Ramp up/down missing time-series data in R - r

Question

I have a set of time-series data (GPS speed data, specifically), which includes gaps of missing values where the signal was lost. For missing periods of short durations I am about to fill simply using a na.spline, however this is inappropriate with longer time periods. I would like to ramp the values from the last true value down to zero, based on predefined acceleration limits.
#create sample data frame
test <- as.data.frame(c(6,5.7,5.4,5.14,4.89,4.64,4.41,4.19,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,5,5.1,5.3,5.4,5.5))
names(test)[1] <- "speed"
#set rate of acceleration for ramp
ramp <- 6
#set sampling rate of receiver
Hz <- 1/10
So for missing data the ramp would use the previous value and the rate of acceleration to get the next data point, until speed reached zero (i.e. last speed [4.19] + (Hz * ramp)), yielding the following values:
3.59
2.99
2.39
1.79
1.19
0.59
0
Lastly, I need to do this in the reverse fashion, to ramp up from zero when the signal picks back up again.
Hope this is clear.
Cheers

Answer 1

It's not really elegant, but you can do it in a loop.
na.pos <- which(is.na(test$speed))
acc = FALSE
for (i in na.pos) {
if (acc) {
speed <- test$speed[i-1]+(Hz*ramp)
}
else {
speed <- test$speed[i-1]-(Hz*ramp)
if (round(speed,1) < 0) {
acc <- TRUE
speed <- test$speed[i-1]+(Hz*ramp)
}
}
test[i,] <- speed
}
The result is:
speed
1 6.00
2 5.70
3 5.40
4 5.14
5 4.89
6 4.64
7 4.41
8 4.19
9 3.59
10 2.99
11 2.39
12 1.79
13 1.19
14 0.59
15 -0.01
16 0.59
17 1.19
18 1.79
19 2.39
20 2.99
21 3.59
22 4.19
23 4.79
24 5.00
25 5.10
26 5.30
27 5.40
28 5.50
Note that '-0.01', because 0.59-(6*10) is -0.01, not 0. You can round it later, I decided not to.

Answer 2

When the question says "ramp the values from the last true value down to zero" in each run of NAs I assume that that means that any remaining NAs in the run after reaching zero are also to be replaced by zero.
Now, use rleid from data.table to create a grouping vector the same length as test$speed identifying each run in is.na(test$speed) and use ave to create sequence numbers within such groups, seqno. Then calculate the declining sequences, ramp_down by combining na.locf(test$speed) and seqno. Finally replace the NAs.
library(data.table)
library(zoo)
test_speed <- test$speed
seqno <- ave(test_speed, rleid(is.na(test_speed)), FUN = seq_along)
ramp_down <- pmax(na.locf(test_speed) - seqno * ramp * Hz, 0)
result <- ifelse(is.na(test_speed), ramp_down, test_speed)
giving:
> result
[1] 6.00 5.70 5.40 5.14 4.89 4.64 4.41 4.19 3.59 2.99 2.39 1.79 1.19 0.59 0.00
[16] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.00 5.10 5.30 5.40 5.50