Cartesian Rolling Join using Data.table - r

Question

I have two tables:
dat: contains the data
dates: contains the table of dates
library(data.table)
dates = structure(list(date = structure(c(17562, 17590, 17621, 17651,
17682, 17712, 17743, 17774, 17804, 17835, 17865, 17896), class = "Date")),
row.names = c(NA, -12L), class = "data.frame")
dat = structure(list(date = structure(c(17546, 17743, 17778, 17901,
17536, 17806, 17901, 17981, 17532, 17722, 17969, 18234), class = "Date"),
country = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L), .Label = c("AAA", "BBB", "CCC"), class = "factor"),
state = structure(c(1L, 1L, 2L, 3L, 4L, 1L, 2L, 5L, 6L, 1L,
2L, 2L), .Label = c("S1", "S2", "S3", "S4", "S5", "S6"), class = "factor"),
item = structure(c(1L, 2L, 4L, 6L, 3L, 5L, 3L, 2L, 2L, 4L,
5L, 7L), .Label = c("M1", "M2", "M3", "M4", "M5", "M6", "M7"
), class = "factor"), value = c(67L, 10L, 50L, 52L, 93L,
50L, 62L, 46L, 6L, 30L, 30L, 14L)), row.names = c(NA, -12L
), class = "data.frame")
dates = data.table(dates)
dat = data.table(dat)
setkey(dates, date)
setkey(dat, date)
The result I'm after is below. I.e doing a rolling join with each individual row of dat and then combining the result.
rbind(
dat[1,][dates, roll = 90],
dat[2,][dates, roll = 90],
dat[3,][dates, roll = 90],
...
dat[12,][dates, roll = 90]
)
My actual dataset is much larger so it's no practical to list every row of dat. Is there a short hand way of doing the same thing without a loop?

Answer 1

If I understand your intent correctly, you want to rollover the records for 90 days.
I used a cross join and then used the rollover criteria to subset
Your original tables:
library(data.table)
dates = structure(list(date = structure(c(17562, 17590, 17621, 17651,
17682, 17712, 17743, 17774, 17804, 17835, 17865, 17896), class = "Date")),
row.names = c(NA, -12L), class = "data.frame")
dat = structure(list(date = structure(c(17546, 17743, 17778, 17901,
17536, 17806, 17901, 17981, 17532, 17722, 17969, 18234), class = "Date"),
country = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L), .Label = c("AAA", "BBB", "CCC"), class = "factor"),
state = structure(c(1L, 1L, 2L, 3L, 4L, 1L, 2L, 5L, 6L, 1L,
2L, 2L), .Label = c("S1", "S2", "S3", "S4", "S5", "S6"), class = "factor"),
item = structure(c(1L, 2L, 4L, 6L, 3L, 5L, 3L, 2L, 2L, 4L,
5L, 7L), .Label = c("M1", "M2", "M3", "M4", "M5", "M6", "M7"
), class = "factor"), value = c(67L, 10L, 50L, 52L, 93L,
50L, 62L, 46L, 6L, 30L, 30L, 14L)), row.names = c(NA, -12L
), class = "data.frame")
dates = data.table(dates)
dat = data.table(dat)
Note, I haven't setkey.
I am using a cross join function from the reference: How to do cross join in R?
CJ.table.1 <- function(X,Y)
setkey(X[,c(k=1,.SD)],k)[Y[,c(k=1,.SD)],allow.cartesian=TRUE][,k:=NULL]
Then I cross join, subset for the roll join, rename columns and sort
dsn1<-CJ.table.1(dat,dates)[i.date-date<=90 & i.date-date>=0][,.(date=i.date,country, state, item, value)][order(country, state, item, value,date),]

Answer 2

This is not necessarily the best way to do it, but you could simply write a loop here to iterate through your data:
df <- data.frame()
for (i in 1:nrow(dat)){
df <- rbind(df, dat[i,][dates, roll = 90])
}
head(df)
date country state item value
1: 2018-01-31 CCC S6 M2 6
2: 2018-02-28 CCC S6 M2 6
3: 2018-03-31 CCC S6 M2 6
4: 2018-04-30 <NA> <NA> <NA> NA
5: 2018-05-31 <NA> <NA> <NA> NA
Edit: just saw you said "without a loop", it's been a long day. This is one way to solve the problem though.