grouping to aggregate values, but tripping up on NA's - r

Question

I have long data, and I am trying to make a new variable (consistent) that is the value for a given column (VALUE), for each person (ID), at TIME = 2. I used the code below to do this, but I am getting tripped up on NA's. If the VALUE for TIME = 2 is NA, then I want it to grab the VALUE at TIME = 1 instead. That part I'm not sure how to do. So, in the example below, I want the new variable (consistent) should be 10 instead of NA.
ID = c("A", "A", "B", "B", "C", "C", "D", "D")
TIME = c(1, 2, 1, 2, 1, 2, 1, 2)
VALUE = c(8, 9, 10, NA, 12, 13, 14, 9)
df = data.frame(ID, TIME, VALUE)
df <- df %>%
group_by(ID) %>%
mutate(consistent = VALUE[TIME == 2]) %>% ungroup
df

Answer 1

If we want to use the same code, then coalesce with the 'VALUE' where 'TIME' is 1 (assuming there is a single observation of 'TIME' for each 'ID')
library(dplyr)
df %>%
group_by(ID) %>%
mutate(consistent = coalesce(VALUE[TIME == 2], VALUE[TIME == 1])) %>%
ungroup
-output
# A tibble: 8 × 4
ID TIME VALUE consistent
<chr> <dbl> <dbl> <dbl>
1 A 1 8 9
2 A 2 9 9
3 B 1 10 10
4 B 2 NA 10
5 C 1 12 13
6 C 2 13 13
7 D 1 14 9
8 D 2 9 9
Or another option is to arrange before doing the group_by and get the first element of 'VALUE' (assuming no replicating for 'TIME')
df %>%
arrange(ID, is.na(VALUE), desc(TIME)) %>%
group_by(ID) %>%
mutate(consistent = first(VALUE)) %>%
ungroup
-output
# A tibble: 8 × 4
ID TIME VALUE consistent
<chr> <dbl> <dbl> <dbl>
1 A 2 9 9
2 A 1 8 9
3 B 1 10 10
4 B 2 NA 10
5 C 2 13 13
6 C 1 12 13
7 D 2 9 9
8 D 1 14 9

Answer 2

Another possible solution, using tidyr::fill:
library(tidyverse)
df %>%
group_by(ID) %>%
mutate(consistent = VALUE) %>% fill(consistent) %>% ungroup
#> # A tibble: 8 × 4
#> ID TIME VALUE consistent
#> <chr> <dbl> <dbl> <dbl>
#> 1 A 1 8 8
#> 2 A 2 9 9
#> 3 B 1 10 10
#> 4 B 2 NA 10
#> 5 C 1 12 12
#> 6 C 2 13 13
#> 7 D 1 14 14
#> 8 D 2 9 9

Answer 3

You can also use ifelse with your condition. TIME is guaranteed to be 1 in this scenario if there are only 2 group member each with TIME 1 and 2.
df %>%
group_by(ID) %>%
arrange(TIME, .by_group=T) %>%
mutate(consistent=ifelse(is.na(VALUE)&TIME==2, lag(VALUE), VALUE)) %>%
ungroup()
# A tibble: 8 × 4
ID TIME VALUE consistent
<chr> <dbl> <dbl> <dbl>
1 A 1 8 8
2 A 2 9 9
3 B 1 10 10
4 B 2 NA 10
5 C 1 12 12
6 C 2 13 13
7 D 1 14 14
8 D 2 9 9