| 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 |
I have a data frame with column "Fish_ID","Instance" and"Success" and I want to create a column "First_Success", containing a 1 only at first success and otherwise 0 for each fish_ID group. In the actual data I have more than one Fish_ID. Here is an example. I came up with this code:
mydata %>%
group_by(fish_ID) %>%
mutate(Fist_Success = ifelse(Success == 1, 1, 0))
But with this code, it counts for all success values that equals to 1 and I just want to it only takes for the first success as 1 for the "First_Success" column and the rest is 0 within the same fish_ID group. I would appreciate any suggestions. Thanks a lot!
You can use 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
It has a variant suggested by @langtang:
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)