# create a matrix with some random NAs in it
> m<-matrix(NA,nrow=15,ncol=14)
> m[,3:14]<-52
> m[13,9]<-NA
> m[4:7,8]<-NA
> m[1:2,5]<-NA
> m[,2]<-rep(1800:1804, by=3)
> y<-order(m[,2])
> m<-m[y,]
> m[,1]<-rep(1:3,by=5)
> m
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[,14]
[1,] 1 1800 52 52 NA 52 52 52 52 52 52 52 52
52
[2,] 2 1800 52 52 52 52 52 NA 52 52 52 52 52
52
[3,] 3 1800 52 52 52 52 52 52 52 52 52 52 52
52
[4,] 1 1801 52 52 NA 52 52 52 52 52 52 52 52
52
[5,] 2 1801 52 52 52 52 52 NA 52 52 52 52 52
52
[6,] 3 1801 52 52 52 52 52 52 52 52 52 52 52
52
[7,] 1 1802 52 52 52 52 52 52 52 52 52 52 52
52
[8,] 2 1802 52 52 52 52 52 52 52 52 52 52 52
52
[9,] 3 1802 52 52 52 52 52 52 NA 52 52 52 52
52
[10,] 1 1803 52 52 52 52 52 NA 52 52 52 52 52
52
[11,] 2 1803 52 52 52 52 52 52 52 52 52 52 52
52
[12,] 3 1803 52 52 52 52 52 52 52 52 52 52 52
52
[13,] 1 1804 52 52 52 52 52 NA 52 52 52 52 52
52
[14,] 2 1804 52 52 52 52 52 52 52 52 52 52 52
52
[15,] 3 1804 52 52 52 52 52 52 52 52 52 52 52
52
# the goal is to count all NON NA by changes in column 2
# we can get the count for all rows easily.
> col.sum<-(apply(!is.na(m[,3:14]),2,sum))
> col.sum
[1] 15 15 13 15 15 11 14 15 15 15 15 15
# what we want is a result that looks like this
1800 3 3 2 3 3 2 3 3 3 3 3 3
1801 3 3 2 3 3 2 3 3 3 3 3 3
1802 3 3 3 3 3 3 2 3 3 3 3 3
1803 3 3 3 3 3 2 3 3 3 3 3 3
1804 3 3 3 3 3 2 3 3 3 3 3 3
I've toyed a bit with By
> mask<-!is.na(m[,3:14])
> test<-cbind(m[,1:2],mask)
> test
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[,14]
[1,] 1 1800 1 1 0 1 1 1 1 1 1 1 1
1
[2,] 2 1800 1 1 1 1 1 0 1 1 1 1 1
1
[3,] 3 1800 1 1 1 1 1 1 1 1 1 1 1
1
[4,] 1 1801 1 1 0 1 1 1 1 1 1 1 1
1
[5,] 2 1801 1 1 1 1 1 0 1 1 1 1 1
1
[6,] 3 1801 1 1 1 1 1 1 1 1 1 1 1
1
[7,] 1 1802 1 1 1 1 1 1 1 1 1 1 1
1
[8,] 2 1802 1 1 1 1 1 1 1 1 1 1 1
1
[9,] 3 1802 1 1 1 1 1 1 0 1 1 1 1
1
[10,] 1 1803 1 1 1 1 1 0 1 1 1 1 1
1
[11,] 2 1803 1 1 1 1 1 1 1 1 1 1 1
1
[12,] 3 1803 1 1 1 1 1 1 1 1 1 1 1
1
[13,] 1 1804 1 1 1 1 1 0 1 1 1 1 1
1
[14,] 2 1804 1 1 1 1 1 1 1 1 1 1 1
1
[15,] 3 1804 1 1 1 1 1 1 1 1 1 1 1
1
> result<-by(test[,3:14],test[,2], sum)
> result
INDICES: 1800
[1] 34
---------------------------------------------------------------------------------------------------------
INDICES: 1801
[1] 34
---------------------------------------------------------------------------------------------------------
INDICES: 1802
[1] 35
---------------------------------------------------------------------------------------------------------
INDICES: 1803
[1] 35
---------------------------------------------------------------------------------------------------------
INDICES: 1804
[1] 35
>
as this sums all the values and not by column. it's wrong
so is there an elegant way to get the number of
NON Nas.. by column governed by changes in the values of a variable.
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