Python vs R. Choosing the Best Tool for AI, ML & Data Science

The question “what coding language is better?” — is not about aesthetics, it is about making the appropriate decision. But if start looking for the answer, you will probably stumble upon a whole slew of discussions and so merely information regarding what exactly language is impeccable. The ultimate truth is, there is no answer.

Coding languages are versatile. Each one is unique, presenting specific features you probably not find anywhere else. You can only find out information, carefully analyze it, sample one or another language. Only then you can decide what is best, or otherwise, that none of them fulfills your needs. By the way, the letter one option was the main motivation to generate new languages like Python by Guido van Rossum or C++ by Bjarne Stroustrup.

So what’s this all about then? Today, when living in a fast-developing age of AI, ML and data science, there is no time for long consideration. Programming is relatively like playing chess. It is effortless to learn the rules but challenging to become an expert. Here is an extensive analysis of the two most favored coding languages. This will help to understand what to bet on.

DDI Editor’s Pick — Data Science by John Hopkins

R and Python: Strong and Weak Points When Coding for Data Science

Battling for the title of the best data tool, these two contestants also have its strengths and weaknesses. The choice counts on the specific situation, the cost of training, or what other standard tools are required to resolve the problem. You may be little known to Data Science and coding, or need to prefer one excellent choice on a project, this guide will undoubtedly support you.

Disclaimer: You can undoubtedly meet other opinions regarding which language is better. Personally, I do not claim to be a super pro in coding on Python and R, so don’t be extremely critical. Yet, I am close to the data science field, and I was passionately interested in the titanic struggle between these languages. Hence, I’ve made a huge and unbiased analysis that will simplify your search.

What Is R? It’s free, open source, powerful and highly extensible coding tool. It was initiated exclusively for scientific data by Ross Ihaka and Robert Gentleman in 1995. R owns an all-inclusive catalog of statistical and graphical methods. Although it is not granted by academics, many major corporations like Uber, Airbnb, and Facebook employ it.

When to use R. It is superb for extensive research scientific data and broadly appropriate for almost any preferred option. On top of that, it has myriads of standard packages and ready-made solutions.

How to start working with R. It is advisable to begin with installing IDE RStudio. Next, I recommend use packages like dplyr, plyr, and data.table for simplifying package manipulations. In case if you need work with data visualization then go with ggvis and ggplot2. Plus, caret package is good for machine learning.

R Strong Points:

  • For basic data analysis, you are up to use it without installing additional packages. Large numbers of the functions are included. Testing statistical hypotheses often take only a few lines of code. But, for largest data sets, packages like data.table are mandatory.
  • Installation of IDE (RStudio) and the necessary data processing packages is extremely simplified.
  • Compatibility with different platforms and operating systems. You are also up to import data sheets from other tools such as Microsoft Excel.

R Weak Points:

  • Difficult to learn and easy to code badly. Weak typing is dangerous, functions have a nasty habit of returning unexpected type of objects.
  • Specificity in comparison with other languages. For example, vector indexation begins with one instead of zero.
  • The syntax for solving some problems is not quite obvious. Due to a large number of libraries, the documentation of some less popular ones cannot be considered complete.

What Is Python? Developed by Guido Van Rossum in 1989, now it is applied for the Google, YouTube, NASA and more. In scientific words, it is an object-oriented, high-level tool with integrated dynamic linguistics. It is publicly usable and makes answering trouble with scientific data almost as simple as writing out your considerations about the solution.

When to use Python. It is perfect to quickly plunge into data science both for coders and newcomers. The simple syntax makes it easy to write and debug code. This tool comes in handy when data analysis tasks are added to the work of web applications.

How to start working with Python. Be certain to set up NumPy / SciPy for scientific procedures and pandas for data manipulation). Plus, look at the matplotlib library for typically making graphics including scikit-learn for machine learning.

Python Strong Points:

  • Popularity. Developers have more opportunities in career.
  • Carrying out not only data processing but also their search and use of the result of processing in a web application.
  • All functions are presented.
  • Short and clear syntaxes.
  • The high speed of operation and comfortable interface.

Python Weak Points:

  • The lack of a common repository and the lack of alternatives for many R libraries.
  • Because of dynamic typing, sometimes it is complicated to search for some functions and to track faults connected with the incorrect assignment of different data to the same variables.

Why Python and R and Not Other Languages?

There are literally masses of other languages that you can use for data science, but, in comparison with R and Python, they are slightly not so well-suited and general-purposeful. Let’s consider them in more detail.

Java. Good for visualization libraries or data exploration. Excellent for the data engineering. Yet, lately, Java has been challenged by new languages. Now, it is unfavored because of perceived performance issues due to the JVM adding a layer between the execution code and the hardware. It is not at all pleasant to work.

Scala represents another effective tool that you may aspire to think about. It deals perfectly with handling big data pipelines. But, it is not a good tool for small or middle projects. Or what is more, for newbies.

Why not SQL? Well, it is not a tool you could or would want to use for data science. The occasion is it has a limitation to filtering some columns or grouping category. It also can’t handle any serious data wrangling, cleaning or modeling tasks.

kdb+/q. More precisely, q is the coding language, and kdb+ is a database. This is not a general-purposeful tool for scientific data and operates typically for financial companies. Although it can carry out key operations, it is difficult to work with.

If digging even deeper, you will find other options like Julia, SAS, F#, MATLAB… and the list is never-ending. Taking into account the latest surveys, it became clear that R and Python are on top. Why so? Both of them are open-source data platforms with active, growing communities. They frequently taught as a first language (especially Python) and were designed from the start to be a data analysis tool.

Final Verdict: What Coding Language to Choose?

Before making any conclusion, you should also understand a language remains just a tool in a programmer’s hands. Naturally, it is substantial to manipulate it confidently to generate a superior solution. But, in the first place should represent the developer’s skill.

Now, briefly summing up all the above analysis, we have the next results:

R: not bad for mathematical modeling, research, plotting, and scientific analysis. If you stand in need of hacking into the laptop and enhance spreadsheet and ML-crafts, go with this language.

Python: Great for any project and particularly for all range of startups to construct patterns and analyze data. With Python, you get more detailed functionality. So, if you are organizing activities that demand rapid outcomes, choose Python. It is a complete coding language, whereas R does not have an equal level of functionality.

Bonus: What to choose for a newbie? Paying attention to Python, you are automatically accustomed to some structure and style of code design. Besides, the hanging of the full compilation will give faster feedback. It is very important when learning. Good luck!


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