No matter how obscure, all programming languages have been built to solve a problem from the early days when the only way to code was using punched cards to the present, where Artificial intelligence can debug your code for you. With big data and data mining, the buzzwords R and Python have taken center stage. Now the question arises, is R harder than Python?
Python is easier to learn than R, but that is a matter of perspective. In reality, comparing these two languages is a nuanced subject. Python is way more popular, so it has more avenues to explore and learn.
I assume that you are looking to decide on which language to specialize in, if that is the case read on to develop an informed decision.
To analyze both these programming languages, we need to understand what they are, how they function, and their strengths.
Comparing R And Python – Which Is Harder, Better?
What is Python
Image from Python.org (under creative commons license for commercial use)
It is a “glue language.” It means that languages such as java or C++, which are very time intensive to write in, can have certain time-consuming elements written in Python. It reduces time requirements significantly, which makes for a handy feature for prototyping.
Python is a high-level language. What that means is its syntax is much closer to how we usually type, say, an essay. This makes it easier to read and helps a developer focus on solving problems rather than getting bogged down with the complex construction of something like Machine Language.
Image from r-project.org (under creative commons license for commercial use)
It is a language that is dedicated to handling statistical analysis and graphics, by extension data mining. It also falls under the category of a high-level language. R is the open-source version of its parent language called “S.”
Compared to most programming languages, R has a somewhat unique syntax. Which can mean there is a slight learning curve if you are used to something like Python. For example, the “ = ” sign is replaced by “->,” you can still use the equals to sign, but it is recommended to use the arrow sign.
Should we compare them?
Comparing the two languages is like asking: should I use a hammer or a screwdriver? The answer is no; we should not compare them. A toolbox is not complete without a hammer or a screwdriver. Both these languages are resources we use to solve problems.
But if you are not content with that answer. You want to know which one is easier and better, then let us go through some features of both languages—empowering you to make the decision.
Dissecting R and Python
Let us look at both languages through certain filters. This will allow you to make a more holistic decision.
Since R is more focused on a specific niche, it is great at it. But Python is more versatile because it is a general-purpose language. Plus, employers tend not to appreciate things that are not popular, as tragic as it is. Python can make you more employable.
Data source: Google Trends (free to use according to the rules of https://support.google.com/trends/answer/4365538?hl=en)
This graph compares web searches worldwide since 2004. The blue line is searches related to Python, the red line represents searches about R. As you can see after January 2016, interest in Python sky-rocketed, while searches about R have deviated little.
Since both are Open-source. This means that they are free for anyone to use personally and commercially, and their source code is available online. Because of this, both Python and R have a dedicated community, they assist the development of improved versions, testing them, and creating additional features that are not available in the vanilla versions.
The advantage of having such communities are community forums. These are places where you can post questions about any trouble you are having with either language. People will answer them in extremely helpful ways.
Add-ons are additional features that do not come with the vanilla versions. They can be the ability to change the background in an IDE, something as complex as translating Python into C++. Both R and Python have an extensive library of add ons.
But since the Python community is way larger, it has a lot more add ons, they are updated frequently. Also, there are large corporations that will support the use of Python in their products. Do not think that there are no add ons in R, there are plenty, and enjoy substantial support.
Both languages are widely used for data analysis and mining. This means they can easily integrate complex mathematics. Their powerful built-in math functions enable this. Python and R are easy to learn and comprehend if you try. Compare them to something like C++ and you will understand how user-friendly they are.
For handling and processing large swathes of data, R provides the most amount of built-in tools. The standard IDE has a window that automatically creates a plot of your data. This helps easy visualization of data. Unlike in Python, you can easily search through the largest packet of data. Again for this, there are built-in functions that can quickly let you focus on a point of data and analyze it.
Python is the most popular language used for machine learning and artificial intelligence. The primary reason is its ease of understanding, which has led it to become a popular language. This caused a chain reaction where large corporations, such as Google sponsor Python. These sponsorships have materialized as free machine learning tools. This has resulted in the birth of languages like PyTorch, which are optimized for machine learning.
How can you learn Python and R?
Well, there are two ways to do it, neither of them is wrong.
The hard way
This involves you learning a bunch of languages. It is so you can appreciate the improvements each brings. Firstly learn Machine language, then C, then C++, and finally Python or R. Admittedly this looks daunting, hence it is the hard way.
The straightforward way (recommended as a start)
Just jump right into whatever language you want to learn. Plain and simple.
Ok, now you have chosen a path, how do you learn. Your impulse may be to watch those 3 hours long YouTube tutorials. Yes, they have high production quality and are jam-packed with knowledge. But when you wrap up that course, you will still not be able to code properly.
To learn to code properly, you need to choose a project, it could be something tiny, creating a small game like Pong is one example. During this endeavor, you can take help from the community and the courses on YouTube. Just remember you need the ability to solve logical problems with your coding. The only way to do that is to solve problems.
Work on mastery not Resume Padding
It may tempt you to learn both languages one after the other, but avoid doing that. Become proficient in the first one. Take on tough projects and keep working at them, remember problem-solving skills are what employers are looking after. Once you feel you have pushed yourself, then move on to learning the next one.
What matters is which one do you find interesting, remember learning either language should only be the gateway to learn the most important skill, problem-solving. Just remember neither language is hard, they are just built for different scenarios.
Ozgur, Ceyhun & Jha, Sanjeev & Shen, Yiming. (2020). Using Statistics Software Packages for Teaching Purposes: R and Python Running head: R and Python.