[R] linear interpolation of multiple random time series

[Mike Lawrence]

Hi all,

Looking for tips on how I might more optimally solve this. I have  
time series data (samples from a force sensor) that are not  
guaranteed to be sampled at the same time values across trials. ex.

trial	time	x
1	1	1
1	5	4
1	7	9
1	12	20
2	1	0
2	3	5
2	9	10
2	13	14
2	19	22
2	24	32

Within each trial I'd like to use linear interpolation between each  
successive time sample to fill in intermediary timepoints and x- 
values, ex.

trial	time	x
1	1	1
1	2	1.75
1	3	2.5
1	4	3.25
1	5	4
1	6	6.5
1	7	9
1	8	11.2
1	9	13.4
1	10	15.6
1	11	17.8
1	12	20
2	1	0
2	2	2.5
2	3	5
2	4	5.83333333333333
2	5	6.66666666666667
2	6	7.5
2	7	8.33333333333333
2	8	9.16666666666667
2	9	10
2	10	11
2	11	12
2	12	13
2	13	14
2	14	15.3333333333333
2	15	16.6666666666667
2	16	18
2	17	19.3333333333333
2	18	20.6666666666667
2	19	22
2	20	24
2	21	26
2	22	28
2	23	30
2	24	32


The solution I've coded (below) involves going through the original  
data frame line by line and is thus very slow (indeed, I had to  
resort to writing to file as with a large data set I started running  
into memory issues if I tried to create the new data frame in  
memory). Any suggestions on a faster way to achieve what I'm trying  
to do?

#assumes the first data frame above is stored as 'a'
arows = (length(a$x)-1)
write('', 'temp.txt')
for(i in 1:arows){
	if(a$time[i+1] > a$time[i]){
		write.table(a[i,], 'temp.txt', row.names = F, col.names = F, append  
= T)
		x1 = a$time[i]
		x2 = a$time[i+1]
		dx = x2-x1
		if(dx != 1){
			y1 = a$x[i]
			y2 = a$x[i+1]
			dy = y2-y1
			slope = dy/dx
			int = -slope*x1+y1
			temp=a[i,]
			for(j in (x1+1):(x2-1)){
				temp$time = j
				temp$x = slope*j+int
				write.table(temp, 'temp.txt', row.names = F, col.names = F,  
append = T)
			}
		}
	}else{
		write.table(a[i,], 'temp.txt', row.names = F, col.names = F, append  
= T)
	}
}
i=i+1
write.table(a[i,], 'temp.txt', row.names = F, col.names = F, append = T)

b=read.table('temp.txt',skip=1)
names(b)=names(a)