Actually the second aggregate and second rowsum don't need the na.rm = TRUE
so we only need:
aggregate(!is.na(m[, -(1:2)]), m[1], sum)
rowsum(0+!is.na(m[, -(1:2)]), m[,1])
You might also want to look at summaryBy in the doBy package.
On Sun, Dec 7, 2008 at 7:43 AM, Gabor Grothendieck
<[EMAIL PROTECTED]> wrote:
> Try
>
> aggregate(m[, -(1:2)], m[1], sum, na.rm = TRUE)
> aggregate(!is.na(m[, -(1:2)]), m[1], sum, na.rm = TRUE)
>
> # or (this uses row names rather than a column for the group):
>
> rowsum(m[, -(1:2)], m[,1], na.rm = TRUE)
> rowsum(0+!is.na(m[, -(1:2)]), m[,1], na.rm = TRUE)
>
>
> On Sun, Dec 7, 2008 at 7:06 AM, Daren Tan <[EMAIL PROTECTED]> wrote:
>>
>> The aggregate function does "almost" all that I need to summarize a
>> datasets, except that I can't specify exclusion of NAs without a little bit
>> of hassle.
>>
>>> set.seed(143)
>>> m <- data.frame(A=sample(LETTERS[1:5], 20, T), B=sample(LETTERS[1:10], 20,
>>> T), C=sample(c(NA, 1:4), 20, T), D=sample(c(NA,1:4), 20, T))
>>> m
>> A B C D
>> 1 E I 1 NA
>> 2 A C NA NA
>> 3 D I NA 3
>> 4 C I 2 4
>> 5 A C 3 2
>> 6 E J 1 2
>> 7 D J 2 2
>> 8 C G 4 1
>> 9 C D NA 3
>> 10 B G 3 NA
>> 11 C B 4 2
>> 12 A B NA NA
>> 13 E A NA 4
>> 14 B B 3 3
>> 15 E I 4 1
>> 16 E J 3 1
>> 17 B J 4 4
>> 18 B J 1 3
>> 19 D D 4 2
>> 20 B B 4 3
>>
>>> aggregate(m[,-c(1:2)], by=list(m[,1]), sum)
>> Group.1 C D
>> 1 A NA NA
>> 2 B 15 NA
>> 3 C NA 10
>> 4 D NA 7
>> 5 E NA NA
>>
>>> aggregate(m[,-c(1:2)], by=list(m[,1]), length)
>> Group.1 C D
>> 1 A 3 3
>> 2 B 5 5
>> 3 C 4 4
>> 4 D 3 3
>> 5 E 5 5
>>
>> My own defined version of length and sum to exclude NA
>>
>>> mylength <- function(x) { sum(as.logical(x), na.rm=T) }
>>> mysum <- function(x) {sum(x, na.rm=T)}
>>
>>> aggregate(m[,-c(1:2)], by=list(m[,1]), mysum) <----------------- this
>>> computes correctly.
>> Group.1 C D
>> 1 A 3 2
>> 2 B 15 13
>> 3 C 10 10
>> 4 D 6 7
>> 5 E 9 8
>>
>>> aggregate(m[,-c(1:2)], by=list(m[,1]), mylength) <----------------- this
>>> computes correctly.
>> Group.1 C D
>> 1 A 1 1
>> 2 B 5 4
>> 3 C 3 4
>> 4 D 2 3
>> 5 E 4 4
>>
>> There are other statistics I need to compute e.g. var, sd, and it is a
>> hassle to create customized versions to exclude NA. Any alternative
>> approaches ?
>>
>>
>>
>>
>> _________________________________________________________________
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>>
>> ______________________________________________
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>> and provide commented, minimal, self-contained, reproducible code.
>>
>
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