For loop - how to change a varying number of entries if requirement is met in another column

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")
order <- c("1", "2", "1", "2", "1", "2", "3", "1", "1", "2", "3", "1")
order_result <- c("positive", "lost", "negative", "return", "negative", "lost", "negative", "lost", "lost", "return", "lost", "return")
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")
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 true or false entries.

The 1st order for a customer always is TRUE in the summary column.

Then, going row by row, if the order_result is "negative", then an unspecified number of later rows in summary for that customer are either FALSE if their order_date is ≤400 days from the index order or TRUE if >400 days from the index order_date. The number of rows to fill out with FALSE depends on the actual order_dates, which can vary based on customer.

If the order_result is "return" or "lost", the next order has TRUE in summary. If the order_result is "positive", all later orders for that patient have FALSE in summary. We move down the list of orders by jumping to the next order that has TRUE in summary (which becomes the next index order) and repeating.

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

summary <- c(TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE)

My question is I am not sure how to approach filling out an unspecified number of rows with FALSE 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 TRUE to the next row with TRUE in summary (ie, change index dates) for the same customer (not the whole dataset) and repeat the process all over again? Is there a way to do this with for-loops and iterating through orders based on customerid and order_date?? I tried using lag/lead in dplyr 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.

The great thing about mutate is, you can use group_by to group your data, then pass the columns you are interested in to a function, and it will give you the data for each column as a vector, with it filtered to the group you want.

That's the basis of what I did below:

library(tidyverse)

order_process <- function(order_col, result_col, date_col) {
  summary <- c()
  negative_order_date <- NA
  lost_or_returned <- FALSE
  for (i in seq_along(order_col)) {
    
    # first row is always TRUE
    if (i == 1) {
      summary[i] <- TRUE
      
      # if the order was lost or returned, the next one is TRUE. This is that next one
    } else if (lost_or_returned) {
      summary[i] <- TRUE
      lost_or_returned = FALSE
      
      # If positive
    } else if (result_col[i] == "positive") {
      # everything from now until the end of the column is FALSE
      summary[i:length(order_col)] <- FALSE
      return(summary)
      
      # If negative
    } else if (result_col[i] == "negative") {
      if (is.na(negative_order_date)) {
        negative_order_date = date_col[i]
      }
      summary[i] <- date_col[i] - negative_order_date > 400
      
      # If lost or returned
    } else if (result_col[i] == "return" | result_col[i] == "lost") {
      summary[i] <- FALSE
      lost_or_returned <- TRUE
    }
  }
  return(summary)
}

df1 %>% 
  as_tibble() %>% 
  mutate(order_date = ymd(order_date),
         order = as.numeric(order), 
         order_result = as.factor(order_result)) %>%
  group_by(customer) %>%
  mutate(summary = order_process(order, order_result, order_date)) %>%
  pull(summary)

It's the same for every value except the third last - you didn't specify what should happen on the current value when order_result is lost or returned, so I guessed at it being FALSE.