Question

I have a data table “the.data”, where the first column indicate a measurement instrument, and the rest different measured data.

instrument <- c(1,2,3,4,5,1,2,3,4,5)
hour <- c(1,1,1,1,1,2,2,2,2,2)
da <- c(12,14,11,14,10,19,15,16,13,11)
db <- c(21,23,22,29,28,26,24,27,26,22)
the.data <- data.frame(instrument,hour,da,db)

I also have defined groups of instruments, where for example group 1 (g1) refers to instruments 1 and 2.

g1 <- c(1,2)
g2 <- c(4,3,1)
g3 <- c(1,5,2)
g4 <- c(2,4)
g5 <- c(5,3,1,2,6)
groups <- c("g1","g2","g3","g4","g5")

I need to find out at which hour the sum of each group has maximum per data type, and its sum.

g1 hour 1: sum(da)=12+14=26
g1 hour 2: sum(da)=19+15=34

So, for g1 and da the answer is hour 2 and value 34.

I did this with a for-loop within a for-loop, but it takes too long time (I interrupted after a few hours). The issue is that the.data is about 100.000 rows long and that there are about 5.000 groups with 2-50 instruments each.

What can be a good method to do this?

Sincere thanks to all contributors to Stack-overflow.

Update: Now only five groups in examples.

/Chris

Answer 1

The group loop will have to stay, or at best be replaced by something like lapply(). The hour loop, however, can be totally replaced by reformatting to an instrument x hour matrix and then just doing vectorized algebra. For example:

library(reshape2)

groups = list(g1, g3)

the.data.a = dcast(the.data[,1:3], instrument ~ hour)

> sapply(groups, function(x) data.frame(max = max(colSums(the.data.a[x, -1])),
                                        ind = which.max(colSums(the.data.a[x, -1]))))
    [,1] [,2]
max 34   45  
ind 2    2