Re: [R] Studdy Missing Data, differentiate between a percent with in the valid answers and with in the different missing answers

From: Frank E Harrell Jr <>
Date: Mon, 03 Mar 2008 07:21:21 -0600

James Reilly wrote:
> On 3/3/08 8:21 PM, Ericka Lundström wrote:
> > I'm trying to emigrate from SPSS to R, thou I have some problems whit
> > getting R to distinguish between the different kind of missing.
> ...
> > Is there a smart way in R to differentiate between missing and valid
> > and at the same time treat both the categories within missing and
> > valid as answers (like SPSS did above)
> The Hmisc package has some support for special missing values, for
> instance when reading in SAS datasets using sas.get. I don't believe
> spss.get offers the same facility, though.
> You can define special missing values for a variable manually, which
> might seem a bit involved, but this could easily be automated. For your
> example, try:
> special <- dataFrame$TWO %in% c("?","X")
> attr(dataFrame$TWO, "special.miss") <-
> list(codes=as.character(dataFrame$TWO[special]),
> obs=(1:length(dataFrame$TWO))[special])
> class(dataFrame$TWO) <- c("factor", "special.miss")
>$TWO) <- special
> # Then describe gives new percentages
> describe(dataFrame$TWO)
> dataFrame$TWO
> n missing ? X unique
> 3 4 2 2 2
> No (2, 67%), yes (1, 33%)
> HTH,
> James

Thanks for pointing out how this can be done with Hmisc, James. If the foreign package can sense SPSS special missing values in general, it would not be hard to add the special.miss mechanism to spss.get in Hmisc.


Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University

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Received on Mon 03 Mar 2008 - 13:29:56 GMT

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