How to check if pairs remain the same between years? - r

Question

I have a data frame with pairs of individual birds (male and female) that were observed in several years. I am trying to figure out whether these pairs have changed from one year to the next so that I can do some further analyses.
My data is structured like this:
dat <- tibble(year = rep(1:3, each = 3),
Male = c("A1", "B1", "C1",
"A1", "B1", "C1",
"A1", "B1", "C2"),
Female = c("X1", "Y1", "Z1",
"X1", "Y2", "Z2",
"X1", "Y2", "Z2"))
# A tibble: 9 x 3
year Male Female
<int> <chr> <chr>
1 1 A1 X1
2 1 B1 Y1
3 1 C1 Z1
4 2 A1 X1
5 2 B1 Y2
6 2 C1 Z2
7 3 A1 X1
8 3 B1 Y2
9 3 C2 Z2
And my expected output is something like:
# A tibble: 9 x 5
year Male Female male_state female_state
<int> <chr> <chr> <chr> <chr>
1 1 A1 X1 new new
2 1 B1 Y1 new new
3 1 C1 Z1 new new
4 2 A1 X1 reunited reunited
5 2 B1 Y2 divorced new
6 2 C1 Z2 divorced new
7 3 A1 X1 reunited reunited
8 3 B1 Y2 reunited reunited
9 3 C2 Z2 new divorced
I cannot figure out how to check whether a value from a different column is the same in the year before (e.g. if the male ID is the same for a certain female in year 2 or 3 as in the year prior). Any ideas?

Answer 1

This (probably overcomplicated) pipe produces the following output.
dat <- tibble(year = rep(1:3, each = 3),
Male = c("A1", "B1", "C1",
"A1", "B1", "C1",
"A1", "B1", "C2"),
Female = c("X1", "Y1", "Z1",
"X1", "Y2", "Z2",
"X1", "Y2", "Z2"))
dat %>%
mutate(pair=paste0(Male,Female)) %>%
arrange(pair,year) %>%
mutate(check = if_else((pair==lag(pair)) & (year>lag(year)), 'old couple', 'new couple')) %>%
mutate(check = if_else(is.na(check), 'new couple', check)) %>%
mutate(divorced = if_else((Male == lag(Male)) & (Female != lag(Female)), 'divorce', '')) %>%
mutate(divorced = if_else(is.na(divorced), '', divorced))
OUTPUT:
# A tibble: 9 × 6
year Male Female pair check divorced
<int> <chr> <chr> <chr> <chr> <chr>
1 1 A1 X1 A1X1 new couple ""
2 2 A1 X1 A1X1 old couple ""
3 3 A1 X1 A1X1 old couple ""
4 1 B1 Y1 B1Y1 new couple ""
5 2 B1 Y2 B1Y2 new couple "divorce"
6 3 B1 Y2 B1Y2 old couple ""
7 1 C1 Z1 C1Z1 new couple ""
8 2 C1 Z2 C1Z2 new couple "divorce"
9 3 C2 Z2 C2Z2 new couple ""

Answer 2

Try this:
library(tidyverse)
dat <- tibble(
year = rep(1:3, each = 3),
Male = c(
"A1", "B1", "C1",
"A1", "B1", "C1",
"A1", "B1", "C2"
),
Female = c(
"X1", "Y1", "Z1",
"X1", "Y2", "Z2",
"X1", "Y2", "Z2"
)
)
dat |>
mutate(pairing = str_c(Male, "|", Female)) |>
add_count(pairing) |>
group_by(pairing) |>
mutate(male_state = if_else(pairing == lag(pairing), "reunited", NA_character_),
female_state = if_else(pairing == lag(pairing), "reunited", NA_character_)) |>
group_by(Male) |>
mutate(
male_state = if_else(row_number() == 1, "new", male_state),
male_state = if_else(is.na(male_state), "divorced", male_state)
) |>
group_by(Female) |>
mutate(
female_state = if_else(row_number() == 1, "new", female_state),
female_state = if_else(is.na(female_state), "divorced", female_state)
) |>
arrange(year, Male)
#> # A tibble: 9 × 7
#> # Groups: Female [5]
#> year Male Female pairing n male_state female_state
#> <int> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 1 A1 X1 A1|X1 3 new new
#> 2 1 B1 Y1 B1|Y1 1 new new
#> 3 1 C1 Z1 C1|Z1 1 new new
#> 4 2 A1 X1 A1|X1 3 reunited reunited
#> 5 2 B1 Y2 B1|Y2 2 divorced new
#> 6 2 C1 Z2 C1|Z2 1 divorced new
#> 7 3 A1 X1 A1|X1 3 reunited reunited
#> 8 3 B1 Y2 B1|Y2 2 reunited reunited
#> 9 3 C2 Z2 C2|Z2 1 new divorced
Created on 2022-05-03 by the reprex package (v2.0.1)