Numeric vs Factors & IF Statements - r

Question

I am trying to create a function for gender distribution. Is there a way to define a letter as something other than as.factor? I would like to operate func(F) instead of func("F"). Or should I go numeric: func(0), func(1), func(2)?
I also finished off the statement with an else that is designed to operate when left blank, but does not. If I whittle down the function to not include an IF statement a blank variable works fine:
genderDist <- function(){
cat("Female:", sum(voterData$GENDER == "F"))
}
Thanks in advance! Cheers!
Full Statement:
genderDist <- function(x){
if (x == "F"){
cat("Female:", sum(voterData$GENDER == "F"))
}
else if (x == "M"){
cat("Male:", sum(voterData$GENDER == "M"))
}
else if(x == "U"){
cat("Unknown:", sum(voterData$GENDER == ""))
}
else{
cat("Female:", sum(voterData$GENDER == "F"))
cat("Male:", sum(voterData$GENDER == "M"))
cat("Unknown:", sum(voterData$GENDER == ""))
}
Desired results:
genderDist(F) gives count of Females
genderDist(M) gives count of Males
genderDist(U) gives count of Unknown
genderDist() gives count for all the above

Answer 1

There are several possibilities for coding gender, besides factor:
1. as character, not as factor. You will still have to call your function like func("F").
2. You already thought of using numeric yourself. Disadvantage is that it may be unclear if 1 is male or female.
3. The best option IMHO would be to go binary. Name your column "male" and use TRUE, FALSE and NA for unknown. The binary also works great in your if statement. Start with if(is.na(male)) ... ; else if(male).
EDIT
But to achieve your desired outcome, the coding of gender is not the issue, I would take this approach:
#First, define variables Fe, Ma and Un
#WARNING: Do NOT USE 'F', as 'F' is an abbr. for 'FALSE'!!
Fe <- "F"
Ma <- "M"
Un <- "U"
#now define a lookup dataframe for convienience
LT <- data.frame(code = c(Fe,Ma,Un), name = c("Female","Male","Unknown"), stringsAsFactors = FALSE)
# then define your function without an ifelse needed
genderDist <- function(x){
cat(LT[LT$code == x,"name"], sum(voterData$GENDER == x))
}
Introduce some fake data:
voterData <- data.frame(GENDER= c("F","F","F","M","M","U"))
Then run function:
> genderDist(Fe)
Female 3
> genderDist(Ma)
Male 2
> genderDist(Un)
Unknown 1