On 26/05/2021 10:22 a.m., Adrian Dușa wrote:
Dear Duncan,
On Wed, May 26, 2021 at 2:27 AM Duncan Murdoch <murdoch.dun...@gmail.com
<mailto:murdoch.dun...@gmail.com>> wrote:
You've already been told how to solve this: just add attributes to the
objects. Use the standard NA to indicate that there is some kind of
missingness, and the attribute to describe exactly what it is. Stick a
class on those objects and define methods so that subsetting and
arithmetic preserves the extra info you've added. If you do some
operation that turns those NAs into NaNs, big deal: the attribute will
still be there, and is.na <http://is.na>(NaN) still returns TRUE.
I've already tried the attributes way, it is not so easy.
If you have specific operations that are needed but that you can't get
to work, post the issue here.
In the best case scenario, it unnecessarily triples the size of the
data, but perhaps this is the only way forward.
I don't see how it could triple the size. Surely an integer has enough
values to cover all possible kinds of missingness. So on integer or
factor data you'd double the size, on real or character data you'd
increase it by 50%. (This is assuming you're on a 64 bit platform with
32 bit integers and 64 bit reals and pointers.)
Here's a tiny implementation to show what I'm talking about:
asMultiMissing <- function(x) {
if (isMultiMissing(x))
return(x)
missingKind <- ifelse(is.na(x), 1, 0)
structure(x,
missingKind = missingKind,
class = c("MultiMissing", class(x)))
}
isMultiMissing <- function(x)
inherits(x, "MultiMissing")
missingKind <- function(x) {
if (isMultiMissing(x))
attr(x, "missingKind")
else
ifelse(is.na(x), 1, 0)
}
`missingKind<-` <- function(x, value) {
class(x) <- setdiff(class(x), "MultiMissing")
x[value != 0] <- NA
x <- asMultiMissing(x)
attr(x, "missingKind") <- value
x
}
`[.MultiMissing` <- function(x, i, ...) {
missings <- missingKind(x)
x <- NextMethod()
missings <- missings[i]
missingKind(x) <- missings
x
}
print.MultiMissing <- function(x, ...) {
vals <- as.character(x)
if (!is.character(x) || inherits(x, "noquote"))
print(noquote(vals))
else
print(vals)
}
`[<-.MultiMissing` <- function(x, i, value, ...) {
missings <- missingKind(x)
class(x) <- setdiff(class(x), "MultiMissing")
x[i] <- value
missings[i] <- missingKind(value)
missingKind(x) <- missings
x
}
as.character.MultiMissing <- function(x, ...) {
missings <- missingKind(x)
result <- NextMethod()
ifelse(missings != 0,
paste0("NA.", missings), result)
}
This is incomplete. It doesn't do printing very well, and it doesn't
handle the case of assigning a MultiMissing value to a regular vector at
all. (I think you'd need an S4 implementation if you want to support
that.) But it does the basics:
> x <- 1:10
> missingKind(x)[4] <- 23
> x
[1] 1 2 3 NA.23 5 6 7 8 9
[10] 10
> is.na(x)
[1] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[10] FALSE
> missingKind(x)
[1] 0 0 0 23 0 0 0 0 0 0
>
Duncan Murdoch
Base R doesn't need anything else.
You complained that users shouldn't need to know about attributes, and
they won't: you, as the author of the package that does this, will
handle all those details. Working in your subject area you know all
the
different kinds of NAs that people care about, and how they code
them in
input data, so you can make it all totally transparent. If you do it
well, someone in some other subject area with a completely different
set
of kinds of missingness will be able to adapt your code to their use.
But that is the whole point: the package author does not define possible
NAs (the possibilities are infinite), users do that.
The package should only provide a simple method to achieve that.
I imagine this has all been done in one of the thousands of packages on
CRAN, but if it hasn't been done well enough for you, do it better.
If it were, I would have found it by now...
Best wishes,
Adrian
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