我无法顺序计算变量的值。
我的数据看起来像这样
IDPERS value year divorce
1 371102 married 1 0
2 371102 divorced 2 1
3 371102 divorced 3 1
4 371102 NA 4 0
5 371102 divorced 5 1
6 371102 divorced 6 1
7 371102 NA 7 0
8 371102 NA 8 0
9 371102 divorced 9 1
10 371102 divorced 10 1
11 371102 divorced 11 1
我想要的是计算离婚，例如
IDPERS value year divorce divorceEp
1 371102 married 1 0 0
2 371102 divorced 2 1 1
3 371102 divorced 3 1 2
4 371102 NA 4 0 2
5 371102 divorced 5 1 3
6 371102 divorced 6 1 4
7 371102 NA 7 0 4
8 371102 NA 8 0 4
9 371102 divorced 9 1 5
10 371102 divorced 10 1 6
11 371102 divorced 11 1 7
任何线索如何才能正确计算这一系列剧集？
library(dplyr)
dta %>% mutate(1:sum(divorce))
谢谢
dta = structure(list(IDPERS = c(371102, 371102, 371102, 371102, 371102,
371102, 371102, 371102, 371102, 371102, 371102), value = c("married",
"divorced", "divorced", "NA", "divorced", "divorced", "NA", "NA",
"divorced", "divorced", "divorced"), year = 1:11, divorce = c(0,
1, 1, 0, 1, 1, 0, 0, 1, 1, 1), divorceEp = c(0, 1, 2, 2, 3, 4,
4, 4, 5, 6, 7)), class = "data.frame", .Names = c("IDPERS", "value",
"year", "divorce", "divorceEp"), row.names = c(NA, -11L))
答案 0 :(得分：2)
您可以使用cumsum()：
mutate(dta,divorceEp=cumsum(divorce))
IDPERS value year divorce divorceEp
1 371102 married 1 0 0
2 371102 divorced 2 1 1
3 371102 divorced 3 1 2
4 371102 NA 4 0 2
5 371102 divorced 5 1 3
6 371102 divorced 6 1 4
7 371102 NA 7 0 4
8 371102 NA 8 0 4
9 371102 divorced 9 1 5
10 371102 divorced 10 1 6
11 371102 divorced 11 1 7