As someone who uses and teaches R extensively (and loves the tidyverse) the tidyverse is so much easier to teach to people without any programming experience and who have very little faith in their tech or maths skills (which was me as well).
The tidyverse just 'made sense' to me when I started using R for the first time a few years ago, and now I love using R and programming. On the other hand, some of my ex-classmates learnt base R (because that's what we were taught) and found it hard, didn't learn anything properly, and now still think R or other programming languages are opaque and hard.
I'm not particularly fussed if Statistics Profs prefer data.table to dplyr or base R to tidyr, I know what is easier to teach, understand and use for me and a lot of other ecology/bio students and people.
I concur with your sentiments having cultivated data science teams from the ground up with diverse educational backgrounds.
Programming in base R is more akin to assembly language and has accreted a babel of inconsistencies that make it difficult to teach and learn. Learning base R isolates you into a Galapagos island of academics who are either ignorant of the needs of data workers or too elitist to engage with those not in their priesthood.
Learning Tidyverse is a considerably better transition for learning other languages, frameworks, and libraries.
Functional programming is closer to algebra than indexing into data structures with magic numbers. I've found more success teaching functional pipelines of data structures using the idioms in Tidyverse as a general framework for data work than base R. Abstraction has a cost but for learning it is the appropriate cost.
I sense that much of this `monopolistic` fear mongering is really about feeling out of date.
"I know what is easier to teach, understand and use"
I think this really depends on the end point. If you want to learn to read data into R and do basic manipulations, plotting, modeling, etc., the Tidyverse absolutely has a lower bar to entry. Once you get into writing functions, it gets a little trickier. Knowing some of the base R programming concepts and skills will make you much more efficient. If you start debugging and profiling code, only knowing the Tidyverse becomes a liability because you fundamentally do not understand R's computational model (the Tidyverse does not follow it). Hence, if your end goal is to write and debug functions in R, the steeper learning curve of base R can more than pay off. If not, then the Tidyverse's low bar to entry can be more attractive.
I'd always (and always do) google an advert rather than trying to remember the exact website anyway. If you search 'href sep' or 'hrefs seo' or even 'aref seo' it always still comes up as the first result for me.
So this is the research area that I currently work in, albeit with a disease ecology perspective rather than a biochemistry one. I've had a quick read of it and and although it does seem to track other research that has been done ("Why are behavioral and immune traits linked?" by Lopes 2017 is a nice review) I'd love to see some power analyses - the sample size seems pretty small for something as complex as this question. The study I'm helping with now has a minimum sample size in the hundreds, for instance, for a broadly similar question. PCA-ing a few tests together is pretty common, but also comes with a lot of potential biases (see "Avoiding the misuse of BLUP in behavioural ecology" by Houslay and Wilson if anyone is interested!)
It's great that this has been picked up recently, it's something I've been following along with for a while. If anyone is interested, James Heathers runs a podcast called Everything Hertz (https://twitter.com/hertzpodcast) with Dan Quintana and Nick has been a guest on it - it's well worth listening to if you're interested in science full stop.
These guys aren't researchers in my area of interest but the topics they cover are interesting and done very entertainingly!
I'm pretty sure they would be able to - I worked with cancer trial data (just a student job, doing data entry) but all patients were completely anonymised to anyone working with the data but readily identifiable by a specific unique ID number, so higher-level trial managers and doctors could find patient information if needed.
I love Chance, but I can't imagine that he's got the net worth already himself to buy SoundCloud / make a meaningful investment - maybe he has contacts who do or could help an investment group, but I think it might be a bit fanciful to say that Chance by himself can save SoundCloud.
The tidyverse just 'made sense' to me when I started using R for the first time a few years ago, and now I love using R and programming. On the other hand, some of my ex-classmates learnt base R (because that's what we were taught) and found it hard, didn't learn anything properly, and now still think R or other programming languages are opaque and hard.
I'm not particularly fussed if Statistics Profs prefer data.table to dplyr or base R to tidyr, I know what is easier to teach, understand and use for me and a lot of other ecology/bio students and people.