extrapolate position of vehicle by adding new rows to tibbles

Question

I have time t, speed v and position x of a vehicle along with other informations: info1, info2, status

How can I extend the tibble by linearly extrapolating the position based on last speed (v = 14) for given timestamps. So the added rows are copy of the last row except with the predicted positions and status changed to "predicted"`.

Is it possible to do it without using loop.


tbl <- tibble(info1 = rep("a", 3),
              info2 = rep("b", 3),
              t = c(1, 2, 3),
              v = c(12, 13, 14),
              x = c(12, 24, 37), 
              status = rep("real", 3))

timestamps <- c(4, 5, 6, 8) # timestamps does not linearly increase

# desired output
tbl_desired <- tibble(info1 = rep("a", 7),
                      info2 = rep("b", 7),
                      t = c(1, 2, 3, 4, 5, 6, 8),
                      v = c(12, 13, 14, 14, 14, 14, 14),
                      x = c(12, 24, 37, 51, 65, 79, 107), 
                      status = c(rep("real", 3), rep("predicted", 4)))

akrun · Accepted Answer · 2021-12-06T22:15:04.590

The conditions are not clear especially the predicted values in 'x'. Below code works in the following way

Extracts the last row (slice_tail, n = 1)
update the columns 't', 'v', 'x' (summarise)
Bind the rows from the original data (bind_rows)

library(dplyr)
tbl %>% 
   slice_tail(n = 1) %>% 
   summarise(info1, info2, t = timestamps, v = v, 
     x =  (x + cumsum(c(1, diff(t)) * 
      rep(last(v), length(t)))), status = 'predicted') %>% 
   bind_rows(tbl, .)

-output

# A tibble: 7 × 6
  info1 info2     t     v     x status   
  <chr> <chr> <dbl> <dbl> <dbl> <chr>    
1 a     b         1    12    12 real     
2 a     b         2    13    24 real     
3 a     b         3    14    37 real     
4 a     b         4    14    51 predicted
5 a     b         5    14    65 predicted
6 a     b         6    14    79 predicted
7 a     b         8    14   107 predicted

If there are many columns, after sliceing the last row, use mutate to update only the columns that needs to be changed and wrap in a list whereever the length is greater than 1, then unnest the list column

library(tidyr)
tbl %>% 
   slice_tail(n = 1) %>%
   mutate(t = list(timestamps), v = v,
    x = list((x + cumsum(c(1, diff(timestamps)) * 
      rep(last(v), length(timestamps))))), status = 'predicted') %>% 
   unnest(where(is.list)) %>% 
   bind_rows(tbl, .)

-output

# A tibble: 7 × 6
  info1 info2     t     v     x status   
  <chr> <chr> <dbl> <dbl> <dbl> <chr>    
1 a     b         1    12    12 real     
2 a     b         2    13    24 real     
3 a     b         3    14    37 real     
4 a     b         4    14    51 predicted
5 a     b         5    14    65 predicted
6 a     b         6    14    79 predicted
7 a     b         8    14   107 predicted

Or use add_row and then fill the NA rows with previous non-NA for those columns not specified in the add_row

library(tibble)
tbl %>% 
   add_row(t = timestamps, v = last(.$v), 
    x = (last(.$x) + cumsum(c(1, diff(timestamps)) * 
       rep(last(.$v), length(timestamps)))), status = 'predicted') %>% 
   fill(everything())

-output

# A tibble: 7 × 6
  info1 info2     t     v     x status   
  <chr> <chr> <dbl> <dbl> <dbl> <chr>    
1 a     b         1    12    12 real     
2 a     b         2    13    24 real     
3 a     b         3    14    37 real     
4 a     b         4    14    51 predicted
5 a     b         5    14    65 predicted
6 a     b         6    14    79 predicted
7 a     b         8    14   107 predicted

Thanks, I have over 50 columns (not just info1, info2). I could show in the example. Is there a way to duplicate with out writing all columns. — SiH, Dec 06 '21 at 21:56
@SiH Does the update works for you? The columns to be changed can only be changed manually because the values are going to be different for those columns and the functions to be applied. — akrun, Dec 06 '21 at 22:04
I am trying to repeat the steps but I am getting errors - I have 4 rows and I am adding 10 rows. New columns must be compatible with `.data`. x New columns have 10 rows. i `.data` has 4 rows. — SiH, Dec 06 '21 at 22:23
@SiH I don't know about the errors. All the solutions works on the data you provided. i.e. I provided 3 solutions — akrun, Dec 06 '21 at 22:24
@SiH The incompabile may be from a type issue which I couldn't reproduce from your data showed — akrun, Dec 06 '21 at 22:25

extrapolate position of vehicle by adding new rows to tibbles

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