Unexpected behaviour of function table with "NaN" values

Question

Recently, I've faced a behaviour in table function that was not what I was expected:

For example, let take the following vector:

ex_vec <- c("Non", "Non", "Nan", "Oui", "NaN", NA)

If I check for NA values in my vector, "NaN" is not considered one (as expected):

is.na(ex_vec)
# [1] FALSE FALSE FALSE FALSE FALSE  TRUE

But if I tried to get the different values frequencies:

table(ex_vec)
#ex_vec
#Nan Non Oui 
#  1   2   1

"NaN" does not appear in the table.

However, if I "ask" table to show the NA values, I get this:

table(ex_vec, useNA="ifany")
#ex_vec
# Nan  NaN  Non  Oui <NA> 
#   1    1    2    1    1

So, the character strings "NaN" is treated as a NA value inside table call, while being treated in the ouput as a not NA value.

I know (it would be better and) I could solve my problem by converting my vector to a factor but nonetheless, I'd really like to know what's going on here. Does anyone have an idea?

Answer 1

When factor matches up levels for a vector it converts its exclude list to the same type as the input vector:

exclude <- as.vector(exclude, typeof(x))

so if your exclude list has NaN and your vector is character, this happens:

as.vector(exclude, typeof(letters))
[1] NA    "NaN"

Oh dear. Now the real "NaN" strings will be excluded.

To fix, use exclude=NA in table (and factor if you are making factors that might hit this).

I do love this in the docs for factor:

 There are some anomalies associated with factors that have ‘NA’ as
 a level.  It is suggested to use them sparingly, e.g., only for
 tabulation purposes.

Reassuring...

Answer 2

First idea coming to my mind was to have a look at table definition which start by:

> table
function (..., exclude = if (useNA == "no") c(NA, NaN), useNA = c("no", 
    "ifany", "always"), dnn = list.names(...), deparse.level = 1) 
{

Sounds logical, by default table exclude NA and NaN.

Digging within table code we see that if xis not a factor it coerce it to a factor (nothing new here, it's said in the doc).

    else {
        a <- factor(a, exclude = exclude)

I didn't find anything else which could have impacted the input to coerce "NaN" into NA values.

So looking into factor to get the why we find the root cause:

> factor
function (x = character(), levels, labels = levels, exclude = NA, 
    ordered = is.ordered(x), nmax = NA) 
{
 [...] # Snipped for brievety
    exclude <- as.vector(exclude, typeof(x))
    x <- as.character(x)
    levels <- levels[is.na(match(levels, exclude))] # defined in the snipped part above, is the sorted unique values of input vector, coerced to char.
    f <- match(x, levels)
 [...]
    f
}

Here we got it, the exclude parameter, even being NA values is coerced into a character vector.

So what happens is:

> ex_vec <- c("Non", "Non", "Nan", "Oui", "NaN", NA)
> excludes<-c(NA,NaN)
> as.vector(excludes,"character")
[1] NA    "NaN"
> match(ex_vec,as.vector(excludes,"character"))
[1] NA NA NA NA  2  1

We do match character "NaN" as the exclude vector as been coerced to character before comparison.