When choosing between Python and R, the decision will mainly depend on the intended use. R is recognized for its applications in statistics and the display of results. For its part, Python does well thanks to its many functions and solutions.
Why compare Python and R?
Anyone who wants to learn to program and is looking for a specialized language in solutions adapted to the scientific work with analyzes and statistics will sooner or later fall on Python and R. Indeed, these two programming languages find applications particularly in the fields of data science, predictive analyzes and data visualization. They each have a large community. At first glance, the two options have a lot in common. But what are the differences between Python and R and is it better to choose one over the other?
Advantages and disadvantages of R
The name « R » comes from the language’s designers: Ross Ihaka and Robert Gentleman, two statisticians from the University of Auckland, who developed it starting in 1992, focusing on performing and presenting complex statistical data analyzes. With this new language first released in 1993, they mainly aimed to reach people with extensive prior knowledge in the fields of statistics and programming. R is based on the S programming language with a free implementation.
R can be compiled and runs on UNIX, Linux, Windows and Mac platforms. The language is mainly used for statistical software development and for in-depth data analysis. Thanks to its numerous libraries, R can also be used for the graphical implementation and exploitation of the collected data. The language is open source and part of the GNU project. While R was first popular in academia, many companies have since discovered the benefits of the language. It integrates very well with other languages and programs thanks to its numerous interfaces.
Positive points of R
- Open source : R is a language for everyone. This is at least true regarding its availability and cost: not only is the programming language completely free but it is also open source. This means that it is normally possible to use or extend it in such a way as to completely adapt it to one’s own needs.
- Extent : the Open source approach has as a corollary the existence of numerous adaptations made available free of charge. There is therefore a good chance that a solution to a given problem already exists. In fact, nearly 20,000 packages have already been created by development teams based on R. They are specialized in certain areas with tailor-made solutions.
- Compatibility : not only does R run on many different platforms but it also has interfaces with many other languages and databases. You can therefore easily use R for a partial domain and integrate the language into a broader context.
- User interface : To improve the usability of the language, Rstudio has developed a graphical user interface. It makes working with code a lot easier. Enough to implement projects more quickly, with a clearly simplified and improved visual presentation thanks to packages like Plotly. This tool allows you to represent the results of your projects in the form of graphs or diagrams.
- Community :R has a community ready to help. Many fans of the language are specialists in their respective fields and are able to provide valuable advice for solving problems and answering questions. Finally, there is plenty of documentation as well as additional packs and libraries already mentioned.
Negative points of R
- Performance : R is not inherently a slow or weak language. There may, however, be some delays for large datasets. This is notably due to MonoThread processing which can only be used by one central processing unit at a time.
- Learning curve : As R is offered by default without a graphical user interface, getting started can quickly become difficult. Additionally, it takes some time to become familiar with all the scoring rules, restrictions and particularities. You’ll also need basic knowledge of statistics, an important prerequisite for working with R. You can get a feel for the language in our R beginner tutorial.
Advantages and Disadvantages of Python
Better known than R, Python is used by millions of people around the world. Developed in 1991 by Guido van Rossum, the Python language has, since day one, nourished the objective of guaranteeing making code as simple as possible. Many language terms are taken from English and are easily understood. The code is very clear and easy to read. Python is platform independent and works in an object-oriented manner. The very versatile language also offers, thanks to its large community and its open source approach, numerous packages in the fields of deep learning, AI and data science. Consulting our Python Tutorial will allow you to learn more about this language.
Positive points of Python
- Versatility : Python is a versatile language in every way. On the one hand, it finds applications in many fields and thus makes it possible to approach projects globally. On the other hand, the language is also platform independent and can therefore run on different machines. Added to this are numerous interfaces with other programs, languages and databases.
- Open source : like R, Python is open source and available for free. Development is coordinated by the Python Software Foundation but all users are free to optimize the language for their own projects.
- Extent : its popularity helps provide a multitude of different packages to development teams working with Python. More than 300,000 solutions can be downloaded and used directly, making project work easier.
- Learning curve : Python is considered one of the simplest programming languages in the world. Despite its impressive possibilities, the language can be learned and used in a short time. The scope of the code is also relatively clear. This streamlines teamwork processes and makes it easy to carry out small projects.
- Community : Python has a huge community that publishes documentation and its own libraries. The Python community is known for its support of newbies. If you have a question or problem, you will quickly find a qualified person who can help you.
Cons of Python
- Performance : Python is a dynamic language which can sometimes present some speed deficits. This is especially observed when working with large datasets. In these specific cases, development teams most often resort to alternatives.
- Prone to errors : Python is no more prone to errors than other languages but these are often only noticed during execution. This is why it is very important to carry out in-depth tests and regular checks beforehand.
- Visualization : Python also has some shortcomings in the representation of values and statistical results. It has only few tools to provide truly satisfactory results.
- Mobile terminals : Python is not optimized for use on mobile devices. While there are a few solutions in this direction, most app development teams prefer languages and programs that are basicly compatible with Android and iOS.
Python vs R: what are the differences?
While it is true that the two languages have some similarities, let’s take a closer look at the few differences between Python and R.
Syntax
At first glance, the differences in syntax are obvious. Here is the syntax for R:
$ R
> myString <- "Bonjour ! Vous utilisez R."
> print (myString)r
In this case, Python makes it a little shorter:
>>> print("Bonjour ! Vous utilisez Python.")python
Python vs R: other differences
There are of course a few other features that differ between Python and R.
- Application : the approach of each language is very different. R is mainly intended for statistical analysis and its representation, with excellent results in this area. Python takes a broader approach and is also suitable for software programming and deep learning.
- Scale and distribution : if more and more people use the R language without any academic background, it still remains very linked to this field. Many more development teams are using Python, resulting in the existence of many packages for Python.
- Performance : Both languages are not the fastest on the market, but overall Python is a little faster and more efficient than R.
- Formats : While Python can handle many data formats, R is a bit more restrictive. Only CSV, Excel and text files can be used without additional tools.
Python vs R: Which Language is Better to Learn?
So who wins this match between Python and R? The answer largely depends on the intended use. The two languages are good choices, both very powerful. If it is primarily a question of creating statistical models to visualize them, R remains the best choice. For other tasks, which include processing beyond statistics, Python offers many more possibilities.
