Try this:

subset(as.data.frame(xtabs( ~ city + var, df)), !Freq)


On Wed, Feb 2, 2011 at 1:49 PM, H Roark <hrbuilder@hotmail.com> wrote:

>
> I have a data set covering a large number of cities with values for
> characteristics such as land area, population, and employment. The problem I
> have is that some cities lack observations for some of the characteristics
> and I'd like a quick way to determine which cities have missing data.  For
> example:
>
> city<-c("A","A","A","B","B","C")
> var<-c("sqmi","pop","emp","pop","emp","pop")
> value<-c(10,100,40,30,10,20)
> df<-data.frame(city,var,value)
>
> In this data frame, city A has complete data for the three variables, while
> city B is missing land area, and city C only has population data. In the
> full data frame, my approach to finding the missing observations has been to
> create a data frame with all combinations of 'city' and 'var', merge this
> onto the original data frame, and then extract the observations with missing
> data for 'value':
>
> city_unq<-c("A","B","C")
> var_unq<-c("sqmi","pop","emp")
> comb<-expand.grid(city=city_unq,var=var_unq)
>
> mrg<-merge(comb,df,by=c("city","var"),all=T)
> missing<-mrg[is.na(mrg$value),]
>
> This works, but on a large dataset it gets slow and I'm looking for a a
> more efficient way to achieve this same result.  Any suggestions would be
> much appreciated.
>
> Cheers
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
>



-- 
Henrique Dallazuanna
Curitiba-Paraná-Brasil
25° 25' 40" S 49° 16' 22" O

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