Interpolating timeseries

You can also use approx function like this and it will be much easier. Just make sure that you are working with data frames. Also, make sure that format of the column in calibration and sample data-set are the same by using as.POSIXct.

 calib <- data.frame(calib); sample <- data.frame(sample)

 IPcal <- data.frame(approx(calib$time,calib$value, xout = sample$time, 
                 rule = 2, method = "linear", ties = mean))

 head(IPcal)

#                x        y
#1 2017-03-22 01:00:52 252.3000
#2 2017-03-22 01:03:02 251.1142
#3 2017-03-22 01:05:23 249.9617
#4 2017-03-22 01:07:42 252.7707
#5 2017-03-22 01:10:12 255.6000

Read more about approx on approxfun documentation.

I would use zoo (or xts) and do it like this:

library(zoo)
# Create zoo objects
zc <- zoo(calib$value, calib$time)    # low freq
zs <- zoo(sample$value, sample$time)  # high freq
# Merge series into one object
z <- merge(zs,zc)
# Interpolate calibration data (na.spline could also be used)
z$zc <- na.approx(z$zc, rule=2)
# Only keep index values from sample data
Z <- z[index(zs),]
Z
#                      zs       zc
# 2012-10-25 01:00:52 256 252.3000
# 2012-10-25 01:03:02 254 251.1142
# 2012-10-25 01:05:23 255 249.9617
# 2012-10-25 01:07:42 257 252.7707
# 2012-10-25 01:10:12 256 255.6000