Edit string value based on value in another column using r - r

Question

I have data on women who married and sometimes changed surnames over the period 1990-1999. However, I do not always know the exact year the name change took place, only that the surname changed sometime between year x and year y. In the original data, the old surname has only been crossed over and the new surname has been written next to it, which is indicated in the column "crossed_over". For example, Sarah Smith changed her name to Sarah Draper sometime in the period 1994-1999.
What I would like is that each woman have a unique surname for each year, like Liza Moore who changed her name to Liza Neville, preferably taking an average value when assigning a surname, using the column "crossed_over". For example, Sarah Smith would become Sarah Draper in 1997 and Mary King would become Mary Fisher in 1997 or 1998.
Does anyone have a suggestion to how I can achieve this using the example below?
library(tidyverse)
id <- rep(1:4, each = 10)
year <- rep(1990:1999, 4)
first_name <- c(rep("molly", 10), rep("sarah", 10), rep("mary", 10), rep("liza", 10))
last_name <- c(rep("johnson", 10), rep("smith", 4), rep("smith draper", 6), rep("king", 5), rep("king fisher", 5),
rep("moore", 7), rep("neville", 3))
crossed_over <- c(rep(NA, 10), rep(NA, 4), rep("smith", 6), rep(NA, 5), rep("king", 5), rep(NA, 10))
df <- tibble(id, year, first_name, last_name, crossed_over)

Answer 1

Here is one approach. For those rows with crossed_over names, set the new_last_name to the crossed_over name for the first half of rows, and to the difference between crossed_over and last_name for the second half of rows.
library(tidyverse)
library(stringr)
df %>%
filter(!is.na(crossed_over)) %>%
group_by(across(c(-year))) %>%
mutate(new_last_name = ifelse(row_number() <= n()/2,
crossed_over,
str_trim(str_remove(last_name, crossed_over)))) %>%
ungroup() %>%
right_join(df) %>%
mutate(new_last_name = coalesce(new_last_name, last_name)) %>%
arrange(id, year)
Output
id year first_name last_name crossed_over new_last_name
<int> <int> <chr> <chr> <chr> <chr>
1 1 1990 molly johnson NA johnson
2 1 1991 molly johnson NA johnson
3 1 1992 molly johnson NA johnson
4 1 1993 molly johnson NA johnson
5 1 1994 molly johnson NA johnson
6 1 1995 molly johnson NA johnson
7 1 1996 molly johnson NA johnson
8 1 1997 molly johnson NA johnson
9 1 1998 molly johnson NA johnson
10 1 1999 molly johnson NA johnson
11 2 1990 sarah smith NA smith
12 2 1991 sarah smith NA smith
13 2 1992 sarah smith NA smith
14 2 1993 sarah smith NA smith
15 2 1994 sarah smith draper smith smith
16 2 1995 sarah smith draper smith smith
17 2 1996 sarah smith draper smith smith
18 2 1997 sarah smith draper smith draper
19 2 1998 sarah smith draper smith draper
20 2 1999 sarah smith draper smith draper
21 3 1990 mary king NA king
22 3 1991 mary king NA king
23 3 1992 mary king NA king
24 3 1993 mary king NA king
25 3 1994 mary king NA king
26 3 1995 mary king fisher king king
27 3 1996 mary king fisher king king
28 3 1997 mary king fisher king fisher
29 3 1998 mary king fisher king fisher
30 3 1999 mary king fisher king fisher
31 4 1990 liza moore NA moore
32 4 1991 liza moore NA moore
33 4 1992 liza moore NA moore
34 4 1993 liza moore NA moore
35 4 1994 liza moore NA moore
36 4 1995 liza moore NA moore
37 4 1996 liza moore NA moore
38 4 1997 liza neville NA neville
39 4 1998 liza neville NA neville
40 4 1999 liza neville NA neville