning both of them at the same time is the best solution. The use of R and Python necessitates a time commitment, and not everyone has the luxury of such time. Python is a general-purpose programming language with an easily understandable syntax. R, on the other hand, was developed by statisticians and includes their special language.


R has been developed by academics and statisticians over the course of two decades. R today has one of the most diverse ecosystems for performing data analysis on the planet. There are approximately 12000 packages accessible in the CRAN repository (open-source repository). No matter what type of analysis you want to conduct, you can find a library that will accommodate your needs. R is the preferred choice for statistical analysis because of its extensive library, which is particularly useful for specialised analytical work.

The output of R distinguishes it from the other statistical programmes on the cutting edge of technology. R provides excellent tools for communicating the results. Knitr is a library that comes with Rstudio. This package was written by Xie Yihui. He made the process of reporting simple and attractive. It’s simple to communicate the findings through a presentation or a document, too.


Python is capable of doing many of the same activities as R, including data wrangling, engineering, feature selection, web scraping, app development, and so on. Python is a programming language that is used to install and apply machine learning on a big scale. When compared to R, Python code is easier to maintain and more resilient. Python didn’t have many data analysis and machine learning libraries when I first started using it. The Python programming language has recently caught up and now includes cutting-edge APIs for machine learning and artificial intelligence. Numpy, Pandas, Scipy, Scikit-learn, and Seaborn are five Python libraries that can be used to complete the majority of data science tasks.

Instead than making replication and accessibility difficult, Python simplifies the process significantly. If you need to incorporate the outcomes of your analysis into an application or website, Python is the programming language of choice.

Popularity index

It is possible to measure the popularity of a programming language using the IEEE Spectrum rating, which is calculated using many indicators. The left column displays the ranking for 2017, while the right column displays the ranking for 2016. After finishing third the year before, Python ascended to first spot in 2017. R is currently in sixth place.

Possibility of Employment
The chart below depicts the number of data science-related jobs available in each programming language, organised by language. SQL is far ahead of the competition, and it is followed by Python and Java. R is ranked 5th.

R vs Python as a potential job opportunity
We can observe that Python (in yellow) is more frequently mentioned in job descriptions than R (in blue) if we concentrate on the long-term trend between the two languages.

R and Python were used to conduct the analysis.
However, when it comes to data analytic jobs, R is by far the most effective programme available.

Analysis done by R and Python

The percentage of people who make the switch
There are two important items to note in the illustration below.

Difference between R and Python

Parameter R Python
Objective Data analysis and statistics Deployment and production
Primary Users Scholar and R&D Programmers and developers
Flexibility Easy to use available library Easy to construct new models from scratch. I.e., matrix computation and optimization
Learning curve Difficult at the beginning Linear and smooth
Popularity of Programming Language. Percentage change 4.23% in 2018 21.69% in 2018
Average Salary $99.000 $100.000
Integration Run locally Well-integrated with app
Task Easy to get primary results Good to deploy algorithm
Database size Handle huge size Handle huge size
IDE Rstudio Spyder, Ipython Notebook
Important Packages and library tidyverse, ggplot2, caret, zoo pandas, scipy, scikit-learn, TensorFlow, caret
Disadvantages Slow
High Learning curve
Dependencies between library
Not as many libraries as R
  • Graphs are made to talk. R makes it beautiful
  • Large catalog for data analysis
  • GitHub interface
  • RMarkdown
  • Shiny
  • Jupyter notebook: Notebooks help to share data with colleagues
  • Mathematical computation
  • Deployment
  • Code Readability
  • Speed
  • Function in Python

R or Python Usage

Python was created by Guido van Rossum, a computer programmer, in the late 1990s or early 1990s. In the fields of mathematics, statistics, and artificial intelligence, Python has a number of significant libraries. Python may be seen of as a pure player in the field of Machine Learning. Python, on the other hand, is not completely developed (yet) in the areas of econometrics and communication. Python is the most effective tool for Machine Learning integration and deployment, however it is not the most effective tool for business analysis.

However, the good news is that R was created by academics and scientists. It is intended to provide answers to statistical challenges, machine learning difficulties, and data science problems. Because of its powerful communication libraries, R is the ideal tool for data science. Furthermore, R comes pre-loaded with a plethora of tools for doing time series analysis, panel data analysis, and data mining. On top of that, there aren’t any better tools available when compared to R.

If you are a newbie in data science with the requisite statistical basis, you should ask yourself the following two questions, in our opinion:

What if I don’t want to know how the algorithm works?
Do I wish to put the model into action?
If you answered yes to both questions, you’d most likely start with learning Python as a starting point. For example, Python has excellent libraries for manipulating matrixes and coding algorithms, to name a couple of examples. As a beginner, it may be more convenient to understand how to construct a model from scratch before moving on to the methods provided by machine learning library functions. For those who are already familiar with the algorithm or who wish to go immediately into data analysis, both R and Python are acceptable choices to begin with. If you’re going to be concentrating on statistical methods, R has several advantages.

For the second time, if you want to perform more than just statistics, such as deployment and repeatability, Python is a better option than R. If you need to generate a report as well as develop a dashboard, R is a better fit for your job.

Short and sweet, the statistical gap between R and Python is narrowing as time goes on. The majority of the work can be completed in either language. You should select the tool that best meets your requirements as well as the one that your colleagues are utilising. It is preferable if you all communicate in the same language. Learning a second programming language is easier if you have mastered the first one you learned.


In the end, the decision between R and Python is based on the following factors:

Your mission’s objectives are as follows: Statistical investigation or application
The amount of time you have available to devote
The most frequently used tool in your firm or industry