Assigning NAs to rows with conditional statement in r - r

Question

I'm trying to assign NAs to the first two rows of each event, with the following conditional statement:
If the first day of each event has a value of "variable" = 0, check the day before. If the day before (last day of previous event) has a "variable" > 0, then assign NAs to the first two rows of the event having "variable" = 0 on the first day. If the day before has a "variable" = 0, do nothing.
Here is an example:
day <- c(1:16)
event<- c(1,1,2,3,4,4,4,5,5,5,6,6,6,7,7,7)
variable<- c(0,0,5,0,0,0,10,0,1,1,0,0,0,0,0,0)
A<- data.frame(day, event, variable)
day event variable
1 1 1 0
2 2 1 0
3 3 2 5
4 4 3 0
5 5 4 0
6 6 4 0
7 7 4 10
8 8 5 0
9 9 5 1
10 10 5 1
11 11 6 0
12 12 6 0
13 13 6 0
14 14 7 0
15 15 7 0
16 16 7 0
And how it should look like
day event variable
1 1 1 0
2 2 1 0
3 3 2 5
4 4 3 NA
5 5 4 0
6 6 4 0
7 7 4 10
8 8 5 NA
9 9 5 NA
10 10 5 1
11 11 6 NA
12 12 6 NA
13 13 6 0
14 14 7 0
15 15 7 0
16 16 7 0
Note: It doesn't matter if event 1 has to be assigned with NAs
I tried to do this with if conditions, but is not working well. Any idea? and thanks in advance!

Answer 1

EDIT: New example data from OP
library(data.table)
event2<- c(1,2,2,3,4,4,4,4,4,5,5)
variable2<- c(140, 0, 69, 569, 28, 0,0,0,100,0,0)
desire_output<- c(140, NA, NA, 569, 28, 0,0,0,100, NA,NA)
A2<- data.frame(event2, variable2, desire_output)
setDT(A2)
A2[,first_days_event:=fifelse(.I==min(.I),1,fifelse(.I==min(.I)+1,2,NA_integer_)),by=.(event2)]
A2[,result:={v <- variable2
for (i in 2:.N) {
if (is.na(first_days_event[i])) {
v[i] <- variable2[i]
} else if (first_days_event[i]==1 & variable2[i]==0){
if (variable2[i-1]>0) {
v[i] <- NA_integer_
if (first_days_event[i+1]==2) {
v[i+1] <- NA_integer_
}
}
}
}
v}]
A2
#> event2 variable2 desire_output first_days_event result
#> 1: 1 140 140 1 140
#> 2: 2 0 NA 1 NA
#> 3: 2 69 NA 2 NA
#> 4: 3 569 569 1 569
#> 5: 4 28 28 1 28
#> 6: 4 0 0 2 0
#> 7: 4 0 0 NA 0
#> 8: 4 0 0 NA 0
#> 9: 4 100 100 NA 100
#> 10: 5 0 NA 1 NA
#> 11: 5 0 NA 2 NA
I will use this simple loop solution. Just need to create a flag indicating the first tow days of each event.
library(data.table)
day <- c(1:16)
event<- c(1,1,2,3,4,4,4,5,5,5,6,6,6,7,7,7)
variable<- c(0,0,5,0,0,0,10,0,1,1,0,0,0,0,0,0)
A<- data.frame(day, event, variable)
setDT(A)
A[,first_days_event:=fifelse(.I==min(.I),1,fifelse(.I==min(.I)+1,2,NA_integer_)),by=.(event)]
A[,result:={v <- numeric(.N)
for (i in 2:.N) {
if (is.na(first_days_event[i])) {
v[i] <- variable[i]
} else if (first_days_event[i]==1){
if (variable[i-1]>0) {
v[i] <- NA_integer_
if (first_days_event[i+1]==2) {
v[i+1] <- NA_integer_
}
} else {
v[i] <- variable[i]
}
}
}
v}]
A
#> day event variable first_days_event result
#> 1: 1 1 0 1 0
#> 2: 2 1 0 2 0
#> 3: 3 2 5 1 5
#> 4: 4 3 0 1 NA
#> 5: 5 4 0 1 0
#> 6: 6 4 0 2 0
#> 7: 7 4 10 NA 10
#> 8: 8 5 0 1 NA
#> 9: 9 5 1 2 NA
#> 10: 10 5 1 NA 1
#> 11: 11 6 0 1 NA
#> 12: 12 6 0 2 NA
#> 13: 13 6 0 NA 0
#> 14: 14 7 0 1 0
#> 15: 15 7 0 2 0
#> 16: 16 7 0 NA 0

Answer 2

Here is a potential tidyverse approach.
You can store the last value of a group in a temporary column last_var and use lag to move to the first row of the following group for comparison.
Note that the default in lag will determine if variable in event 1 is 0 or NA.
The final mutate will evaluate the row if within the first 2 rows of the group, and check last_var to determine if should set to NA or leave alone.
Edit: For the ifelse need to also check if first day's variable for the event is 0.
library(tidyverse)
A %>%
group_by(event) %>%
mutate(last_var = ifelse(row_number() == n(), last(variable), 0)) %>%
ungroup %>%
mutate(last_var = lag(last_var, default = 0)) %>%
group_by(event) %>%
mutate(variable = ifelse(row_number() <= 2 & first(last_var) > 0 & first(variable) == 0, NA, variable)) %>%
select(-last_var)
Output
# A tibble: 16 x 3
# Groups: event [7]
day event variable
<int> <dbl> <dbl>
1 1 1 0
2 2 1 0
3 3 2 5
4 4 3 NA
5 5 4 0
6 6 4 0
7 7 4 10
8 8 5 NA
9 9 5 NA
10 10 5 1
11 11 6 NA
12 12 6 NA
13 13 6 0
14 14 7 0
15 15 7 0
16 16 7 0
With the second data frame included in the comments:
Output
# A tibble: 11 x 3
# Groups: event [5]
event variable desire_output
<dbl> <dbl> <dbl>
1 1 140 140
2 2 NA NA
3 2 NA NA
4 3 569 569
5 4 28 28
6 4 0 0
7 4 0 0
8 4 0 0
9 4 100 100
10 5 NA NA
11 5 NA NA