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Comment by epistasis | Hacker News Reader

The tidyverse docs are the only ones with the super frustrating ... of impenetrable gnostic "documentation" that I know of. In general the tidyverse documentation is horrible, almost as bad as typical Python docs, IMHO. Other parts of base R are wonderfully documented in my opinion.

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Do you have any specific examples that illustrate the general problem? I'd love to better understand what you're looking for in docs.

Thanks for taking my aggressive comment with such spirit, it really speaks to a good community. (Sleep training an infant has me a bit frazzled)

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)

Ah yeah, connecting the dots in ggplot2 docs is hard. It's hard for us to document because, under the hood, the pieces quite decoupled and different pieces are responsible for different arguments. But since we last took a deep dive on the ggplot2 docs, we've gotten much better at generating docs with code, so maybe it's time to have another look. I've filed an issue (https://github.com/tidyverse/ggplot2/issues/4770) so we don't forget about it, but no guarantees about when it might get done.

Is there a tutorial someplace that explains how ggplot actually manages plotting? Or the architecture and layers between the high level code and how a plot is drawn? Meaning, I love being able to express what I want and ggplot figures out a good plot for me. But I know there are many layers that can be manipulated, but I just don’t understand the layers.

One of the best compliments I can think of is that with ggplot, easy things are easy and hard things are possible. But I haven’t been able to figure out how to fully work the system.

(Thanks for all of the work!)

That should be covered in the original A layered grammar of graphics paper: https://vita.had.co.nz/papers/layered-grammar.html

And then there is an entire ggplot2 book (there are many, but this one was written by Hadley): https://ggplot2-book.org/

That's very helpful. I think this chapter was what I was looking for:

https://ggplot2-book.org/internals.html

I think the issue with some of this documentation is that for other packages, the function documentation is largely self contained. If I look up glm() it tells me how to use glm(). However, for ggplot2 there is an assumption that you have some level of knowledge of how the pieces should be strung together. So when I know I want a boxplot, and I find geom_boxplot() documentation it wonderfully describes the options for itself, and gives examples for it's use. But sometimes it doesn't give a good idea of the context of how the other pieces might interact. It makes complete sense if you read the book and just want to refresh your memory, but if you are coming in as a new user it really can be difficult to use the documentation exclusively.

Having read an online book of some sort on ggplot2, on one of the tidyverse sites, I found the per-function documentation difficult to use and difficult to match to the concepts I had learned. This may be because I'm used to using the parameters section of a function as the primary resource for understanding the inputs. But with ggplot it's scattered in other places, and the holes are not apparent unless you know the specific terminology (not concepts) to match up.

All that said, I find the documentation to be saying a lot more than it did in the past, and it sounds like it has been continually improving.

He may be right in specific instances, but I think he's way wrong in general. Tidyverse is generally a triumph of documentation, and part of that is that it doesn't tell you too much. Lots of how-to, not too much implementation detail. It's appreciated.

After 15+ years of shipping open source stuff, I've rather concluded that any given piece of documentation is either going to be too terse or too verbose for any given user and all you can really do is mix judgement and balancing how many of each type of complaint you receive.

It's probably possible at least in theory to structure docs so you have a terse section followed by a verbose section for each thing, but I've yet to develop the discipline or the competence to pull that off remotely regularly.

Maybe in another 15 years.

> almost as bad as typical Python docs

I found numpy, scipy, pandas, and plotly docs to be quite clear and extensive. The only docs I have found to be confusing are matplotlib's and the Python standard library's. Not sure what packages you are referring to?

On the other hand, I'm curious what you've found to be lacking about the standard library documentation. I've found it to be generally very thorough, in some cases fantastic, though there's an occasional weak point.

For me, the stdlib docs isn't lacking in material, but it is hard to navigate. I think that is partly because it mixes different types of documentations together (tutorial, reference, and changelog). They should split each module's docs into two pages: a tutorial with examples of the most common use-case of the module, and a reference listing all of the classes, methods, etc. of the module.

Also, the built-in types are documented in one page, going from boolean to sequence types and even type annotations. Every Ctrl + F gives me 20 different results, which is annoying as hell.

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