How to learn statistics for medical research? I'm a last year medical student, as we say "Intern doctor". In the future I want to do research on the issue that I want to get in.
Therefore I want to learn mathematics, statistics, R programming and so on, because I want to do best statistical analysis for my research. Proper statistical analysis is the most important part after the research issue as far as I'm concerned.
Consequently, for these things I watch on Youtube the Richard McElreath statistical rethinking videos and I do learning R programming on DataCamp courses, I bought annual premium plan. As far as I see the Youtube videos that I watch are somewhat hard for me to understand. Without statistical substructure I think to learn R programming on DataCamp would be on shaky ground.
What would you think about that? I want to learn the ideas, advices from more experienced people than me. What would you advice for me to learn these skills with very well?

You're Getting The Order Wrong.
It's as though you've said "I want to learn medicine, therefor I bought a scalpel." R is a very useful tool. But it will be much harder to learn statistics from using R than it would to learn to use R after already knowing statistics.
If I were learning statistics from scratch, I would not start with Baysian. I would start really simple. Use physical objects to understand how possible outcomes work first (like dice and coins). If you flip a coin, how many outcomes are there? Can you list them out? How about two coins, etc. Getting a solid understanding of the set of possible outcomes is the best basis for thinking about how "likely" something is to fall into a given subset of those outcomes.
Conveniently, this is the way that most introductory statistics classes are taught. As such, I really recommend that you start learning statistics with an intro class.
Following that, probably the next most important skill is formal logic. That's likely to be filed under philosophy at your school or online. You will essentially never be able to actually "prove" anything with research in a strict, formal sense. Example: with the logic chain A -> B -> C, maybe you test B->C a million times, and get the desired result every time. That demonstration doesn't "prove" that B->C. But practically, we would probably accept research that asserted B->C. What you need there is the grounding and discipline to understand and clearly state your full logic chain and all your assumptions. Doing so will make the analysis easier, and will make your results more robust.
Once you have that, you can look at Baysian approaches much more usefully. I find it's easiest to think of Bayes as dealing with some epistemological problems with frequentist statistics, which means it wont make much sense unless you already understand the basics of statistics and of the logic chains. (Some people have claimed success with learning Baysian from the get-go. I have trouble even imagining how one could usefully do that. YMMV.)
Unfortunately, while you can get a solid working knowledge of applied statistics that way, it will be exceedingly difficult to get a true understanding without calculus, and fairly difficult calculus at that. So if you really want to dive deep, this is about the point where you would want to brush up on that. Then look for a calculus-based statistics. It might be called "Statistics for scientists and engineers" or similar.
