I have a data.table with columns p1
, p2
, ... which contains percentages. I want to compute the quantiles for each columns given a reference variable val
. Conceptually, this is like:
quantile(val, p1, type = 4, na.rm = T)
quantile(val, p2, type = 4, na.rm = T)
...
My attempt at using data.table is as follows:
fun <- function(x, y) quantile(y, x, type = 4, na.rm = T)
dt[, c('q1', 'q2') := lapply(.SD, fun), .SDcols = c('p1', 'p2'), by = grp]
where grp is some grouping variable
However, I am having trouble specifying the y
variable in a way that keeps it fixed.
I tried the following:
fun <- function(x, y, dt) quantile(dt[, y], x, type = 4, na.rm = T)
dt[, c('q1', 'q2') := lapply(.SD, fun, y, dt), .SDcols = c('p1', 'p2'), by = grp]
But doing it this fashion does not enforce the grouping when the quantiles are computed. It will compute the quantile based on the whole range of the y
variable instead of the y
within groups. What is the correct way to do this?
EDIT:
Here is a trivial example of just one variable:
> dt <- data.table(y = 1:10, p1 = rep(seq(0.2, 1, 0.2), 2), g = c(rep('a', 5), rep('b', 5)))
> dt
y p1 g
1: 1 0.2 a
2: 2 0.4 a
3: 3 0.6 a
4: 4 0.8 a
5: 5 1.0 a
6: 6 0.2 b
7: 7 0.4 b
8: 8 0.6 b
9: 9 0.8 b
10: 10 1.0 b
> fun <- function(x, dt, y) quantile(dt[, y], x, type = 4, na.rm = T)
> dt[, c('q1') := lapply(.SD, fun, dt, y), .SDcols = c('p1'), by = c('g')]
> dt
y p1 g q1
1: 1 0.2 a 2
2: 2 0.4 a 4
3: 3 0.6 a 6
4: 4 0.8 a 8
5: 5 1.0 a 10
6: 6 0.2 b 2
7: 7 0.4 b 4
8: 8 0.6 b 6
9: 9 0.8 b 8
10: 10 1.0 b 10
You can see q1 is computed using the entire range of y
.