merging two datasets based on common id and date within interval range - r

Question

I have two datasets: DF1 - data frame which lists heads of states (leader_id) of countries (country_code) and an interval of their time in office (office_interval). DF2 - data frame where every observation is an event that has an ID (event_ID) country (country_code) and date it occurred (event_date)
Data:
library(lubridate)
#Leader DF
leader_id <- c("Adam","Bob","Charlie","Derek", "Edgar")
country_code <- c(1,1,2,2,3)
office_interval <- c(interval(ymd("1900-01-01"), ymd("1905-01-01")),
interval(ymd("1910-01-01"), ymd("1915-01-01")),
interval(ymd("1920-01-01"), ymd("1925-01-01")),
interval(ymd("1930-01-01"), ymd("1935-01-01")),
interval(ymd("1940-01-01"), ymd("1945-01-01")))
DF1 <- data.frame(leader_id, country_code, office_interval)
#Event DF
event_id <- c(1,1,2,3,3)
country_code <- c(1,2,2,1,3)
event_date <- c(as.Date("1901-01-02"),
as.Date("1920-01-02"),
as.Date("1921-01-02"),
as.Date("1911-01-02"),
as.Date("1941-02-02"))
DF2 <- data.frame(event_id, country_code, event_date)
I would like to take create a new column in DF2 that takes the leaderid from DF1 based on each row in DF2 that occur within a leaders office_interval in the same country.
DF2 should look like this afterward:
event_id country_code event_date leader_id
1 1 1 1901-01-02 Adam
2 1 2 1920-01-02 Charlie
3 2 2 1921-01-02 Charlie
4 3 1 1911-01-02 Bob
5 3 3 1941-02-02 Edgar
I've tried some solutions from here but I cannot get any of them to work.

Answer 1

Here is a solution maybe can work for your purpose
idx <- sapply(1:nrow(DF2), function(k) which(DF2$event_date[k] %within% DF1$office_interval & DF2$country_code[k]%in% DF1$country_code))
DF2$leader_id <- DF1$leader_id[idx]
such that
> DF2
event_id country_code event_date leader_id
1 1 1 1901-01-02 Adam
2 1 2 1920-01-02 Charlie
3 2 2 1921-01-02 Charlie
4 3 1 1911-01-02 Bob
5 3 3 1941-02-02 Edgar

Answer 2

We can left_join DF2 and DF1 by "country_code" and keep the records which are within the time interval range.
library(dplyr)
library(lubridate)
left_join(DF2, DF1, by = "country_code") %>%
filter(event_date %within% office_interval)
# event_id country_code event_date leader_id office_interval
#1 1 1 1901-01-02 Adam 1900-01-01 UTC--1905-01-01 UTC
#2 1 2 1920-01-02 Charlie 1920-01-01 UTC--1925-01-01 UTC
#3 2 2 1921-01-02 Charlie 1920-01-01 UTC--1925-01-01 UTC
#4 3 1 1911-01-02 Bob 1910-01-01 UTC--1915-01-01 UTC
#5 3 3 1941-02-02 Edgar 1940-01-01 UTC--1945-01-01 UTC

Answer 3

This should also work:
# add start and end date
DF1$start_date <- substr(DF1$office_interval, 1, 10)
DF1$end_date <- substr(DF1$office_interval, 17, 26)
# merge dataframes
DF2 <- merge(x = DF2, y = DF1, by.x = "country_code", by.y = "country_code")
# filter for correct times
DF2 <- DF2[(DF2$event_date >= DF2$start_date & DF2$event_date <= DF2$end_date),]
# select columns
DF2[1:4]