One Year with R

I should have been more specific, the ... frustration for me comes up mostly in ggplot, Which usually directs you to layer(). Which gets parameter string documentation like:

* geom - The geometric object to use display the data

* stat - The statistical transformation to use on the data for this layer, as a string.

These are two hugely important parameters, with really big concepts and abstractions under them, but the documentation is of the style "foobar(): this is a function that foos the bar", documentation that restates the information in the name, but with more words, and no insight on where to go next.

So now a person is two pages deep into documentation, and it's actually circular documentation because layer() has a ... argument that gets passed back to what? The function documentation that you came from? For a newcomer it's a completely twisty series of passages, and as an experienced user who reaches for ggplot before any other tool, it's confusing.

The other function based confusion is that the list of aesthetics is not connected quite well enough to the mapping argument from aes(). What aesthetic values the function understands is probably one of the most important things about looking up the function. But reading the parameter documentation, it's not clear that there's an entire section below that describes that crucial material, far further down the page. And on a long long page it's easy to accidentally skip over that section when skimming.

(These are the sorts of frustration I have with typical Python documentation, btw, so maybe my brain is just different from typical engineers)

You almost literally can't come up empty on CRAN.

Most of his examples of WTF's are from base-R. And he's definitely not wrong, as many of these have bitten me a bunch over the years.

> I'm interested in non-flamewar non-religious reasons that the tidyverse is bad.

For the very reason that it's great to use, it's a nightmare to develop with. NSE is super handy as a user, but it's an absolute nightmare to build new functions on top of (dplyr specifically). Like, I now know 2-3 different ways in which quoting/substituting etc can be done for the tidyverse, and I've had to maintain code using them a bunch of times.

It's incredibly annoying, and every time I do it I need to look up Hadley's new approach to NSE (don't get me wrong, I adore using the tidyverse, but I absolutely despise programming with it).

Non-uniformity imposes a burden that will be too much to bear, unless the system offers particular advantages. The fact that several systems co-exist is proof that the advantage-burden balance is favourable in each case.

There is no need to converge on a single tool. Carpenters need both saws and hammers.

In practical applications, language syntax is just part of the story. One must also consider the issue of available libraries. One thing that really stands out with R is its immense collection of well-vetted and well-documented packages. Python and Matlab -- the two main alternatives in my discipline -- fall far behind R in this respect. If there's a journal article on a new statistical technique, then there's a pretty good chance of a package written by same author. And, if that package is on CRAN (the repository for such things) then it has undergone quite rigorous testing on several types of computer, with several versions of R.