Dear all,
I come with an old question I had, which I have also solved but the last point.
I've a data.table as follows:
dt <- data.table(id = rep(1:2, c(5, 2)),
date = as.Date(rep(c("2005-07-25", "2006-09-17",
"2001-10-19"), c(3, 2, 2))),
sex = as.factor( rep(c(0, 1), c(5, 2))),
hosp = as.factor(c('A11', 'C11', 'R1000',
'W1000', 'C11', 'B22', 'D22')),
check = as.factor(rep(c(T, F, T), c(3, 2, 2))))
By avoiding splitting data table, I would like to do: If check = T, keep all
rows by unique pairs (id, date) excepting those
where hosp %in% c("C11", "D22"). If check = F, keep all rows
but changing "C11" to "A11". So desired otput is:
id date sex hosp
1: 1 2005-07-25 0 A11
2: 1 2005-07-25 0 R1000
3: 1 2006-09-17 0 W1000
4: 1 2006-09-17 0 A11
5: 2 2001-10-19 1 B22
I've tried (without result):
dtnew <- dt[, {
if (check == TRUE) {
idx <- which(!hosp %in% c("C11", "D22"))
res <- list(hosp = hosp[idx], sex = sex[idx], check = check[idx])
} else {
if (levels(hosp) == "C11"){
idx <- which(id)
res <- list(hosp = "A22", sex = sex[idx], check =
check[idx])
} else {
idx <- which(id)
res <- list(hosp = hosp[idx], sex = sex[idx], check = check[idx])
}
}
res
}, by = list(id, date)]
I'm aware it may not be the most efficient way. Thanks for any help!!
Frank S.
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