Python or R: Which Is A Better Choice For Data Science?

Nice post!!!

The novice can easily learn R programming language within minutes by doing simple data analysis.

It depends much from previous learner experience.
R is more easy for statistic specialists without much previous programming experience, but Python is much more easy to understand for learners with basic programming experience.
I learned Python quite a lot and tried learn R. For me R seems very non-intuitive and hard to learn — it looks not like strict logical programming language, but more just like set of statistical tools where same things can be done in lot of different ways what confuse learner.
And R have no use outside data-science, while Python can be used in lot of different fields, so it's much more useful, is rising in his popularity from year to year and I am pretty sure it will continue replace R in future.

Stop directly comparing Python and R, these are two completely different tools. Python is a simplified language designed to basically solve everything at the cost of performance/maintainability. R is designed for data science and visualization. Period.

1)
When you compare data structures, it would be nice to see both Python and R implementations, as well as performance comparison. In order to get a tree-like structure in R, you can probably use environments, or write a faster implementation in C/C++ (with the help of e.g. RCpp), or look for a CRAN/GitHub package with said functionality.

2)
Comparing Python 2.7/3.x debacle to R and tidyverse relationship is simply inappropriate. While Python introduced breaking changes and some packages are not being ported to 3.x, R versions have been consistent.
tidyverse is an addon to R, a set of packages that can be used at any point in the code. tidyverse is closer to numpy/matplotlib libs from Python. There is always an alternative, but tidyverse happens to be more widely used.

3)

Python programming language has one OOP paradigm while in R, you can print a function to the terminal many times.

I fail to understand what does it even mean. Anyway, you show no examples of Python metaprogramming/OOP features, at the same time presenting a very poor example of R metaprogramming in a form of code generation. It would be nice to have an explanation of R OOP mechanics, S3 as the very basic level, S4, reference classes, proto (which powers ggplot2 OOP) and R6. This could be compared to Python more traditional OOP system with decorators (which do provide some level of metaprogramming).

9)
R learning curve can be much steeper than that of Python. Python was designed to be simple and understandable and its syntax is very concise. R, on the contrary, differs from typical programming languages. It is also affected by its predecessor, the S language. In R, virtually everything is an expression, and this feature, together with the non-standard evaluation, are the greatest tools that R has to offer. R also has formulas, a quite strange type that is used in e.g. machine learning/fitting procedures (like lm()). Formulae,NSE and environments are at the core of grammar and syntaxis of tools like ggplot2, dplyr, purrr, data.table and so on. I doubt that learning to understand NSE and environments is easier than mastering advanced Python OOP.

10)
Elegance is indeed subjective. I find R more elegant because it naturally allows for functional-style piping (thanks to magrittr and friends). Pipes, NSE used within dplyr/data.table, lambdas that are constructed from one-sided functions in purrr (~ .x + 5), pronouns like . — all these features make R more elegant, in my opinion.
Python, however, has a very typical imperative/OOP syntax. Its only advantage is the absence of braces/other scope delimiting symbols and usage of short keywords (like def, pass, with). The indentation may sometimes make the code less elegant.