I have a question about a vectorized operation with logical vectors. In my problem, there are two vectors: main and secondary. They're both of the same length. I want to replace some elements in the main vector to NA, based on insights I gather from the secondary vector.

• The main vector is comprised of TRUE and FALSE that can appear in any random order.
• The secondary vector is either:
  • a sequence of TRUE then a sequence of FALSE with/without NA as the last element; or
  • all TRUE; or
  • all FALSE; or
  • all FALSE with last element as NA; or
  • all TRUE with last element as NA

I'll provide several examples below and explain the desired algorithm.

A - The most common case
replace x values with NA for positions that are FALSE in y

# here, `x` is the main vector
x <- c(FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, NA)

# `y` is the secondary vector
y <- c(TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)

# `z` is the desired output
z <- c(FALSE, FALSE, TRUE, FALSE, FALSE, NA, NA) # in this case only index 7 in `x` actually changed

B - The secondary vector is all FALSE

x2 <- c(FALSE, NA)
y2 <- c(FALSE, FALSE)

# desired output
z2 <- c(NA, NA)

C - The secondary vector is all TRUE

x3 <- rep(FALSE, 4)
y3 <- rep(TRUE, 4)

# desired output
z3 <- rep(FALSE, 4)

My attempt
I've almost figured this out, but unfortunately it fails with scenario B.

my_func <- function(main, secondary) {
  
  idx_last_true_in_secondary <- max(which(secondary))
  
  if(idx_last_true_in_secondary == length(secondary)) {
    return(main)
  }
  
  main[(idx_last_true_in_secondary   1): length(main)] <- NA
  main
}

# case A
my_func(x, y)
#> [1] FALSE FALSE  TRUE FALSE FALSE    NA    NA

# case B
my_func(x2, y2)
#> Warning in max(which(secondary)): no non-missing arguments to max; returning
#> -Inf
#> Error in (idx_last_true_in_secondary   1):length(main): result would be too long a vector

# case C
my_func(x3, y3)
#> [1] FALSE FALSE FALSE FALSE

My question is whether anyone sees a better way to approach the problem?

CodePudding user response：

We could do it this way:

my_func <- function(x, y) {
  replace(x, !y, NA)
}

How it works:

# A
replace(x, !y, NA)
[1] FALSE FALSE  TRUE FALSE FALSE    NA    NA

# B
replace(x2, !y2, NA)
[1] NA NA

# C
replace(x3, !y3, NA)
[1] FALSE FALSE FALSE FALSE

my_func(x,y)
my_func(x2,y2)
my_func(x3,y3)

output:

> my_func(x,y)
[1] FALSE FALSE  TRUE FALSE FALSE    NA    NA
> my_func(x2,y2)
[1] NA NA
> my_func(x3,y3)
[1] FALSE FALSE FALSE FALSE

CodePudding user response：

This seems to work as expected:

my_func <- function(main,secondary ) {
  main[!secondary] <- NA
  return(main)
}

my_func(x,y)
[1] FALSE FALSE  TRUE FALSE FALSE    NA    NA

my_func(x2,y2)
[1] NA NA

my_func(x3,y3)
[1] FALSE FALSE FALSE FALSE

CodePudding user response：

We can try ifelse like below

> ifelse(y,x,NA) 
[1] FALSE FALSE  TRUE FALSE FALSE    NA    NA