| Fish_ID | Instance | Success | First_Success |
|---|---|---|---|
| 1 | 0 | 0 | 0 |
| 1 | 1 | 0 | 0 |
| 1 | 2 | 1 | 1 |
| 1 | 3 | 0 | 0 |
| 1 | 4 | 1 | 0 |
| 1 | 5 | 0 | 0 |
我有一个包含列"Fish_ID"、"Instance"和"Success"的数据框，我想创建一个列"First_Success"，仅在第一次成功时包含1，否则 for each FISH_ID组包含0.在实际数据中，我有不止一个Fish_ID. 我想出了这个代码:
mydata %>%
group_by(fish_ID) %>%
mutate(Fist_Success = ifelse(Success == 1, 1, 0))
但在此代码中，它计算等于1的所有Success值，我只想让它只对"First_Success"列的第一次成功计算为1，而在同一FISH_ID组中其余的值为0.如有任何建议，我将不胜感激.非常感谢!
您可以使用cumsum():
library(dplyr)
df %>%
mutate(First_Success = +(Success & cumsum(Success) == 1),
.by = Fish_ID)
# Fish_ID Instance Success First_Success
# 1 1 0 0 0
# 2 1 1 0 0
# 3 1 2 1 1
# 4 1 3 0 0
# 5 1 4 1 0
# 6 1 5 0 0
@朗唐的建议也很棒:
df %>%
mutate(First_Success = (cumsum(Success) == 1) * Success,
.by = Fish_ID)
df <- read.table(text =
"Fish_ID Instance Success
1 0 0
1 1 0
1 2 1
1 3 0
1 4 1
1 5 0", header = TRUE)