Pairing rows in data.table - r

Question

I have a data table similar to the one obtained with the following command:
dt <- data.table(
time = 1:8,
part = rep(c(1, 1, 2, 2), 2),
type = rep(c('A', 'B'), 4),
data = rep(c(runif(1), 0), 4))
Basically, such a table contains two different type of instances (A or B). The time column contains a timestamp for when a request arrived to or leaved from a certain part. If the instance type is A, the timestamp states the arrival time (enter), and if the type is B, the timestamp states the leaving time (exit).
time part type data
1: 1 1 A 0.5842668
2: 2 1 B 0.0000000
3: 3 2 A 0.5842668
4: 4 2 B 0.0000000
5: 5 1 A 0.5842668
6: 6 1 B 0.0000000
7: 7 2 A 0.5842668
8: 8 2 B 0.0000000
I would like to pair A and B instances, and obtain the following data table:
part data enter.time exit.time
1: 1 0.4658239 1 2
2: 1 0.4658239 5 6
3: 2 0.4658239 3 4
4: 2 0.4658239 7 8
I have tried the following:
pair.types <- function(x) {
a.type <- x[type == 'A']
b.type <- x[type == 'B']
return(data.table(
enter.time = a.type$time,
exit.time = b.type$time,
data = a.type$data))
}
dt[, c('enter.time', 'exit.time', 'data') := pair.types(.SD), by = list(part)]
But, that gives me the following, which is not exactly what I want:
time part type data enter.time exit.time
1: 1 1 A 0.3441592 1 2
2: 2 1 B 0.3441592 5 6
3: 3 2 A 0.3441592 3 4
4: 4 2 B 0.3441592 7 8
5: 5 1 A 0.3441592 1 2
6: 6 1 B 0.3441592 5 6
7: 7 2 A 0.3441592 3 4
8: 8 2 B 0.3441592 7 8
It is kind of close, but since column 'type' is kept, some rows are duplicated. Perhaps, I can try to remove columns 'time' and 'type', and then remove the second half of rows. But, I am not sure whether that will work in all the cases, and I would like to learn a better way to do this operation.

Answer 1

Assuming your data looks like your example data:
dt[, list(part = part[1],
data = data[1],
enter.time = time[1],
exit.time = time[2]),
by = as.integer((seq_len(nrow(dt)) + 1)/2)]
# by = rep(seq(1, nrow(dt), 2), each = 2)]
# ^^^ a slightly shorter and a little more readable alternative
The idea is very simple - group rows in groups of 2 (that's the by part), i.e. each group will be one A and one B, then for each group take first part and first data and then the enter and exit times are just the first and second time's respectively. This is likely how you'd do this if you followed the by-hand logic, making it easy to read (once you know just a tiny bit about how data.table works).

Answer 2

Another way:
setkey(dt, "type")
dt.out <- cbind(dt[J("A"), list(part, data, entry.time = time)][, type := NULL],
exit.time = dt[J("B"), list(time)]$time)
# part data entry.time exit.time
# 1: 1 0.1294204 1 2
# 2: 2 0.1294204 3 4
# 3: 1 0.1294204 5 6
# 4: 2 0.1294204 7 8
If you want you can now do setkey(dt.out, "part") to get the same order.
The idea: Your problem seems a simple "reshaping" one to me. The way I've approached it is first to create a key column as type. Now, we can subset data.table for a specific value in the key column by: dt[J("A")]. This would return the entire data.table. Since you want the column time renamed, I explicitly mention which columns to subset using:
dt[J("A"), list(part, data, entry.time = time)]
Of course this'll return also the type column (= A) which we've to remove. So, I've added a [, type := NULL] to remove column type by reference.
Now we've the first part. All we need is the exit.time. This can be obtained similarly as:
dt[J("B"), list(time)] # I don't name the column here
But this gives a data.table when you need just the time column, which can be accessed by:
dt[J("B"), list(time)]$time
So, while using cbind I name this column as exit.time to get the final result as:
cbind(dt[J("A"), list(part, data, entry.time = time)][, type := NULL],
exit.time = dt[J("B"), list(time)]$time)
Hope this helps.