Changing the Names in a Column to the most frequent Names per Group

Question

I'm looking for a way to to calculate the most frequent name per group, and then change all names in this group to the most frequent name, or create a new column with the most frequent name. In the dataset, there may be ties in the frequent names, so in these cases, I would like it to pick either one of the tied names. For example:

The sample data looks like this:

first_name = c("John", "John", "John Smith", "Linda Dawn", "Linda Dawn", "Linda", "Linda", "Linda Dawn", "Jack", "Jack", "Jack B", "Jack B")
id = c(1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3)
dt = data.table(cbind(first_name, id))

    first_name id
 1:       John  1
 2:       John  1
 3: John Smith  1
 4: Linda Dawn  2
 5: Linda Dawn  2
 6:      Linda  2
 7:      Linda  2
 8: Linda Dawn  2
 9:       Jack  3
10:       Jack  3
11:     Jack B  3
12:     Jack B  3

The names in my dataset may also contain middle names.

I've tried using the DescTools::Mode() function, which works well if there are no ties in most frequent names. Using this method, I get the following output:

dt[, first_name_new := Mode(first_name), by = id]

    first_name id first_name_new
 1:       John  1           John
 2:       John  1           John
 3: John Smith  1           John
 4: Linda Dawn  2     Linda Dawn
 5: Linda Dawn  2     Linda Dawn
 6:      Linda  2     Linda Dawn
 7:      Linda  2     Linda Dawn
 8: Linda Dawn  2     Linda Dawn
 9:       Jack  3           <NA>
10:       Jack  3           <NA>
11:     Jack B  3           <NA>
12:     Jack B  3           <NA>

Does anyone know how to get the <NA> to state Jack or Jack B, or another technique to perform this task?

Thanks in advance!

Answer 1

Mode() returns 2 values when there is a tie. Simply take the first one from the Mode():

dt[, first_name_new := Mode(first_name)[1], by = id]

> dt
    first_name id first_name_new
 1:       John  1           John
 2:       John  1           John
 3: John Smith  1           John
 4: Linda Dawn  2     Linda Dawn
 5: Linda Dawn  2     Linda Dawn
 6:      Linda  2     Linda Dawn
 7:      Linda  2     Linda Dawn
 8: Linda Dawn  2     Linda Dawn
 9:       Jack  3           Jack
10:       Jack  3           Jack
11:     Jack B  3           Jack
12:     Jack B  3           Jack