6.3 Dealing with NA

Re-write our function to work with missing values.

Note you need to make the NA check first.

k_c <- function(temp_k) {
    if (is.na(temp_k)) {
        return(NA)
    } else if (temp_k < 0) {
        warning('you passed in a negative Kelvin number')
        # stop()
        return(NA)
    } else {
        temp_c <- temp_k - 273.15
        return(temp_c)
    }
}

k_c(-9)
## Warning in k_c(-9): you passed in a negative Kelvin number
## [1] NA

k_c(NA)
## [1] NA

k_c(0)
## [1] -273.15

if (c(TRUE, FALSE)) {}
## Warning in if (c(TRUE, FALSE)) {: the condition has length > 1 and only the
## first element will be used
## NULL

if (NA) {}
## Error in if (NA) {: missing value where TRUE/FALSE needed

use && and || to short-circuit the boolean comparisons. This will also guarantee a value of length 1L. == is also vectorized, should use identical() or all.equal().

identical is very strict. Doesn’t corece types.

identical(0L, 0)
## [1] FALSE

all.equal has ability to set tolerances.

all.equal: compare R objects x and y testing ‘near equality’. If they are different, comparison is still made to some extent, and a report of the differences is returned. Do not use all.equal directly in if expressions—either use isTRUE(all.equal(….)) or identical if appropriate.

all.equal(0L, 0)
## [1] TRUE

if (isTRUE(all.equal(0L, 0))) {print("Hello")}
## [1] "Hello"