This is a useful tip, but there is some confusion in the thread that makes it
hard to follow. Just clarifying, I hope, so that folks can easily follow it
in the future.
The original posting had a set of encounter histories with descriptions that
did not match the histories. There were 2 males and 1 female for the first
history and 4 males for the second history (but the descriptions indicated
that there was 1 male and 3 females). In the example subsequently posted,
the first history is the same but the second history includes 3 males and 1
female (the opposite of what was originally described):
> I am sorry but here is an example
>> test
>> t1 t2 t3 t4 M F id
>> 1 1 0 0 0 1 0 1
>> 2 1 0 0 0 1 0 1
>> 3 1 0 0 0 0 1 1
>> 4 1 0 1 1 1 0 1
>> 5 1 0 1 1 1 0 1
>> 6 1 0 1 1 1 0 1
>> 7 1 0 1 1 0 1 1
>
Working from the provided example above (3 males and 1 female for the second
history), all that is needed to condense this set of histories is the
following (I dropped the ID column because I didn't see why it was
included):
> library(reshape)
> tmp1 <- read.table("test.txt", header=T)
> tmp2 <- melt(tmp1, measure.var=c("M", "F"))
> condensed <- cast(tmp2, t1 + t2 + t3 + t4 ~ variable,
fun.aggregate=sum)
> condensed
t1 t2 t3 t4 M F
1 1 0 0 0 2 1
2 1 0 1 1 3 1
Once "M" and "F" are included in the 'melt' command they cannot be used in
the 'cast' command.
A more compact solution comes from 'recast':
> tmp3 <- recast(tmp1, t1 + t2 + t3 + t4 ~ variable,
> fun.aggregate=sum, measure.var=c("M", "F"))
> tmp3
t1 t2 t3 t4 M F
1 1 0 0 0 2 1
2 1 0 1 1 3 1
-----
David Hewitt
Research Fishery Biologist
USGS Klamath Falls Field Station (USA)
--
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