This ignores the reality that one will ultimately have to interact with people who have no understanding of any of these tools. Unless you happen to work in a pure computational biology group, one _will_ have to interact with lab workers, biologists with no training (or understanding of) in R or python, doctors, etc. All these people will know excel.
IMO the change is good, but is a case of detected vs undetected.
Recently I was working with some colleagues, being I the computer savvy and them the lab people. I send them some data in CSV, that when opened in Excel turned 123.456 into 123456 (it was a problem with locales, some people using "," as decimal and some using "."). We noticed because the values should be between 0 and 1000. But what if the column could be between 0 and 1000000? A small quantity of numbers bumped up by a factor of 3 could fly under the radar, and distort further measurements. And the error is undetectable forever once published.
I like it better the programming language approach: look, this is how you write a string, this is a char, this is a float and this an integer. "2020-08-04" is a string until you ask me to turn it into a date. "SEPT1" is a string, and you are going to do quite the gymnastics to make me understand it as "date(2020, 9, 1)". Do you like "," or "." as thousands? Then we first turn the number into a string and then format, but the original number is kept.
Excel technically has a type system where you can change the type of value a cell has. In my experience it is difficult to convince excel to actually change the cell's data type sometimes. Doing so can often change the underlying data as well. Personally I avoid excel if I can, because it's quirks are just too frustrating. But it certainly has its uses.
