R Vs Python? The Most Suitable Language for ML Regression Process



To have a clear picture of R vs Python, read on to know how the two are different from each other

Both R and Python are excellent languages for machine learning. They play a pivotal role in initiating and modifying algorithms in machine learning like classification, regression, clustering, neural networks, and algorithms in artificial intelligence. Simply put, these languages help in understanding the data better to make better-informed decisions. To have a clear picture of R vs Python, as to which of the two languages you should study, read on to know how the two are different from each other. 

Introduction to Python

Python is a multi-paradigm language developed in 1991by Guido van Rossum. It’s useful for web creation, software development, and system scripting, among other things. It can be used on a variety of platforms. It enables you to build data sets and SQL tables for use in your code. Python also organizes data and code into objects and provides several useful resources. 

Introduction to R

R is an open-source programming language used primarily by statisticians and data engineers to create various algorithms and techniques for statistical modeling and data analysis. In August of 1993, it first appeared on the scene. R has a large number of built-in libraries that include a broad range of statistical and graphical methods, such as regression analysis, statistical tests, classification models, clustering, and time-series analysis. 


R is widely used for statistical analysis. It makes heavy use of statistical models for this. On the contrary, Python is more inclined towards data wrangling. If the objective is data analysis or making use of machine learning in a scalable production environment, Python steals the show.


The codes in Python are robust and do not require much maintenance. They are clear, easy to type as well as easy to interpret as well. However, in the case of R, the codes do require maintenance.

Data Visualization

Data visualization is a medium through which the collected data can be understood in a much easier way. On that note, R supports a wide range of packages that pave the way for excellent data visualization. Though Python also supports certain libraries for data visualization, a point to note is that they are a little on the complex side.


Speed is one of those parameters that everyone pays considerable attention to. Talking about R and Python on this front, R is a little slower than Python but not to the extent that one cannot handle it.

Ideal for

Python is considered to be ideal for handling humongous data and building deep learning models. Talking about R, it is best for building statistical models as well as creating graphics and data visualization.

Ease of learning

If you are a beginner in the field of data science then there cannot be a better way to start with than opting for Python. Here, the learning curve is relatively linear and smooth. R might seem easy in the very beginning but as and when you proceed, you realize that the intricacies of advanced functionalities make it a little difficult to gain expertise.


Python doesn’t have as many libraries as R. However, a point worth mentioning is that there are many dependencies between R libraries. This might pose a problem during the learning period.

Data Analysis

For data analysis, packages have to be installed in the case of Python whereas R stands the potential to handle basic data analysis without any requirement to install packages.

With major differences between R and Python being spoken, it won’t be wrong to conclude that both of them are unique in their way and choosing one out of the two depends on the user, his objective, and the applications targeted.