linear interpolate 15 Hz time series to match with 25 Hz time series in R - r

Question

Hi I have the following data recorded with 15Hz and I want to resample it using linear interpolation to 25 Hz. What is the best way to achieve this?
Here is the first second of my data set:
RecordFile YTSIMTMD RBDDLO_0 RBDDGS_0 IDLWMWC1 time timeNF
864 2C01MUC.txx 85535.10 -0.31 -0.348873 1 0.00000 0
865 2C01MUC.txx 85535.17 -0.31 -0.348873 1 0.06667 6667
866 2C01MUC.txx 85535.23 -0.31 -0.348873 0 0.13334 13334
867 2C01MUC.txx 85535.30 -0.31 -0.348832 0 0.20000 20000
868 2C01MUC.txx 85535.37 -0.31 -0.348832 0 0.26667 26667
869 2C01MUC.txx 85535.43 -0.31 -0.348832 0 0.33334 33334
870 2C01MUC.txx 85535.50 -0.31 -0.348832 1 0.40000 40000
871 2C01MUC.txx 85535.57 -0.31 -0.348796 1 0.46667 46667
872 2C01MUC.txx 85535.63 -0.31 -0.348796 1 0.53334 53334
873 2C01MUC.txx 85535.70 -0.31 -0.348796 1 0.60000 60000
874 2C01MUC.txx 85535.77 -0.31 -0.348796 0 0.66667 66667
875 2C01MUC.txx 85535.83 -0.31 -0.348767 0 0.73334 73334
876 2C01MUC.txx 85535.90 -0.31 -0.348767 0 0.80000 80000
877 2C01MUC.txx 85535.97 -0.31 -0.348767 0 0.86667 86667
878 2C01MUC.txx 85536.03 -0.31 -0.348767 1 0.93334 93334
879 2C01MUC.txx 85536.10 -0.31 -0.348735 1 1.00000 100000
After that I want to match it with this data set recorded with 25 Hz
vpName vpID origIndex areaNum areaName startMS endMS durationMS startF endF durationF accumIndex
1 2C01 1 1 2 ATT 0 560 560 0 14 14 1
2 2C01 1 1 2 ATT 0 560 560 0 14 14 1
3 2C01 1 1 2 ATT 0 560 560 0 14 14 1
4 2C01 1 1 2 ATT 0 560 560 0 14 14 1
5 2C01 1 1 2 ATT 0 560 560 0 14 14 1
6 2C01 1 1 2 ATT 0 560 560 0 14 14 1
I found that approx seems to be the linear interpolation for linear interpolation in R, however I am not sure which parameters to use to upsample my data from 15 to 25 Hz?
There seem to be explicit packages for handling time series in R like zoo and xts, but I am not sure whether I need them.
Both data sets start at the same time, so after upsampling I could simply match by rownumber.
Thank your for your help!

Answer 1

I'll make some assumptions - first, that data columns "YTSIMTMD" "RBDDLO_0" and "RBDDGS_0" contain continuous data so linear interpolation can be used. Second, that column IDLWMWC1 contains binary data so we will interpolate using method=constant which selects the data value at the last data time prior to the interpolation time. Given this, the following uses approx to do the interpolations and combine them into a data frame. The interpolation times are generated at a time interval of 1/freq. I put your data into a data frame called xx.
t_seq <- seq(min(xx$time), max(xx$time),1/25)
ap <- cbind(t_seq, sapply(xx[,c("YTSIMTMD", "RBDDLO_0","RBDDGS_0")],
function(y, x, nout) approx(x, y, nout, method="linear")$y, x=xx$time, nout=t_seq ))
ap <- cbind(ap,IDLWMWC1=approx(xx$time, xx$IDLWMWC1, t_seq, method="constant")$y)
I don't quite understand how your second set of data relates to the first but if it's just additional information at intervals of 1/25 starting at the same time, you could just combine the two data frame using cbind.

Answer 2

Here's an example, using approxfun to create a function with the linear fit to the input data:
xin<-seq(1,26,by=5)
yin<-2.5+3*xin
myfun<-approxfun(xin,yin)
plot(xin,yin)
newy<-myfun(seq(3,18,by=5))
points(seq(3,18,by=5),newy)
points(seq(3,18,by=5),newy,col='red')
In your case, the inputs aretime for x-values and whatever you are working with for y-values. Then just feed a sequence of "new" x values at 25Hz intervals (0.04 seconds) to get the fitted values you want.