HI,
I am not sure the output you wanted is correct:
"
sample1 sample2 sample3
1 1.0 0 0.5
"
because
0.2*colMeans(x[,-4])
sample1 sample2 sample3
# 28.40 24.08 21.36
This might help you:
apply(x[-4],2,function(y) length(y[y <0.2*mean(y) &
x$class=="a"])/length(x[x$class=="a"]))
#sample1 sample2 sample3
# 0.0 0.0 0.5
A.K.
----- Original Message -----
From: Simon <simonzm...@gmail.com>
To: r-help@r-project.org
Cc:
Sent: Tuesday, December 4, 2012 4:49 AM
Subject: [R] computing marginal values based on multiple columns?
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 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
[[alternative HTML version deleted]]
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