How to use dplyr or data.table to perform look-ahead calculations by groups of data subsets?

Question

I'm interested in using dplyr (for ease) and/or data.table (for speed since actual data to be operated on has 3 million + rows) to perform "look ahead" calculations on a data set, by grouped subsets, and then flag those rows where those look ahead calculations return a value of 0. The image at the bottom better illustrates what I'm trying to do, where State_1 is a column added (by dplyr mutate(...) for example) to the original data dataframe. Any recommendations for how to do this?

Mastering look-ahead calculations will help tremendously, it's something I've always dealt with clumsily, in XLS.

Starting with the data dataframe, with generating code immediately beneath:

> data
   ID Period Values_1 Values_2 State
1   1      1        5        5    X0
2   1      2        0        2    X1
3   1      3        0        0    X2
4   1      4        0       12    X1
5   2      1        1        2    X0
6   2      2        0        0    X2
7   2      3        0        0    X0
8   2      4        0        0    X0
9   3      1        0        0    X2
10  3      2        0        0    X1
11  3      3        0        0    X9
12  3      4        0        2    X3
13  4      1        1        4    X2
14  4      2        2        5    X1
15  4      3        3        6    X9
16  4      4        0        0    X3

data <- 
  data.frame(
    ID = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4),
    Period = c(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4),
    Values_1 = c(5, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0),
    Values_2 = c(5, 2, 0, 12, 2, 0, 0, 0, 0, 0, 0, 2, 4, 5, 6, 0),
    State = c("X0","X1","X2","X1","X0","X2","X0","X0", "X2","X1","X9","X3", "X2","X1","X9","X3")
  )

Here is an illustration of what I'm try to accomplish:

Answer 1

We may use a group by approach - grouped by 'ID', loop over the 'Values' column with if_all to return TRUE for rows having only 0 values, then create an index where the last position is not the last observation along with the created index and replace

library(dplyr)
data %>%
   group_by(ID) %>%
   mutate(ind1 =  if_all(starts_with('Values'), ~ .x == 0), 
       ind2 = last(which(ind1))== n() & ind1, 
     State_1 = replace(State, ind2, 'XX'), ind1 = NULL, ind2 = NULL) %>%
   ungroup

-output

# A tibble: 16 × 6
      ID Period Values_1 Values_2 State State_1
   <dbl>  <dbl>    <dbl>    <dbl> <chr> <chr>  
 1     1      1        5        5 X0    X0     
 2     1      2        0        2 X1    X1     
 3     1      3        0        0 X2    X2     
 4     1      4        0       12 X1    X1     
 5     2      1        1        2 X0    X0     
 6     2      2        0        0 X2    XX     
 7     2      3        0        0 X0    XX     
 8     2      4        0        0 X0    XX     
 9     3      1        0        0 X2    X2     
10     3      2        0        0 X1    X1     
11     3      3        0        0 X9    X9     
12     3      4        0        2 X3    X3     
13     4      1        1        4 X2    X2     
14     4      2        2        5 X1    X1     
15     4      3        3        6 X9    X9     
16     4      4        0        0 X3    XX