How to combine two columns of time in R?

Question

I have two text files:
1-

> head(val)
      V1       V2       V3 
1 2015/03/31  00:00    0.134  
2 2015/03/31  01:00    0.130  
3 2015/03/31  02:00    0.133  
4 2015/03/31  03:00    0.132  

2-

> head(tes)
          A        B      date
1        0.04     0.02  2015-03-31 02:18:56

What I need is to combine V1 (date) and V2 (hour) in val . search in val the date and time that correspond (the closest) to date in tes and then extract the corresponding V3 and put it in tes .

the desired out put would be:

 tes
           A        B      date                  V3
1        0.04     0.02  2015-04-01 02:18:56     0.133

Answer 1

Updated answer based on OP's comments.

val$date <- with(val,as.POSIXct(paste(V1,V2), format="%Y/%m/%d %H:%M"))
val
#           V1    V2    V3                date
# 1 2015/03/31 00:00 0.134 2015-03-31 00:00:00
# 2 2015/03/31 01:00 0.130 2015-03-31 01:00:00
# 3 2015/03/31 02:00 0.133 2015-03-31 02:00:00
# 4 2015/03/31 03:00 0.132 2015-03-31 03:00:00
# 5 2015/04/07 13:00 0.080 2015-04-07 13:00:00
# 6 2015/04/07 14:00 0.082 2015-04-07 14:00:00

tes$date <- as.POSIXct(tes$date)
tes
#      A    B                date
# 1 0.04 0.02 2015-03-31 02:18:56
# 2 0.05 0.03 2015-03-31 03:30:56
# 3 0.06 0.04 2015-03-31 05:30:56
# 4 0.07 0.05 2015-04-07 13:42:56

f <- function(d) {  # for given tes$date, find val$V3
  diff <- abs(difftime(val$date,d,units="min"))
  if (min(diff > 45))  Inf else which.min(diff)
}
tes  <- cbind(tes,val[sapply(tes$date,f),c("date","V3")])
tes

#      A    B                date                date    V3
# 1 0.04 0.02 2015-03-31 02:18:56 2015-03-31 02:00:00 0.133
# 2 0.05 0.03 2015-03-31 03:30:56 2015-03-31 03:00:00 0.132
# 3 0.06 0.04 2015-03-31 05:30:56                <NA>    NA
# 4 0.07 0.05 2015-04-07 13:42:56 2015-04-07 14:00:00 0.082

The function f(...) calculates the index into val (the row number) for which val$date is closest in time to the given tes$date , unless that time is > 45 min, in which case Inf is returned. Using this function with sapply(...) as in:

 sapply(tes$date, f)

returns a vector of row numbers in val matching your condition for each test$date .

The reason we use Inf instead of NA for missing values is that indexing a data.frame using Inf always returns a single "row" containing NA , whereas indexing using NA returns nrow(...) rows all containing NA .

I added the extra rows into val and tes per your comment.