[Rd] [External] Re: 1954 from NA

[Greg Warnes]

I would be glad to add this to one of my R packages, probably `gdata`..

-G

Gregory R. Warnes, Ph.D.
greg using warnes.net
Eternity is a long time, take a friend!


> On May 26, 2021, at 1:09 PM, Adrian Dușa <dusa.adrian using gmail.com> wrote:
> 
> Yes, that is even better.
> Best,
> Adrian
> 
> On Wed, May 26, 2021 at 7:05 PM Duncan Murdoch <murdoch.duncan using gmail.com <mailto:murdoch.duncan using gmail.com>>
> wrote:
> 
>> After 5 minutes more thought:
>> 
>> - code non-missing as missingKind = NA, not 0, so that missingKind could
>> be a character vector, or missingKind = 0 could be supported.
>> 
>> - print methods should return the main argument, so mine should be
>> 
>> print.MultiMissing <- function(x, ...) {
>>   vals <- as.character(x)
>>   if (!is.character(x) || inherits(x, "noquote"))
>>     print(noquote(vals))
>>   else
>>     print(vals)
>>   invisible(x)
>> }
>> 
>> This still needs a lot of improvement to be a good print method, but
>> I'll leave that to you.
>> 
>> Duncan Murdoch
>> 
>> On 26/05/2021 11:43 a.m., Duncan Murdoch wrote:
>>> 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.duncan using gmail.com
>>>> <mailto:murdoch.duncan using 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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