He is glossing over the major issue with base R, which is its tendancy to switch data types in a way that appears random to new users. As an undergrad I spent nights literally on the verge of tears debugging R code where the types had been mucked up by R's bizarre semantics. Claiming that data[foo] is the same as filter(data, foo) is not correct - the [] and [[]] operators have a lot of strange side effects depending on what argument is passed in. It is like saying map() and a for loop are the same because everything a map can do a for loop can do. That is true, but the single-mindedness of map is a major selling point because you know it is only going to do certain specific things. The issues here will disproportionately affect beginners who are likely to accidentally pass in the wrong thing - it needs to fail with an error or in a predictable way to best help them. Hadley's code fails sensibly.
One of the major innovations of the tibble package is that it puts the types of each column into the headings when it prints the table out. Dr. Matloff is grossly underestimating how painful it is for beginners to, eg, figure out when they are dealing with a column of strings vs a column of factors. If a beginner copies code to someone else for help, the other person can actually tell what types are involved! It is like magic compared to base R.
Given how ad-hoc the R community is and the paucity of people with programmer backgrounds, these minor usability improvements on controlling types are important. And anybody who cares about efficiency is using the wrong language.
EDIT Also, this comment that RStudio is bypassing an Open Source project's leadership - that is one fo the major features of open source. It is usually a healthy sign that the project leadership are lost in the weeds. I recall a situation with broad parallels in the history of GCC for example; it didn't kill the project although I believe a bad codebase died.