How to loop ifelse function through a grouped variable with dplyr - r

Question

I'm trying to apply a rule for a group of IDs that, upon the first instance where the value for a variable in one row equals 1, all values for another variable in all subsequent rows in that group equal 1.
Essentially, here is what I am trying to do:
I have:
ID D
1 1
1 0
1 0
2 0
2 0
3 1
3 0
3 0
4 1
4 0
4 1
4 1
4 1
4 0
I want:
ID D PREV
1 1 0
1 0 1
1 0 1
2 0 0
2 0 0
3 1 0
3 0 1
3 0 1
4 1 0
4 0 1
4 1 1
4 1 1
4 0 1
I'm trying to use dplyr to iterate through a series of grouped rows, in each one applying an ifelse function. My code looks like this:
data$prev = 0
data <-
data %>%
group_by(id)%>%
mutate(prev = if_else(lag(prev) == 1 | lag(d) == 1, 1, 0))
But for some reason, this is not applying the ifelse function over the whole group, resulting in data that looks something like this:
ID D PREV
1 1 0
1 0 1
1 0 0
2 0 0
2 0 0
3 1 0
3 0 1
3 0 0
4 1 0
4 0 1
4 1 0
4 1 1
4 0 1
Can anyone help me with this?

Answer 1

What about this:
library(dplyr)
df %>%
group_by(ID) %>%
mutate(prev = +(cumsum(c(0, D[-length(D)])) > 0)) %>%
ungroup()
#> # A tibble: 14 x 3
#> ID D prev
#> <int> <int> <int>
#> 1 1 1 0
#> 2 1 0 1
#> 3 1 0 1
#> 4 2 0 0
#> 5 2 0 0
#> 6 3 1 0
#> 7 3 0 1
#> 8 3 0 1
#> 9 4 1 0
#> 10 4 0 1
#> 11 4 1 1
#> 12 4 1 1
#> 13 4 1 1
#> 14 4 0 1
To explain what it does, let's just take a simple vector.
The calc will be the same for each group.
Be x our vector
x <- c(0,0,0,1,1,0,0,2,3,4)
Do the cumulative sum over x
cumsum(x)
#> [1] 0 0 0 1 2 2 2 4 7 11
You are interested only on value above zeros, therefore:
cumsum(x)>0
#> [1] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
You don't want logical, but numeric. Just a + makes the trick
+(cumsum(x)>0)
#> [1] 0 0 0 1 1 1 1 1 1 1
However, you want the 1s delayed by 1. Thus, we had a zero on top of x
+(cumsum(c(0,x))>0)
#> [1] 0 0 0 0 1 1 1 1 1 1 1
We need to keep the same length, so we remove the last value of x.
+(cumsum(c(0, x[-length(x)])) > 0)
#> [1] 0 0 0 0 1 1 1 1 1 1
And that makes the trick.

Answer 2

We can use lag
library(dplyr)
df %>%
group_by(ID) %>%
mutate(prev = lag(cumsum(D) > 0, default = 0))
-output
# A tibble: 14 x 3
# Groups: ID [4]
# ID D prev
# <dbl> <dbl> <dbl>
# 1 1 1 0
# 2 1 0 1
# 3 1 0 1
# 4 2 0 0
# 5 2 0 0
# 6 3 1 0
# 7 3 0 1
# 8 3 0 1
# 9 4 1 0
#10 4 0 1
#11 4 1 1
#12 4 1 1
#13 4 1 1
#14 4 0 1
data
df <- data.frame(
ID = c(1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4),
D = c(1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0)
)

Answer 3

You can use a new function from dplyr dplyr::group_modify to apply function over groups
df <- data.frame(
ID = c(1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4),
D = c(1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0)
)
df %>% group_by(ID) %>% group_modify(
function(x, y){
boo <- x[1, ]$D == 1
ifelse(boo,
{x$prev = 1
x$prev[1] = 0
},
{x$prev = 0})
x
}
)
# A tibble: 14 x 3
# Groups: ID [4]
ID D prev
<dbl> <dbl> <dbl>
1 1 1 0
2 1 0 1
3 1 0 1
4 2 0 0
5 2 0 0
6 3 1 0
7 3 0 1
8 3 0 1
9 4 1 0
10 4 0 1
11 4 1 1
12 4 1 1
13 4 1 1
14 4 0 1