Changing a different number of subsequent entries in a row based on values from other rows

*EDIT: NEW question that has not been answered by the responses below or in other SO questions. Is there a way to do this with for-loops and iterating through orders based on customerid and order_date? Or by using elseif, etc in dplyr??

I want to create a new column summary based on the values in order_result and order_date. The column orders is the number of orders for each customer.

I have the following code -

customer <- c("A", "A", "B", "B", "C", "C", "C", "D", "E", "E", "E", "F", "G", "G", "G", "H", "H", "I", "I", "I", "J", "J", "J", "K", "K", "K", "K", "K")
order <- c("1", "2", "1", "2", "1", "2", "3", "1", "1", "2", "3", "1", "1", "2", "3", "1", "2", "1", "2", "3", "1", "2", "3", "1", "2", "3", "4", "5")
order_result <- c("positive", "lost", "negative", "return", "negative", "lost", "negative", "lost", "lost", "return", "lost", "return", "lost", "negative", "lost", "lost", "positive", "return", "negative", "lost", "lost","positive", "lost", "return", "negative", "lost", "lost", "negative")
order_date <- c("2018-09-14", "2020-08-20", "2018-09-15", "2019-08-25", "2017-09-12", "2018-09-16", "2020-08-21", "2018-08-10", "2017-09-13", "2018-02-16", "2020-08-21", "2017-05-20", "2018-07-05", "2019-02-15", "2021-11-04", "2017-08-07", "2021-08-05", "2019-07-30", "2020-09-23", "2020-11-23","2017-10-30", "2018-04-09", "2020-04-09", "2019-07-30", "2019-12-04", "2020-04-04", "2020-08-03", "2021-12-24")
df1 <- data.frame(customer, order, order_result, order_date)

I want to parse through order_results for each customer from earliest to latest order_date and create a new column summary with yes or no entries. The 1st order for a customer always is "yes" in the summary column, and we keep moving down the list of orders for each customer. As we go through the orders based on the order_date (earliest to latest) for each customer, we look at the order_result to determine how many rows to fill out in summary and with what value. Say we start with an index order. If the order_result is "negative", then an unspecified number of later rows in summary for that customer are either "no" if their order_date is ≤400 days from the index order or yes if >400 days from the index order_date. The number of rows to fill out with "no" depends on the actual order_dates, which can vary based on customer. If the order_result is "return" or "lost", the next order has "yes" in summary. If the order_result is "positive", all later orders for that patient have "no" in summary. We move down the list of orders by jumping to the next order that has yes in summary (which becomes the next index order) and repeating.

Each customerid is independent of each other. The result should be:

summary <- c("Yes", "No", "Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "Yes", "No", "Yes", "Yes", "No", "No", "Yes")

My question is I am not sure how to approach filling out an unspecified number of rows with "no" since it all depends on the actual order_dates and the elapsed time from the index date. In addition, how can I jump from 1 row with "yes" to the next row with "yes" in summary (ie, change index dates) for the same customer (not the whole dataset) and repeat the process all over again? I tried using lag/lead but was not getting the correct output. Thanks!

EDIT: Here is my pseudocode:

if (order == 1) {
summary == 'TRUE'}  #first order for a customer is always TRUE. 
if (order_result[row_number()] == 'positive') {   #If result is positive
summary[row_number()+ length(?)] == FALSE} #After a positive result, all subsequent rows in summary for that customer are FALSE.
if (order_result[row_number()] == 'negative') {   #If result is negative, there are 2 options based on time difference between that order date and subsequent order dates. 
if (diff_time(orderdate[row_number()], orderdate[row_number(?)]) <= 400 {  
summary[row_number()+ length(?)] == FALSE}   #After a negative result, all subsequent rows under summary within 400 days of that order for that customer are FALSE
else summary[row_number()+ length(?)] == TRUE}  #Otherwise all subsequent rows in summary over 400 days for that customer are TRUE
if (order_result[row_number()] == 'lost' | order_result[row_number()] == 'return')  {
summary[row_number() + 1] == TRUE}  #If order result is lost or return, the next order for that customer is true under summary.
}

Please check

df %>% 
  mutate(new=ifelse(order==1, 'Yes', NA),
  lag=ifelse(order!=1,lag(order_date), NA),
  diff=as.Date(order_date)-as.Date(lag),
  negpos=ifelse(order==1 & order_result %in% c('positive','negative'),order_result,NA_character_),
  negpos2=ifelse(order!=1 & !order_result %in% c('positive','negative'),order_result,NA_character_)) %>% 
  group_by(customer) %>% 
  fill(negpos,negpos2) %>% ungroup() %>% 
  mutate(summary=case_when(
    order!=1 & order_result=='negative' & negpos=='negative' & negpos2 %in% c('lost','return') ~ 'Yes',
    order!=1 & diff<=400 & negpos=='negative' ~ 'No',
    order!=1 & diff>400 & negpos=='negative' ~ 'Yes',
    order==1 ~ 'Yes',
    order!=1 & order_result %in% c('lost','return') & negpos=='negative' ~ 'No',
    order!=1 & negpos=='positive' ~ 'No'
    )) %>% select(-c(new,lag,negpos,negpos2,diff))

# output

# A tibble: 8 × 5
  customer order order_result order_date summary
  <chr>    <chr> <chr>        <chr>      <chr>  
1 A        1     positive     2018-09-14 Yes    
2 A        2     lost         2020-08-20 No     
3 B        1     negative     2018-09-15 Yes    
4 B        2     return       2019-08-25 No     
5 C        1     negative     2017-09-12 Yes    
6 C        2     lost         2018-09-16 No     
7 C        3     negative     2020-08-21 Yes    
8 D        1     lost         2018-08-10 Yes    

based on your comment, here is the updated code

df %>% 
  mutate(new=ifelse(order==1, 'Yes', NA),
         lag=ifelse(order!=1,lag(order_date), NA),
         diff=as.Date(order_date)-as.Date(lag),
         negpos=ifelse(order==1 & order_result %in% c('positive','negative'),order_result,NA_character_),
         negpos2=ifelse(order!=1 & !order_result %in% c('positive','negative'),order_result,NA_character_)) %>% 
  group_by(customer) %>% 
  fill(negpos,negpos2) %>% ungroup() %>% 
  mutate(summary=case_when(
    order!=1 & order_result=='negative' & negpos=='negative' & negpos2 %in% c('lost','return') ~ 'Yes',
    order!=1 & diff<=400 & negpos=='negative' ~ 'No',
    order!=1 & diff>400 & negpos=='negative' ~ 'Yes',
    order==1 ~ 'Yes',
    order!=1 & order_result %in% c('lost','return') & negpos=='negative' ~ 'No',
    order!=1 & negpos=='positive' ~ 'No',
    order!=1 & is.na(negpos) & !is.na(negpos2) ~ 'Yes',
    order!=1 & diff<=400 & order_result=='negative' ~ 'No',
    order!=1 & diff>400 & order_result=='negative' ~ 'No',
    order!=1 & order_result=='positive' ~ 'No'
  )) %>% select(-c(new,lag,negpos,negpos2,diff))

^{Created on 2023-07-04 with reprex v2.0.2}

# A tibble: 12 × 5
   customer order order_result order_date summary
   <chr>    <chr> <chr>        <chr>      <chr>  
 1 A        1     positive     2018-09-14 Yes    
 2 A        2     lost         2020-08-20 No     
 3 B        1     negative     2018-09-15 Yes    
 4 B        2     return       2019-08-25 No     
 5 C        1     negative     2017-09-12 Yes    
 6 C        2     lost         2018-09-16 No     
 7 C        3     negative     2020-08-21 Yes    
 8 D        1     lost         2018-08-10 Yes    
 9 E        1     lost         2017-09-13 Yes    
10 E        2     return       2018-02-16 Yes    
11 E        3     lost         2020-08-21 Yes    
12 F        1     return       2017-05-20 Yes