>>>>> Suharto Anggono Suharto Anggono via R-devel <r-devel@r-project.org>
>>>>> on Sun, 15 Oct 2017 16:03:48 +0000 writes:
> In R devel, function 'factor' has been changed, allowing and merging
duplicated 'labels'.
Indeed. That had been asked for and discussed a bit on this
list from June 14 to June 23, starting at
https://stat.ethz.ch/pipermail/r-devel/2017-June/074451.html
> Issue 1: Handling of specified 'labels' without duplicates is slower than
before.
> Example:
> x <- rep(1:26, 40000)
> system.time(factor(x, levels=1:26, labels=letters))
> Function 'factor' is already rather slow because of conversion to
character. Please don't add slowdown.
Indeed, I doo see a ~ 20% performance loss for the example
above, and I may get to look into this.
However, in R-devel there have been important internal
changes (ALTREP additions) some of which are currently giving
some performance losses in some cases (but they have the
potential to give big performance _gains_ e.g. for simple
indexing into large vectors which may apply here !).
For factor(), these C level "ALTREP" changes may not be the reason at
all for the slow down;
I may find time to investigate further.
{{ For the ALTREP-change slowdowns I've noticed in some
indexing/subset operations, we'll definitely have time to look into
before R-devel is going to be released next spring... and as mentioned,
these operations may even become considerably faster *thanks*
to ALTREP ... }}
> Issue 2: While default 'labels' is 'levels', not specifying 'labels' may
be different from specifying 'labels' to be the same as 'levels'.
> Example 1:
> as.integer(factor(c(NA,2,3), levels = c(2, NA), exclude = NULL))
> is different from
> as.integer(factor(c(NA,2,3), levels = c(2, NA), labels = c(2, NA),
exclude = NULL))
You are right. But this is not so exceptional and part of the new feature of
'labels' allowing to "fix up" things in such cases. While it
would be nice if this was not the case the same phenomenon
happens in other functions as well because of lazy evaluation.
I think I had noticed that already and at the time found
"not easy" to work around.
(There are many aspects about changing such important base functions:
1. not breaking back compatibility ((unless in rare
border cases, where we are sure it's worth))
2. Keeping code relatively transparent
3. Keep the semantics "simple" to document and as intuitive as possible
)
> File reg-tests-1d.R indicates that 'factor' behavior with NA is slightly
changed, for the better. NA entry (because it is unmatched to 'levels' argument
or is in 'exclude') is absorbed into NA in "levels" attribute (comes from
'labels' argument), if any. The issue is that it happens only when 'labels' is
specified.
I'm not sure anymore, but I think I had noticed that also in
June, considered to change it and found that such a changed
factor() would be too different from what it has "always been".
So, yes, IIRC, this current behavior is on purpose, if only for back
compatibility.
> Function 'factor' could use match(xlevs, nlevs)[f]. It doesn't match NA
to NA level. When 'f' is long enough, longer than 'xlevs', it is faster than
match(xlevs[f], nlevs).
> Example 2:
> With
> levs <- c("A","A") ,
> factor(levs, levels=levs)
> gives error, but
> factor(levs, levels=levs, labels=levs)
> doesn't.
yes, again that is a consequence of what you said above (before
'Example 1')
> Note: In theory, if function 'factor' merged duplicated 'labels' in all
cases, at least in
> factor(c(sqrt(2)^2, 2)) ,
> function 'factor' could do matching on original 'x' (without conversion
to character), as in R before version 2.10.0. If function 'factor' did it,
> factor(c(sqrt(2)^2, 2), levels = c(sqrt(2)^2, 2), labels = c("sqrt(2)^2",
"2"))
> could take sqrt(2)^2 and 2 as distinct.
Well, that may be interesting.. but I doubt if that's somewhere
we should go, easily, because factor() has been documented to do
what it does now (with very slightly rounding such numbers via as.character(.))
and hence such a change would typically lead to much work for
too many people.
I do see that indeed the as.character(.) inside factor() takes
most of the CPU time used in largish factor() examples [as your
first], and indeed, for the case of integer 'x', we really could
be much faster in factor construction.
> Another thing: Function 'factor' in R devel uses 'order' instead of
'sort.list'.
This has been by a change on purpose --- well documented as new
feature in NEWS --- to allow using *methods* for order(),
i.e. for the workhorse of order, xtfrm() so that factor(OB)
works for more general objects OB.
> The case of as.factor(x) for
> x <- as.data.frame(character(0))
> in tests/isas-tests.Rout.save reveals that 'order' on data frame is
strange.
> x <- as.data.frame(character(0))
> y <- unique(x)
> length(y) # 1
> length(order(y)) # 0
> length(as.character(y)) # 1
> order(y) is not as long as as.character(y).
> Another example:
> length(mtcars) # 11
> length(order(mtcars)) # 352
I agree that order(<data.frame>) may look a bit strange;
I've spent more than an hour into looking into it, and making it
[actually, rank(<data.frame>,..) ]
an error, but ended up finding much evidence that there's too
much related code, sometimes even in base R which assumes that a
numeric data frame behaves the same as a numeric matrix.
And also, if you carefully read the help files, of
order(),
xtfrm(),
rank()
there's always mentioned that these work for R object 'x'
basically as long as x[!is.na(x)] returns a "nice"
(typically atomic) vector .. which is the case for such data frames.
The consequence, that in R-devel, currently
factor(mtcars)
just "works", is indeed unexpected or even "shocking", and I
still don't know what the most elegant and reasonable way would
be to make this an error -- as it used to be when sort.list()
was used instead of order(). I'd find it ugly (and even more
time consuming!) if factor() itself would have to check its
argument and signal an error for a data.frame.
The relevant call tree is
factor() -> order() -> xtfrm() -> xtfrm.default() -> rank()
and as I said, rank(x,*) works when x[!is.na(x)] is an atomic
"numeric-like" vector which is the case for a numeric data
frame such as 'mtcars'.
Martin Maechler
ETH Zurich
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