There is a very hot debate on the topic of which language is more suitable for data science: R or Python. The answer is both. People often get confused by comparing the features of R and Python, but we need to understand that features alone cannot define the suitability of any language. Both R and Python have their own specific features suitable for data science and analytics applications. There may be some situations where one language is more preferred than the other, but it does not mean that the other language is useless. (To learn more about data science, see 7 Steps for Learning Data Mining and Data Science.)

What Are R and Python?

R is an open-source language which was developed during mid-1990s as a variation of the S language. It was developed by Robert Gentleman and Ross Ihaka. It was designed in order to streamline the programming experience. Nowadays, it is being used extensively for research, enterprise and academics. Due to its usage in many fields, it is one of the most popular statistical programming languages. It is quite simple to use, but it can be a little difficult for those completely new to programming. However, they can learn more from the different resources available on the internet.

Python was created during the early 1990s by Guido Van Rossum. It focuses on ease of coding and more adaptability. Python is being widely used by those programmers who want to have greater control over the codes they make for faster and more efficient data analysis. It is also for used for special statistical techniques in their code to make it work even faster. The programming language is very easy to use and learn. It is also very flexible and can be used to create what exactly the user wants to create.

How They're Different from Other Languages

The work of data analysis is very important and the process must be flexible. For this, the process must be very interactive so that it remains efficient. However, the language must also be very flexible, interactive and easy to use. R is a very flexible language. While other languages are used for some exact purpose and cannot work for anything else, R can actually work for a range of purposes, especially in the scientific fields.

Another thing that differentiates R from other statistical programming languages is its interactivity. R has a very powerful mechanism which can be used for quickly creating data structures. R is also a very powerful graphics medium, unlike textual programming languages; graphics are very useful, especially in the field of statistics and data analysis. R can be used to produce many different types of graphs easily.

Python is also an excellent choice for data analysis. It is very adaptable when compared to languages like Perl or Ruby, as it can be customized by the use of modules. It has many features too. It is also a graphical language, which allows it to have visual libraries and that helps to visualize graphs and statistical data easily. Another thing which differentiates it from other languages is its easy-to-use syntax. (For more on programming languages, see Scripting Languages 101.)

Why They Are Used in Data Science Applications

Data science is one of the most important fields of science nowadays. Without this, it is nearly impossible to predict anything, and accurate prediction is the base of today’s society. Thus, the best tools are required for data analysis, which is a crucial part of data science.

R and Python both have many features which make them suitable for data science. However, which one you should use depends entirely upon your own preferences. R is perfect for graphical representation of data and Python is extremely easy to use.

What Are the Advantages?

There are many advantages of both R and Python. One of the greatest advantages of both these languages is their graphical visualization system. R supports many professional-grade visualization packages like googleVis, ggvis and rCharts. These packages can be customized to make perfect graphical representations of the statistical data. Python also has many powerful visualization libraries like Pygal, Seaborn and Bokeh.

A thing that makes R so useful is its ecosystem. Both of these languages have an ever-active community which is always happy to help, and both these languages are being constantly updated, in order to accommodate new features and technologies. These languages are multipurpose tools which are very easy to learn.

Use Cases for R and Python

There are many use cases of both R and Python for data analysis. For example, ForecastWatch.com collects data from different weather forecast sites and rates the sites according to their accuracy. This allows better weather forecasting and allows weather forecasters to compare their accuracy with others. Python was used for every component of this service due to its flexibility, which comes from its ability to use many standard libraries.

Another use case of Python is that it was used to power social networks for EZTrip.com and Gusto.com. They required a system to help their customers report on their journeys while improving their online booking system. While their existing booking system already worked quite well, it couldn’t handle multiple requests efficiently. However, since Python was used, it became much faster due to better data analysis and management facilities. This further helped them create a better user interface based on the user’s queries.

R is also being used in many places like social networking sites and crowdfunding sites. R’s visualization ability is making it the favorite of many data analytics organizations too. R is currently being used in the ANZ Bank for analyzing the risks of crediting. Facebook is also using R to analyze large numbers of status updates.

Future of R and Python in Data Science

R and Python are going to have a very bright future in data science. Both these open-source programming languages are very powerful and are being developed and updated regularly by an active community. Thousands of organizations, both new and old, are quickly turning to these solutions as they are free and very customizable. These are replacing other languages used in data science at a furious pace.

Conclusion

Many data scientists wonder which language is better for data analysis, R or Python. Both of these programming languages are very popular and are strong in their own fields. They have their own pros and cons, so people must decide which one to choose in order to get the best out of their data. However, they forget the fact that both of them can be used to analyze data easily.