Quick look to R vs. Python
Tags: datascience, python
Published: 2022-01-23 23:42:24

Since the beginning of my journey in the Data Science field, I’ve programmed my projects with help of **Python** programming language. But I was aware that another popular language related to Data Science is **R**. Recently, I’ve started my research on what are the main differences, similarities, and main uses of those two languages. And here is a quick look at what I’ve learned.

## Overview of languages

### R

- **R** is a **statistical language**. It is used for statistical software development and data analysis. 

- R has many libraries for creating **dynamic and interactive graphs.**

- The development of R began as a research project by New Zealanders **Robert Gentleman and Ross Ihak**. In 1993, the first binary versions of R were published in Statlib, an archive of statistical software and datasets.

- R has its own **open-source CRAN** (Comprehensive R Archive Network) repository. There are over 16,000 packages available in CRAN for analytical tasks.

- R is a **command-line language**, but there are several IDEs that provide an interactive GUI.


### Python

- Python is a multifunctional language that can be used in web development, software development, system scripting. 

- It is among the ten most commonly used programming languages.

- Python was developed by the Dutch programmer Guido van Rossum in 1991. The name was inspired by the TV show Monty Python's Flying Circus. 

- The language is designed to be easy to read and has similarities to English. 

- Python uses dynamic typing (a variable is bound to a type at the time a value is assigned).

- Previously, Python lacked libraries for data analysis and ML. Now, it provides APIs for creating artificial intelligence.

- Most data processing tasks can be solved using five Python libraries: Numpy, Pandas, Scipy, Scikit-learn, and Seaborn. 

- It is suitable for those who want to use the results of calculations in an application or website.


## What is the difference?

- R was created for statistical tasks and data analysis, while Python is more versatile.

- R is great for complex visualizations, as opposed to Python.

- R is difficult to integrate into a production process, while Python can easily become part of the product.

- Python codes are easier to maintain and more robust than similar algorithms in R.

## Benefits of each

### Pros of Python

It is common to hear that it is easier for beginners to learn Python. The R language has a rather steep "learning curve" because statisticians created it for themselves. Python has a simpler syntax.
Compared to R logic, the coding syntax in Python is more in line with how people think. Therefore, Python is easier to "translate" into other programming languages.

### Pros R

The language is command-line oriented, but many use the RStudio or R commander environments. These IDEs have data editors, debugging support, and a graphics window. Python is partially closing this functionality with Eclipse and Visual Studio.
R is designed specifically for data visualization. Rendering in Python is more confusing, and there are fewer libraries to choose from - only a few dozen.

## Which language to choose?

To understand what language you need, first, decide what problems you want to solve, how the results should be presented, and do you plan to deploy your model.

If you are planning to work with massive amounts of data, build deep learning models or perform non-statistical tasks – **Python** would be a better option. And if you are going to build statistical models and require better visualizations, then **R** would be just what you need.