Один способ сделать это, но должен быть лучший хак:
library(dplyr)
df %>%
group_by(ID) %>%
mutate(
DATE = as.Date(DATE),
VAR2 = ifelse(VAR2 == 1 & lag(VAR2) == 1, 0, VAR2),
PRESENT = sapply(DATE,
function(x) any(VAR1[between(DATE, x - 2, x + 2)] == 1)) & VAR2 == 1
) %>%
summarise(PRESENT = +any(PRESENT))
Вывод:
# A tibble: 2 x 2
ID PRESENT
<int> <int>
1 1 0
2 2 1
Используемые данные:
df <- structure(list(ID = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), DATE = structure(1:26, .Label = c("2018-07-27", "2018-07-28",
"2018-07-29", "2018-07-30", "2018-07-31", "2018-08-01", "2018-09-30",
"2018-10-01", "2018-10-02", "2018-10-03", "2018-10-04", "2018-10-05",
"2018-10-06", "2018-10-07", "2018-10-08", "2018-10-10", "2018-10-12",
"2018-10-13", "2018-10-14", "2018-10-15", "2018-10-18", "2018-10-19",
"2018-10-20", "2018-10-26", "2018-10-28", "2018-11-02"), class = "factor"),
VAR1 = c(0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L),
VAR2 = c(0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L)), class = "data.frame", row.names = c(NA,
-26L))