Cumulative sum based on factor on R - r

Question

I have the following dataset, and I need to acumulate the value and
sum, if the factor is 0, and then put the cummulated sum when I found
the factor != 0.
I've tried the loop bellow, but it didn't worked at all.
for(i in dataset$Variable.1) {
ifelse(dataset$Factor == 0,
dataset$teste <- dataset$Variable.1 + i,
dataset$teste <- dataset$Variable.1)
i<- dataset$Variable.1
print(i)
}
Any ideas?
Bellow an example of the dataset. I wish to get the "Result" Column.
On the real one, I also have a negative factor (-1).
Date Factor Variable.1 Result
1 03/02/2018 0 0.75 0.75
2 04/02/2018 0 0.75 1.50
3 05/02/2018 1 0.96 2.46
4 06/02/2018 1 0.76 0.76
5 07/02/2018 0 1.35 1.35
6 08/02/2018 1 0.70 2.05
7 09/02/2018 1 2.02 2.02
8 10/02/2018 0 0.00 0.00
9 11/02/2018 0 0.00 0.00
10 12/02/2018 0 0.20 0.20
11 13/02/2018 0 0.13 0.33
12 14/02/2018 0 1.64 1.97
13 15/02/2018 0 0.03 2.00
14 16/02/2018 1 0.51 2.51
15 17/02/2018 1 0.00 0.00
16 18/02/2018 0 0.00 0.00
17 19/02/2018 0 0.83 0.83
18 20/02/2018 1 0.42 1.25
19 21/02/2018 1 0.17 0.17
20 22/02/2018 1 0.97 0.97
21 23/02/2018 0 0.92 0.92
22 24/02/2018 0 0.00 0.92
23 25/02/2018 0 0.00 0.92
24 26/02/2018 1 0.19 1.11
25 27/02/2018 1 0.87 0.87
26 28/02/2018 1 0.85 0.85
27 01/03/2018 1 1.95 1.95
28 02/03/2018 1 0.54 0.54
29 03/03/2018 1 0.00 0.00
30 04/03/2018 0 0.00 0.00
31 05/03/2018 0 1.17 1.17
32 06/03/2018 1 0.25 1.42
33 07/03/2018 1 1.45 1.45
Thanks In advance.

Answer 1

If you want to stick with the for-loop, you can try this code :
DF$Result <- NA
prev <- 0
for(i in seq_len(nrow(DF))){
DF$Result[i] <- DF$Variable.1[i] + prev
if(DF$Factor[i] == 1)
prev <- 0
else
prev <- DF$Result[i]
}

Answer 2

Iteratively, try something like:
a=as.data.frame(cbind(Factor=c(0,0,1,1,0,1,1,
rep(0,3),1),Variable.1=c(0.75,0.75,0.96,0.71,1.35,0.7,
0.75,0.96,0.71,1.35,0.7)))
Result=0
aux=NULL
for (i in 1:nrow(a)){
if (a$Factor[i]==0){
Result=Result+a$Variable.1[i]
aux=c(aux,Result)
} else{
Result=Result+a$Variable.1[i]
aux=c(aux,Result)
Result=0
}
}
a$Results=aux
a
Factor Variable.1 Results
1 0 0.75 0.75
2 0 0.75 1.50
3 1 0.96 2.46
4 1 0.71 0.71
5 0 1.35 1.35
6 1 0.70 2.05
7 1 0.75 0.75
8 0 0.96 0.96
9 0 0.71 1.67
10 0 1.35 3.02
11 1 0.70 3.72

Answer 3

A possibility using tidyverse and data.table:
df %>%
mutate(temp = ifelse(Factor == 1 & lag(Factor) == 1, NA, 1), #Marking the rows after the first 1 in "Factor" as NA
temp = ifelse(!is.na(temp), rleid(temp), NA)) %>% #Run length along non-NA values
group_by(temp) %>% #Grouping by run length
mutate(Result = ifelse(!is.na(temp), cumsum(Variable.1), Variable.1)) %>% #Cumulative sum of desired rows
ungroup() %>%
select(-temp) #Removing the redundant variable
Date Factor Variable.1 Result
<chr> <int> <dbl> <dbl>
1 03/02/2018 0 0.750 0.750
2 04/02/2018 0 0.750 1.50
3 05/02/2018 1 0.960 2.46
4 06/02/2018 1 0.760 0.760
5 07/02/2018 0 1.35 1.35
6 08/02/2018 1 0.700 2.05
7 09/02/2018 1 2.02 2.02
8 10/02/2018 0 0. 0.
9 11/02/2018 0 0. 0.
10 12/02/2018 0 0.200 0.200