Get the rowwise minimum of certain columns excluding 0 and NA - r

Question

I have made a very complex solution to something I feel should have a much simpler solution.
In short what I want:
I want to compute a new column containing the minimum value across 3 columns
I want to ignore zeros and NAs
If I only have zeros and NAs I want a zero
If I have only NAs I want a NA
Here is my solution, it works, but it is very complex and produces a warning.
> library(dplyr)
> df <- data.frame(
+ id = c(1, 2, 3, 4),
+ test1 = c( NA, NA, 2 , 3),
+ test2 = c( NA, 0, 1 , 1),
+ test3 = c(NA, NA, 0 , 2)
+ )
> df2 <- df %>%
+ mutate(nieuw = apply(across(test1:test3), 1, function(x) min(x[x>0]))) %>%
+ rowwise() %>%
+ mutate(nieuw = if_else(is.na(nieuw), max(across(test1:test3), na.rm = TRUE), nieuw)) %>%
+ mutate(nieuw = ifelse(is.infinite(nieuw), NA, nieuw))
> df
id test1 test2 test3
1 1 NA NA NA
2 2 NA 0 NA
3 3 2 1 0
4 4 3 1 2
> df2
# A tibble: 4 x 5
# Rowwise:
id test1 test2 test3 nieuw
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 NA NA NA NA
2 2 NA 0 NA 0
3 3 2 1 0 1
4 4 3 1 2 1
Warning message:
Problem while computing `nieuw = if_else(...)`.
i no non-missing arguments to max; returning -Inf
i The warning occurred in row 1.

Answer 1

You can create a helper function and then apply it rowwise:
library(dplyr)
safe <- function(x, f, ...) ifelse(all(is.na(x)), NA,
ifelse(all(is.na(x) | x == 0),
0, f(x[x > 0], na.rm = TRUE, ...)))
df %>%
rowwise() %>%
mutate(a = safe(c_across(test1:test3), min))
# A tibble: 4 × 5
# Rowwise:
id test1 test2 test3 a
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 NA NA NA NA
2 2 NA 0 NA 0
3 3 2 1 0 1
4 4 3 1 2 1

Answer 2

Here is another option. It leverages making zeros and NA's very large and then recodes them at the end:
library(tidyverse)
get_min <- function(data, cols){
data[is.na(data)] <- 1e6
data[data == 0] <- 1e5
nums <- do.call(pmin, select(data, all_of(cols)))
recode(nums, `1e+06` = NA_real_, `1e+05` = 0.)
}
df %>%
mutate(nieuw = get_min(., c("test1", "test2", "test3")))
#> id test1 test2 test3 nieuw
#> 1 1 NA NA NA NA
#> 2 2 NA 0 NA 0
#> 3 3 2 1 0 1
#> 4 4 3 1 2 1