The proper way to vectorise an if-else tower in R

Question

I came across the following post: Vectorized IF statement in R?, which deals with a vecotrisation of one if-else construct in R. However, I do not want to build nested $ifelse$ functions in R, is there another way to do this?

As an example I have this:

library(data.table)
precalc_nrcolumns <- function(TERM_DATE, CHANGE_DATE){
  if(CHANGE_DATE < TERM_DATE){
    if(CHANGE_DATE == 0) {
      if(TERM_DATE > 100) {
        nrcolumns <- 100
      }else{
        nrcolumns <-  TERM_DATE
      }
    }else{
      nrcolumns <- CHANGE_DATE
    }
  }else{
    nrcolumns <- 100
  }
  return(nrcolumns)
}


test.data <- data.table(TERM_DATE = sample(1:500, 100, replace=TRUE),
           CHANGE_DATE = sample(1:500, 100, replace=TRUE))

test.data[, value := mapply(precalc_nrcolumns, TERM_DATE, CHANGE_DATE)]

I am fully aware of using mapply, which actually works, but I was wondering what other ways there are to deal with this.

Could you add some data and what would happen to that data with your nested if-else? — NelsonGon, Jun 19 '19 at 14:18
Of course, but I think I should add a smaller example, since this example is not the minimum viable example. — Snowflake, Jun 19 '19 at 14:21
I agree the `case_when()` is much easier to read. https://dplyr.tidyverse.org/reference/case_when.html — Bryan Adams, Jun 19 '19 at 14:49
Something like `pmax(pmin(100, TERM_DATE), CHANGE_DATE)`, I guess — Frank, Jun 19 '19 at 14:51

score 2 · Answer 1 · answered Jun 20 '19 at 03:56

A possible approach:

test.data[, val := pmin(CHANGE_DATE, TERM_DATE)][
    CHANGE_DATE==0L, val := pmin(100L, TERM_DATE)][
        CHANGE_DATE >= TERM_DATE, val := 100L]

data:

set.seed(0L)
library(data.table)
nr <- 300
test.data <- data.table(TERM_DATE = sample(0:150, nr, replace=TRUE),
    CHANGE_DATE = sample(0:150, nr, replace=TRUE))

The proper way to vectorise an if-else tower in R

1 Answers1