Assigning categorical values to NAs randomly or proportionally

We can use ifelse and is.na to determine if na exist, and then use sample to randomly select female and male.

df$gender <- ifelse(is.na(df$gender), sample(c("female", "male"), 1), df$gender)

How about this:

> df <- structure(list(gender = c("female", "male", NA, NA, "male", "male", 
+                                 "male"),
+                      Division = c("South Atlantic", "East North Central", 
+                                   "Pacific", "East North Central", "South Atlantic", "South Atlantic", 
+                                   "Pacific"),
+                      Median = c(57036.6262, 39917, 94060.208, 89822.1538,
+                                 107683.9118, 56149.3217, 46237.265),
+                      first_name = c("Marilyn", "Jeffery", "Yashvir", "Deyou", "John", "Jose", "Daniel")),
+                 row.names = c(NA, -7L), class = c("tbl_df", "tbl", "data.frame"))
> 
> Gender <- rbinom(length(df$gender), 1, 0.52)
> Gender <- factor(Gender, labels = c("female", "male"))
> 
> df$gender[is.na(df$gender)] <- as.character(Gender[is.na(df$gender)])
> 
> df$gender
[1] "female" "male"   "female" "female" "male"   "male"   "male"  
> 

Thats is random with a given probability. You could also consider imputing values using nearest neighbors, hot desk, or similar.

Hope it helps.

Just assign

df$gender[is.na(df$gender)]=sample(c("female", "male"), dim(df)[1], replace = TRUE)[is.na(df$gender)]