In R, can I make the table() function return the number of NA values in a named element?

Question

I am using R to summarize a large amount of data for a report. I want to be able to use lapply() to generate a list of tables from the table() function, from which I can extract my desired statistics. There are a lot of these, so I've written a function to do it. My issue is that I am having difficulty returning the number of missing (NA) values even though I have that in each table, because I can't figure out how to tell R that I want the element from table() that holds the number of NA values. As far as I can tell, R is "naming" that element NA...and I can't call that.

I'm trying to avoid writing some complex statement where I say something like which(is.na(names(element[1]))) | names(element[1])=="var_I_want" because I feel like that's just really wordy. I was hoping there was some way to either tell R to label the NA variable in each table with a character name, or to tell it to pick the one labeled NA, but I haven't had much luck yet.

Minimal example:

example <- data.frame(ID=c(10,20,30,40,50),
                      V1=c("A","B","A",NA,"C"),
                      V2=c("Dog","Cat",NA,"Cat","Bunny"),
                      V3=c("Yes","No","No","Yes","No"),
                      V4=c("No",NA,"No","No","Yes"),
                      V5=c("No","Yes","Yes",NA,"No"))

varlist <- c("V1","V2","V3","V4","V5")

list_o_tables <- lapply(X=example[varlist],FUN=table,useNA="always")

list(V1=list_o_tables[["V1"]]["A"],
     V2=list_o_tables[["V2"]]["Cat"],
     V3=list_o_tables[["V3"]]["Yes"],
     V4=list_o_tables[["V4"]]["Yes"],
     V5=list_o_tables[["V5"]]["Yes"])

What I get:

$V1
A 
2 

$V2
Cat 
  2 

$V3
Yes 
  2 

$V4
Yes 
  1 

$V5
Yes 
  2

What I'd like:

$V1
A     <NA>
2       1

$V2
Cat   <NA>
  2     1

$V3
Yes   <NA> 
  2     0

$V4
Yes   <NA> 
  1     1

$V5
Yes   <NA> 
  2     1

Answer 1

This is ugly (IMHO) but it works:

my_table <- function(x){
    setNames(table(x,useNA = "always"),c(sort(unique(x[!is.na(x)])),'NA'))
}

So you'd lapply this instead, and then you'd have access to the NA column.

Looking more closely, this is rooted in the behavior of factor:

levels(factor(c(1,NA,2),exclude = NULL))
[1] "1" "2" NA 

My recollection is that the distinction between a factor level of NA versus "NA" has been at the very least a source of confusion in R in the past. I feel like I've seen some debates about the merits of this on r-devel, but I can't recall for sure at the moment.

So the issue is, if you have a factor with NA values, what do you call the levels? Technically, this is correct, one of the levels is "missing" not literally "NA". It would be nice (IMHO) if table didn't adhere to this quite so strictly, though.