I'm not necessarily pushing for R, but just for your information

R and Python are two free (OSS) tools used in data science. Recently, everyone immediately goes for Python, and while I understand why given the amount of information on the web, I also think it's worth understanding a little bit more.

If I were to describe the division of roles between the two based on my old-school perspective, in a very rough way:

• Functionality

  • If you are doing deep learning on unstructured data like text, images, or audio, the frameworks for that are built to be used from Python, so it is effectively Python or nothing.

  • For time series analysis, Bayesian networks, and statistical analysis fields, R is still better equipped and easier to use.

• Ease of use

  • Python is a proper programming language, and because it has good compatibility with downstream system construction, it is preferred by the IT industry.

  • R is what a student from an IT background once called a 'high-density language' in a previous training session. Because it is specialized for data analysis and statistical analysis, the analysis that can be achieved in R can be done in fewer lines of code than in Python. Also, the language is less strict (it can execute even with somewhat sloppy code). It was used by the business side because they just wanted to do data analysis flexibly and freely. (However, in well-funded businesses and industries, they use MATLAB, Stata, SAS, etc.)

During the big data boom, R was about the only thing known and used, so R was the subject of articles. Recently, the IT industry, which wants to sell and promote deep learning solutions, has been putting effort into appealing for them, but that leads to Python in terms of both functionality and ease of use, so I suspect that beginners who see such articles end up thinking, 'Okay, Python first it is.'

Actually, I don't really intend to push R that strongly.
I just want you to know that both R and Python still have their own uses, pros, and cons. People who have only recently dabbled in data utilization often say things like 'Python is the way' or 'R, lol' without researching or thinking enough. Even if you can't grasp the reality, I think it's good to keep in mind that all discourse, whether conscious or not, is position-based.

Adding a slightly more objective article.

By the way, point 1
If a young person asks which one they should learn now, if they are in the IT industry looking at a career decades ahead, it's Python. Because even if R or Python go obsolete, you will have acquired general programming skills.
For others, I think you should choose the one you need according to your purpose for learning. Perhaps in the future, the difference in functionality and usability between R and Python will disappear; I honestly don't know when that will be, but at that time, you can choose the de facto standard or the one you are comfortable with. In the first place, there is a possibility that R and Python will be obsolete, or that no-code OSS tools will become widespread enough to be practical.

Also, for those who have only dabbled and dislike programming, you probably won't be doing such difficult analysis, so no-code tools should be fine.
I introduce R-based ones because R is easier to install and set up at a minimum. There are similar things for Python, so if you like, please look for them yourself.

By the way, point 2
The article I mentioned at the beginning says that R has a higher annual income, but other surveys say Python is higher. The fact that it fluctuates significantly depending on the survey method matches my own experience, so I always enjoy reading these kinds of articles.
Excluding the income ranking, I like this article because it feels like it was written by someone outside the field who understands it to some extent. I've seen recruitment requirements from HR departments and articles from recruitment agents that don't even have this level of understanding...

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