I'm looking to do the following -- cumulative sum the indicator values and remove the indicators after those days original:
transaction | day | indicator |
---|---|---|
1 | 1 | 0 |
1 | 2 | 0 |
1 | 3 | 0 |
1 | 4 | 1 |
1 | 5 | 1 |
1 | 6 | 1 |
2 | 1 | 0 |
2 | 2 | 0 |
2 | 3 | 0 |
2 | 4 | 0 |
2 | 5 | 1 |
2 | 6 | 1 |
and make the new table like this --
transaction | day | indicator |
---|---|---|
1 | 1 | 0 |
1 | 2 | 0 |
1 | 3 | 0 |
1 | 4 | 3 |
2 | 1 | 0 |
2 | 2 | 0 |
2 | 3 | 0 |
2 | 4 | 0 |
2 | 5 | 2 |
CodePudding user response:
Change all day with indicator == 1 to the first day with indicator == 1
df%>%
group_by(transaction)%>%
mutate(day=case_when(indicator==0~day,
T~head(day[indicator==1],1)))%>%
group_by(transaction,day)%>%
summarise(indicator=sum(indicator))%>%
ungroup
transaction day indicator
<int> <int> <int>
1 1 1 0
2 1 2 0
3 1 3 0
4 1 4 3
5 2 1 0
6 2 2 0
7 2 3 0
8 2 4 0
9 2 5 2
CodePudding user response:
Please try the below code
code
df <- bind_rows(df1, df2) %>% group_by(transaction) %>%
mutate(cumsum=cumsum(indicator), cumsum2=ifelse(cumsum==1, day, NA)) %>%
fill(cumsum2) %>%
mutate(day=ifelse(!is.na(cumsum2), cumsum2, day)) %>%
group_by(transaction, day) %>% slice_tail(n=1) %>% select(-cumsum2)
Created on 2023-01-19 with reprex v2.0.2
output
# A tibble: 8 × 4
# Groups: transaction, day [8]
transaction day indicator cumsum
<dbl> <int> <dbl> <dbl>
1 1 1 0 0
2 1 2 0 0
3 1 3 0 0
4 1 4 1 3
5 2 1 0 0
6 2 2 0 0
7 2 3 0 0
8 2 4 1 2