Thanks for your reply!
> To answer your implicit question, VECTOR_ELT() unclasses the nodes
> because it doesn't go through the stats:::`[[.dendrogram` method,
> instead dereferencing the data pointer directly.
That�s roughly what I had suspected�I appreciate the clarification.
To your point on other *apply functions, I wasn�t actually aware of that
implementation, but it�s definitely a smarter way to do it. I�ll try later
today/tomorrow to incorporate that method; it seems much better and more
future-proof than my approach. Definitely agree with you with respect to cases
where unclass(node)[[i]] is invalid. It may be slightly slower due to having to
rely on R method dispatch, but I think the benefits outweigh the drawbacks in
this case.
> Would you mind telling me more about the following case?
> > if(!(inherits(res,c('dendrogram', 'list')))){
> > res1 <- lapply(unclass(node), \(x) x)
> > }
> If you're looking to improve the performance, there might be a way to
> avoid the wrapper and this lapply(unclass(node), identity) call in it.
This was a product of trying to get performance to be the same as in the
current method�I agree that it�s probably not the best way to do this. The
use-case is when you apply a function to the dendrogram that doesn�t return a
dendrogram object. One example is the one from reg-tests-1c.R:
```
D <- as.dendrogram(hclust(dist(cbind(setNames(c(0,1,4), LETTERS[1:3])))))
dendrapply(D, labels))
# Expected result:
#
# [[1]]
# �C�
#
# [[2]]
# [[2]][[1]]
# �A�
#
# [[2]][[2]]
# �B�
#
# [[3]]
# �C�
```
Applying labels to the root node returns c(�C�, �A�, �B�), and if we convert
that to a list, we get a length 3 list of length 1 character vectors. However,
when traversing the dendrogram pre-order, this would break things, since then
the first entry of the node is no longer a dendrogram object, it�s been
replaced by a character vector. I had written it this way with the unclass so
that I could replace entries that needed to be evaluated at child nodes with
child nodes. For example, in this instance, after evaluating the function at
the root, the tree would look like:
```
[[1]]
<unclassed D[[1]]>
[[2]]
<unclassed D[[2]]>
[[3]]
�B�
```
To answer the question on why there�s an lapply(�, identity) call, I think I
ended up doing it this way because I was having some issues with not getting
the elements to populate correctly from the dendrogram. Looking back on it now,
there�s definitely an easier way to do this that isn�t so hard to understand
code-wise�.
```
if(!is.leaf(node)){
if(!is.list(res)){
res <- as.list(res)
}
res[seq_along(node)] <- node
}
```
That should perform almost identically and make more sense, with the added
benefit that it doesn�t unclass the child nodes, so (when I also incorporate
the other fix you suggested) we shouldn�t have any unexpected performance from
functions relying on a hypothetical `subclass-of-dendrogram`. This
implementation is also slightly faster due to no lapply call and is.list() over
inherits(�).
Result after applying to root node with this approach:
```
[[1]]
D[[1]]
[[2]]
D[[2]]
[[3]]
�B�
```
Classes of D[[1]] and D[[2]] are preserved for future evaluations.
Thanks for pointing this out, I�ll incorporate this into the code when I check
the `[[` case later. If you have any other questions/comments/suggestions I
would love to hear them! Happy to clarify further as well if I didn�t answer
your questions fully.
Sincerely,
Aidan
-----------------------
Aidan Lakshman (he/him)<https://www.ahl27.com/>
Doctoral Candidate, Wright Lab<https://www.wrightlabscience.com/>
University of Pittsburgh School of Medicine
Department of Biomedical Informatics
ah...@pitt.edu
(724) 612-9940
From: Ivan Krylov <krylov.r...@gmail.com>
Date: Thursday, March 2, 2023 at 09:47
To: Lakshman, Aidan H <ah...@pitt.edu>
Cc: R-devel@r-project.org <R-devel@r-project.org>
Subject: Re: [Rd] `dendrapply` Enhancements
Dear Aidan Lakshman,
To answer your implicit question, VECTOR_ELT() unclasses the nodes
because it doesn't go through the stats:::`[[.dendrogram` method,
instead dereferencing the data pointer directly.
Other *apply functions in base R create a call to the `[[` operator,
letting the language dispatch the generic call, allowing the method to
assign a class to the return value. The following example is taken from
src/main/apply.c:do_lapply():
// prepare a call to FUN(X[[i]], ...)
SEXP isym = install("i");
SEXP tmp = PROTECT(lang3(R_Bracket2Symbol, X, isym));
SEXP R_fcall = PROTECT(lang3(FUN, tmp, R_DotsSymbol));
MARK_NOT_MUTABLE(R_fcall);
// inside the loop: evaluate the call
tmp = R_forceAndCall(R_fcall, 1, rho);
Not sure which way is faster, but it may make sense to try, and it's
probably more correct in (contrived) cases where unclass(node)[[i]] is
invalid because it relies on a hypothetical `[[.subclass-of-dendrogram`
to restore some invariants.
Would you mind telling me more about the following case?
> if(!(inherits(res,c('dendrogram', 'list')))){
> res1 <- lapply(unclass(node), \(x) x)
> }
If you're looking to improve the performance, there might be a way to
avoid the wrapper and this lapply(unclass(node), identity) call in it.
--
Best regards,
Ivan
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