Nice, thanks Ista!
On 3/20/2013 3:18 PM, Ista Zahn wrote:
> How about
>
> library(reshape2)
> mdf.m <- melt(my_df, measure.vars=c("a", "b", "c"))
> mdf.m <- mdf.m[mdf.m$value > 0, ]
>
> ddply(mdf.m, "variable", function(x) c("mean"=mean(x$dat), "n"=nrow(x)))
>
> ?
>
> Best,
> Ista
>
> On Wed, Mar 20, 2013 at 3:57 PM, Alexander Shenkin <ashen...@ufl.edu> wrote:
>> Hi folks,
>>
>> I'm trying to figure out how to get summarized data based on multiple
>> columns. However, instead of giving summaries for every combination of
>> categorical columns, I want it for each value of each categorical column
>> regardless of the other columns. I could do this with three different
>> commands, but i'm wondering if there's a more elegant way that I'm
>> missing. Thanks!
>>
>> allie
>>
>>> my_df = data.frame(a = c(1,1,1,0,0,0), b=c(0,0,0,1,1,1),
>> c=c(1,0,1,0,1,0), dat=c(10,11,12,13,14,15))
>>
>>> my_df
>> a b c dat
>> 1 1 0 1 10
>> 2 1 0 0 11
>> 3 1 0 1 12
>> 4 0 1 0 13
>> 5 0 1 1 14
>> 6 0 1 0 15
>>
>>> # not what I want
>>> ddply(my_df, .(a,b,c), function(x) c("mean"=mean(x$dat), "n"=nrow(x)))
>> a b c mean n
>> 1 0 1 0 14 2
>> 2 0 1 1 14 1
>> 3 1 0 0 11 1
>> 4 1 0 1 11 2
>>
>> What I want:
>> a b c mean n
>> 1 1 * * 11 3
>> 2 * 1 * 14 3
>> 3 * * 1 12 3
>>
>> where "*" refers to any value of the other columns.
>>
>> ______________________________________________
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>> and provide commented, minimal, self-contained, reproducible code.
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