R Programming vs. Python: A Comparison for Data Science

The premise

“My psychiatrist told me I was crazy and I said I want a second opinion.”

Late American stand-up comedian Rodney Dangerfield used the above as a running gag on his doctors. However, the line can be interpreted differently to throw light on how dearly we hold on to our opinions. From favorite sporting stars to political leaning, we invariably come across everyday personalities who go to great lengths to justify their beliefs.

When it comes to Data Science, there are several popular debates. Figuring out the relationship between Artificial Intelligence, Machine Learning, and Deep Learning continues to divide practitioners, with vehement backing based on sharp reasoning on either side. In a similar vein, it might be perplexing to explain the exact difference between the responsibilities a data scientist, a Data engineer, or even a Data Analyst performs.

An allegedly raging question implores professionals to reveal the “most apt/best/top programming language” for practicing data science. There is a long list of languages that have enjoyed the patronage of researchers, statisticians, and the sundry over the years. SPSS, Scala, SQL, Julia, Java, and Javascript are notable entries. Yet, Python and R are arguably the most illustrious entrants, with several listicles featuring them in the top two. This is where the argument commences.

So what?

Before enunciating the pros and cons of utilizing R or Python, let us reexamine the definition of Data Science. To state lucidly, we define Data Science as the art of manipulating data so that it is able to answer your questions. Manipulation requires using algorithms and ‘systems’. The systems that facilitate the realization of data science are numerous.

Though, the useful ones share certain common characteristics which are broadly explained below. We have also included comparisons between Python and R too so that you can decide the better one for yourself.

Tackling data tactfully

“Data” is no longer limited to a few lines of traditional data points/observations today. Even simple .csv files used these days may have millions of rows. The arrival of big data has catapulted media-like pictures and sound into the fray fundamentally, which was unthinkable even a few decades ago. Thus, languages that are able to import data from multiple resources are bound to have more takers.

Python purportedly triumphs out R on this facet, with libraries like pandas that are built for the purpose. While .csv and excel files, SQL databases, and other traditional forms are workable for both, the former supports advanced forms of retrieval like crawling and advanced web scraping tool. Users also find Python more suitable for data wrangling (also known as EDA).

The velocity, volume, and variety of data thus require a greater ability to clean it. Similar capacity is required when it comes to visualizing the data, where experts generally prefer “graphs that speak”. This is an allusion to interactive and dynamic representation. After all, the age-old adage goes as, “A picture is worth a thousand words”.

R is pronounced as a clear winner by many as its visualizations are considered to be suitable for even building dashboards. Python, on the other hand, has come up with libraries like Matplotlib and Seaborn. However, it is reported that their charts and graphs pale in appearance and are convoluted, when juxtapositioned with R.

Also, analyzing this voluminous data calls for unprecedented speeds. For instance, TensorFlow, a cutting-edge deep learning library, employs GPUs along with CPU(s), to provide increased processing power and training speed for dealing with the aforementioned complexity. This state-of-the-art package is implemented in Python, which in itself tells a story.

Lingual Aspects

Non-proprietary software, which can be modified by the online community, has been able to grow by leaps and bounds, especially after the turn of the century. Open-source software is also guaranteed to remain free for personal use, which enables new learners to come into the fold and enhance the usefulness of the concerned application.

This may be especially pertinent to explain why MATLAB, a paid software, has not garnered a lot of takers despite being powerful. It also explains why R and Python lead the race, having had hundreds of libraries included since their initial releases, for addressing changing demands of practitioners. Dynamic online communities have played in their favor while ensuring usage all over the globe.

The CRAN (Comprehensive R Archive Network) stands as proof of how R has persisted in fulfilling its goal of being the go-to tool for statisticians and researchers, for over three decades. It boasts of having over 10,000 packages, wherein ggplot2, data. table, dplyr, zoo, caret, and Shiny are cited among the most useful ones.

On the other hand, Python, being a multipurpose language like C++ and Java, had a comparatively slower foray into the domain. However, it has caught up in recent years by coming up with statistical and Machine Learning libraries that include StatsModels, Scikit Learn, NumPy, Pandas, Matplotlib, and Seaborn.

Further, programming languages that are easier to learn and understand are typically adapted more than those which are meant to address niche requirements of a specific industry. Consider learning to write a natural language (English, Telugu, Hindi, etc.) that you already know how to speak. Compared to learning a totally new language, this is obviously easier.

In the same way, Python resembles everyday English. Building upon commands like for and while from C/C++/Java, it went on to include the likes of is not, in, if, and, or, and except. Further, the advanced libraries stated above employ methods with intuitive names: read_csv(), fit(), summary(), compile(), etc.

This makes it easier to learn the “syntax” (analogous to grammatical rules for a natural language), which lets you get to the real deal (building and deploying models on datasets) quicker. Compared to this, R’s syntax is widely dubbed to be a considerable impediment for complete beginners, which translates to a steep learning curve in the beginning.

A related factor is the nature of the IDE that enables users to leverage these cutting-edge applications. RStudio is the most widely-used one by proponents of R. Python, on the other hand, has no unanimous winner, with Jupyter Notebook, Spyder, Rodeo, etc. being the most renowned ones.

Conclusion

After taking the above factors, we conclude that the most useful programming language for Data Science is:

The humor aside, the answer depends on the application you seek. R has built a reputation for being an “ingroup” for statisticians and researchers that have included wide-spanning applications like genome sequencing, finances, banking, customer behavior analysis, etc. It is also considered the language of Data Science as Python started catching up only a decade back. Thus, with its enormous library of packages, it is more suitable for distinct applications.

Python tends to be helpful in multiple use cases, with web development, app development, and game development being invaluable additions to it enabling Machine Learning and Data Science. Even if you are focusing only on the last bit, the language helps you in building models from scratch more efficiently. All of this has prompted us to devote our course to learning Python and its libraries.

In the end, from simple scatter plots and regression curves to Machine Learning, both Python and R are equally able. Quite a lot of learners also go on to familiarize themselves with both languages. Hence, for various corporations, the answer has been a judicious mix of the two based on their unique needs.

However, recent trends have indicated that Python is turning out to be the preferred first choice for beginners, not only for Data Science but also as a programming language too. So, take a deep breath and plunge into the Python universe!