[R] computing marginal values based on multiple columns?

[Gerrit Eichner]

Hello, Simon,

see below!


On Tue, 4 Dec 2012, Simon wrote:

> Hello all,
>
> I have what feels like a simple problem, but I can't find an simple
> answer. Consider this data frame:
>
>> x <- data.frame(sample1=c(35,176,182,193,124),
> sample2=c(198,176,190,23,15), sample3=c(12,154,21,191,156),
> class=c('a','a','c','b','c'))
>
>> x
>  sample1 sample2 sample3 class
> 1      35     198      12     a
> 2     176     176     154     a
> 3     182     190      21     c
> 4     193      23     191     b
> 5     124      15     156     c
>
> Now I wish to know: for each sample, for values < 20% of the sample mean,
> what percentage of those are class a?
>
> I want to end up with a table like:
>
>   sample1 sample2 sample3
> 1      1.0     0     0.5


I can't reproduce this result from your description above, but if I 
understand the latter correctly, maybe the following does what you want:

x.wo.class <- subset( x, select = -class)
   # extract only the sample-columns

x.small.and.a <- x.wo.class < 0.2 * colMeans( x.wo.class) & x$class == "a"

apply( x.small.and.a, 2, function( xx) mean( x$class[ xx] == "a"))


  Hth  --  Gerrit


> I can calculate this for an individual sample using this rather clumsy
> expression:
>
> length(which(x$sample1 < mean(x$sample1) & x$class=='a')) /
> length(which(x$sample1 < mean(x$sample1)))
>
> I'd normally propagate it across the data frame using apply, but I
> can't because it depends on more than one column.
>
> Any help much appreciated!
>
> Cheers,
>
> Simon
>
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>
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