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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