Owing to the surge in popularity of machine learning, artificial intelligence, advanced analytics, and data science, the people that are much more interested in learning more regarding these fields have different important questions to be considered.
It includes choosing the right programming language that is a better solution or making the decision of which programming languages, frameworks, APIs, platforms, and packages to use for a particular scenario or task.
If you are thinking of developing artificial intelligence solutions and are stuck between selecting the right programming language for the project, then this article will assist in clearing your doubts, which are related to the characteristics of R and Python. So, let’s get started.
Both Python and R share similar types of features and are the most commonly used and popular tools by developers. Both are open-source and hence free, but Python is structured as quite a broadly useful programming language, whereas R is essentially created for statistical analysis. Let’s look at a few pros, cons, and major differences between both languages so that you can easily decide which particular option suits you the most.
Developed in the late 80s, this programming language plays a pivotal role in powering the internal infrastructure of the tech giant Google. It is comprised of many enthusiastic developers, and it is widely used in the applications of Instagram, YouTube, Quora, and Dropbox.
It is broadly utilized across the different IT businesses and permits quite a simple effort of collaboration within the development groups. Hence, if you need a multi-reason and adaptable programming language with quite a large supporting network of engineers along with extendable AI bundles, then this language is the best pick.
Advantages of Python
Language of General Purpose
It is regarded as a great choice if the project demands more than simple statistics. For example, designing an intuitive and functional website.
Easier Learning Curve
It is quite easy to learn and is easily accessible which enables a custom software development company to find skilled developers on a much faster basis.
There are Numerous important libraries
It has countless libraries for gathering and controlling information. For instance, Scikit consists of tools for both information mining as well as investigation to support the convenience of AI using Python.
In any engineering environment, generally, Python is much better integrated than R. Hence, regardless of the designers’ attempt to exploit the different lower-level languages like C, Java, and C++, it often results in the improved joining of different components with the given Python wrapper. Also, a Python-based stack is quite easier to incorporate the different tasks at hand for data researchers, bringing them easily into the creation.
It boosts productivity
Python’s syntax is quite extensively decipherable and, like other programming languages, unique in comparison to R. It guarantees higher profitability for the developers.
The Disadvantages of Python
- It only consists of quite a few statistical model packages.
- Because of the presence of GIL, or Global Interpreter Lock, threading in Python is both difficult and problematic. Also, multi-threaded CPU-based applications act quite slowly compared to single-threaded ones. Essentially, an AI project is more valuable for executing multiprocessing than multi-threaded programming.
It was essentially developed by statisticians for statisticians, which any particular developer can easily predict by simply looking at its syntax.
Since the language contains different mathematical computations that are involved in machine learning that is derived from statistics, it is the right choice for those who wish to gain an in-depth understanding of the different underlying details along with creating innovative applications. If your project is majorly based on statistics, then R can easily be considered a great choice for narrowing down different projects that require a one-time dive into the given dataset.
Advantages of R
Best Suited for analysis
In cases where data visualization or analysis is at the very core of the project, then R can easily be considered the perfect choice since it allows rapid prototyping and even works with different datasets to design different machine learning models.
Several Useful libraries and tools
Quite similar to Python, R consists of different packages that help in improving the performance of artificial intelligence projects. R developers can easily benefit from the various advanced data analysis packages that cover the pre- and post-modeling stages that are aimed at specific tasks such as model validation or data visualization.
Disadvantages of R
Steeper learning curves
It is quite a fact that R is a challenging programming language, and hence you can find quite a few experts to develop the project.
Since the algorithms of R essentially come from third parties, it ends up with different inconsistencies. Also, every time an AI development company makes use of any new algorithm, all of the connected resources have to learn various ways to model data as well as make predictions. Also, every new package needs learning, and there is no particular detailed documentation of R since it has quite a negative impact on the overall development speed.
When it comes down to artificial intelligence projects used by cloud computing services, both Python and R have their own particular set of advantages. However, Python performs better in data manipulation as well as in repetitive tasks. So, it is a particularly good choice if you wish to create an artificial intelligence solution. If you have to develop a tool for ad-hoc analysis at quite an early stage of your project, then go for R. The final choice depends on which particular language you wish to go with.