How do I count how many days it took to surpass a value in R?-CodePudding

I have a data df2

      date    X Days
2020-01-06  525   NA
2020-01-07  799   NA
2020-01-08  782   NA
2020-01-09  542   NA
2020-01-10  638    5
2020-01-11 1000    5
2020-01-12 1400    3
2020-01-13 3500    1

I want to count how many days it will take for the sum of X to surpass a value. In this case, the value is 3000. For example on 1/13, it took 1 day because X is 3500, so it already surpassed 3000. On 1/12 it took 3 days (1400 1000 638)=3038.

I wish to get the column Days.

dput(df2)
structure(list(date = structure(c(1578268800, 1578355200, 1578441600, 
1578528000, 1578614400, 1578700800, 1578787200, 1578873600), class = c("POSIXct", 
"POSIXt"), tzone = "UTC"), X = c(525, 799, 782, 542, 638, 1000, 
1400, 3500), Days = c(NA, NA, NA, NA, 5, 5, 3, 1)), class = "data.frame", row.names = c(NA, 
-8L))

CodePudding user response：

I think a rolling-function works well. Unlike most rolling functions which have a fixed window that is smaller than the length of data, we will intentionally make this full-width.

zoo::rollapplyr(
  df2$X, nrow(df2),
  FUN = function(z) which(cumsum(rev(z)) > 3000)[1],
  partial = TRUE)
# [1] NA NA NA NA  5  5  3  1

(I'm ignoring date, assuming that the rows are consecutive-days.)

CodePudding user response：

cs <- c(0, cumsum(rev(df$X)))
out <- sapply(cs, function(x) which(cs - x > 3e3)[1])
rev(out - seq_along(cs))
#> [1] NA NA NA NA NA  5  5  3  1

^{Created on 2022-01-06 by the reprex package (v2.0.1)}