R vs Python | Best Programming Language for Data Science and Analysis

R is considered to be the best programming language for any statistician as it possesses an extensive catalog of statistical and graphical methods. Python on the other hand, can do pretty much the same work as R but it is preferred by the data scientists or data analysts because of its simplicity and high performance. R is a powerful scripting language and highly flexible with a vibrant community and resource bank whereas Python is a widely used, object oriented language which is easy to learn and debug.

So let’s move ahead with the comparison on R vs Python and have a look at the comparison factors.

Comparison Factors
R was introduced for data analytics whereas Python was developed as a general purpose language. The former is mostly preferred for hoc analysis and exploring datasets whereas the latter one is suitable for data manipulation and repeated tasks.

Let’s look at the  factors  we will be using for the  comparison  on  R vs Python:

Ease of Learning

R has a steep learning curve and people with less or no experience in programming finds it difficult in the beginning. Once you get a grip of the language, it is not that hard to understand.

Python emphasizes on productivity and code readability which makes it one of the simplest programming languages. It is preferable due to its ease of learning and understandability.

Speed

R is a low-level programming language due to which it requires longer codes for simple procedures. This is one reason for the reduced speed.

Python is a high-level programming language and it has been the choice for building critical yet fast applications.

Data Handling Capabilities

R is convenient for analysis due to the huge number of packages , readily usable tests and the advantage of using formulas. But it can also be used for basic data analysis without the installation of any package.

The Python packages for data analysis were an issue but this has improved with the recent versions. Numpy and Pandas are used for data analysis in Python. It is also suitable for parallel computation.

Graphics & Visualization

Visualized data is understood efficiently and more effectively than raw values. R consists of numerous packages that provide advanced graphical capabilities.

Visualizations are important while choosing data analysis software and Python has some amazing visualization libraries. It has more number of libraries but they are complex and gives a tidy output.

Flexibility

It is easy to use complex formulas in R and also the statistical tests and models are readily available and easily used.

Python is a flexible language when it comes to building something from scratch. It is also used for scripting a website or other applications.

Popularity

Now if we look at the popularity of both the languages, they started from the same level a decade ago but Python witnessed a huge growth in popularity and was ranked first in 2016 as compared to R that ranked 6th in the list.

Python users are more loyal to their language when compared to the users of the latter as the percentage of switching from R to Python is twice as large as Python to R.

The software companies have been more inclined towards technologies such as Machine learning, Artificial Intelligence and Big data which explains the growth in the demand for Python developers. Although, both the languages can be used for statistics and analysis , Python has a slight edge over the other due to its simplicity and ranks higher on the job trends.

Commercial softwares usually offer paid customer support but R and Python do not have customer service support which means you are on your own if you face any trouble. Both the languages have online communities for help though. Python has a greater community support as compared to R.

Now with this we have come to the end of the comparison on R vs Python. Both the languages give a head to head fight in the world of data science and data analytics. But Python emerge as the winner out of the two because of its immense popularity and simplicity in writing codes. You can click here to read more articles on Python.