The smallest date of `v` which makes the difference `w-v` positive

Question

From these vectors of dates

v<-c("2019-12-06 01:32:30 UTC","2019-12-31 18:44:31 UTC","2020-01-29 22:18:25 UTC","2020-03-22 16:44:29 UTC")
v<-as.POSIXct(v)
w<-c("2019-12-07 00:11:46","2020-01-01 05:29:45","2019-12-08 02:54:10","2020-03-23 07:48:26","2020-02-02 16:58:16","2020-01-31 06:46:46")
w<-as.POSIXct(w)

I would like to obtain a dataframe of two columns. One of them is just w. The second is built on v entries so that in the row there is the smallest date of v which makes the difference w-v positive. For example, the difference

w-rep(v[1],length(w))
Time differences in hours
[1]   22.65444  627.95417   49.36111 2598.26556 1407.42944 1349.23778

Then, if the second column of the desired dataframe is w, then the first one has at the first row the date 2019-12-06 01:32:30 UTC. The operation should be:

date <- w-rep(v[1],length(w))
v[date==min(date[date>0])]

Then the first row of the dataframe should be

2019-12-06 01:32:30 UTC, 2019-12-07 00:11:46

How could I build the others row wihtout using loops?

Answer 1

How about this:

o <- outer(w, v, `-`)
o
# Time differences in hours
#            [,1]       [,2]        [,3]        [,4]
# [1,]   22.65444 -594.54583 -1294.11083 -2559.54528
# [2,]  627.95417   10.75389  -688.81111 -1954.24556
# [3,]   49.36111 -567.83917 -1267.40417 -2532.83861
# [4,] 2597.26556 1980.06528  1280.50028    15.06583
# [5,] 1407.42944  790.22917    90.66417 -1174.77028
# [6,] 1349.23778  732.03750    32.47250 -1232.96194

We don't want negative values, so

o[o < 0] <- NA
o
# Time differences in hours
#            [,1]       [,2]       [,3]     [,4]
# [1,]   22.65444         NA         NA       NA
# [2,]  627.95417   10.75389         NA       NA
# [3,]   49.36111         NA         NA       NA
# [4,] 2597.26556 1980.06528 1280.50028 15.06583
# [5,] 1407.42944  790.22917   90.66417       NA
# [6,] 1349.23778  732.03750   32.47250       NA

Now just apply which.min on each row, then subset v on this value:

apply(o, 1, which.min)
# [1] 1 2 1 4 3 3
v[apply(o, 1, which.min)]
# [1] "2019-12-06 01:32:30 EST" "2019-12-31 18:44:31 EST" "2019-12-06 01:32:30 EST" "2020-03-22 16:44:29 EDT"
# [5] "2020-01-29 22:18:25 EST" "2020-01-29 22:18:25 EST"
data.frame(w=w, v2=v[apply(o, 1, which.min)])
#                     w                  v2
# 1 2019-12-07 00:11:46 2019-12-06 01:32:30
# 2 2020-01-01 05:29:45 2019-12-31 18:44:31
# 3 2019-12-08 02:54:10 2019-12-06 01:32:30
# 4 2020-03-23 07:48:26 2020-03-22 16:44:29
# 5 2020-02-02 16:58:16 2020-01-29 22:18:25
# 6 2020-01-31 06:46:46 2020-01-29 22:18:25