Both are scientific programming languages ideal for performing statistical analysis on the data. R and Python, each has its own importance in the IT world. In fact, according to many job portals, the companies require both R and Python skills. Therefore, is Python certification mandatory even if you are proficient in R? Let’s see where and why each of the programming languages is used.

What is R used for?

R is popular with statisticians, data analysts, marketers, researchers who perform data wrangling after retrieving, cleansing the data, then visualizing and presenting it to the stakeholders. 

What makes R so popular?

R is a highly adopted programming language used by tech giants to perform statistical analysis. Fortune 500 companies like Google, Microsoft, Twitter, Ford make use of R for various reasons. For one, R is a free, open-source language that runs on all operating systems. 

Is it easy to learn? This is a tough question to answer. But, without an exception R is the first programming language to learn to perform statistical analysis. 

That said, programmers who have worked on Python or PHP get out of tune as they find R a little away from the usual coding techniques.

As R is used predominantly for analyzing data tables and matrices, you will hardly find working on for loops or other conditional statements.

What is Python used for?

Python, on the other hand, is a jack of all trades. Python is also widely adopted by top companies such as Wikipedia, Google, Yahoo!, NASA, and many more.

It is used as a scripting language for developing web applications, for developing games, but most commonly used for scientific and mathematical computation, in the fields of Data Science, Artificial Intelligence. 

Python is a relatively easy to pick-up programming language, extensive libraries that can help the programmers take a pick of the library, specifically for their application.

What are the key differences between Python and R?

Wondering which is better – Python or R? To understand which is better for statistical analysis, we will have to first figure out the strengths and weaknesses of the two programming languages.

To start with, it is better to set the expectations right. Either R or Python is ideal to tackle any data science task. Data manipulation, exploring data sets can be handled by both languages very efficiently. 

In the initial stages of the data analysis, the programmers might want to leverage both the programming languages for the best results. For example, performing earlier stages of data analysis and exploratory analysis using R, then switching to Python for subsequent analysis.

So, choosing between the Python and R depends on the factors such as, what programming language is being used in the company, by the colleagues, which programming language are you most comfortable with, etc.

The following are the influential factors that can help you determine which programming language to choose between R and Python.

  • Commonly used tools at your workplace
  • Different problems you are looking to solve 
  • People with a coding background might find adapting to Python easily than R.
  • Coding is simple in Python.
  • One can use different ways to achieve the same results using R. Basically, R offers flexibility in coding.
  • While Python has huge community support, so does R.
  • R is well-known for producing beautiful visualizations and graphs
  • Python has the ease of deployment.
  • R is great for statistical analysis while Python is great for mathematical computations.
  • R is great for data analysis without the need for installing the packages. Python, on the other hand, needs to have the packages installed.
  • R has better libraries known as Tidyverse which provides easy to learn tools for importing data, manipulating data, creating impressive visualizations, and reporting.
  • Python is popular for AI and other applications where R’s functionality is limited.

Should one learn Python if they know R?

At the moment R is far ahead of Python where performing data analysis is concerned, however, Python libraries are constantly evolving and are more likely to catch up with R.

However, as already mentioned R is limited when it comes to its application in Artificial Intelligence, deep learning techniques. 

Moreover, Python is not just meant for statistical analysis but its application is widely spread from Data Science to testing to AI, gaming, and many more.

Concluding the discussion, while R dominates in the data science niche, Python is ubiquitous in its application in several other fields.