Update all the records in between from NA to the value - r

Question

I have a data table in the below format :
id c1 c2
1 1 NA
1 1 NA
1 1 10
1 1 NA
1 1 NA
1 1 10
1 1 NA
1 1 NA
1 1 11
1 1 NA
1 1 NA
1 1 11
2 1 NA
2 1 12
2 1 NA
2 1 NA
2 1 12
From this data table I would like to update all the NA in between the two values in c2 as below:
id c1 c2
1 1 NA
1 1 NA
1 1 10
1 1 10
1 1 10
1 1 10
1 1 NA
1 1 NA
1 1 11
1 1 11
1 1 11
1 1 11
2 1 NA
2 1 12
2 1 12
2 1 12
2 1 12

Answer 1

Can do it using a for loop and which():
df=data.frame(id = c(rep(1,12)),c2 = c(NA,NA,10,NA,NA,10, NA,NA,11,NA,11,NA))
Find unique values of c2:
vals=unique(df[which(!is.na(df$c2)),'c2'])
Loop through unique values and replace observations between their first and last appearance:
for(i in vals){
df[min(which(df$c2==i)):max(which(df$c2==i)),'c2']=i
}

Answer 2

Besides David's approach which is working directly with row indices there is another data.table approach which uses a non-equi join:
# coerce to data.table
setDT(DT)[
# append unique row id
, rn := .I][
# non-equi join on row ids
DT[!is.na(c2), .(rmin = min(rn), rmax = max(rn)), by = c2],
on = .(rn >= rmin, rn <= rmax), c2 := i.c2][
# remove row id column
, rn := NULL][]
id c1 c2
1: 1 1 NA
2: 1 1 NA
3: 1 1 10
4: 1 1 10
5: 1 1 10
6: 1 1 10
7: 1 1 NA
8: 1 1 NA
9: 1 1 11
10: 1 1 11
11: 1 1 11
12: 1 1 11
13: 2 1 NA
14: 2 1 12
15: 2 1 12
16: 2 1 12
17: 2 1 12
Caveat
The expression
DT[!is.na(c2), .(rmin = min(rn), rmax = max(rn)), by = c2]
returns the row id ranges for each unique value of c2
c2 rmin rmax
1: 10 3 6
2: 11 9 12
3: 12 14 17
There is an implicit assumption that the row id ranges do not overlap. It requires that each "gap" is associated with a unique c2 value. This affects other solutions 1, 2 as well.
Improved solution using rleid()
The code can be improved to handle cases where the above mentioned assumption is violated.
Using rleid(), we can distinguish different gaps even if the have the same c2 value. For instance, for the second sample data set
DT2[!is.na(c2), .(c2 = first(c2), rmin = min(rn), rmax = max(rn)), by = rleid(c2)]
rleid c2 rmin rmax
1: 1 10 3 6
2: 2 11 9 12
3: 3 12 14 17
4: 4 10 20 23
The complete code:
setDT(DT2)[, rn := .I][
DT2[!is.na(c2), .(c2 = first(c2), rmin = min(rn), rmax = max(rn)), by = rleid(c2)],
on = .(rn >= rmin, rn <= rmax), c2 := i.c2][, rn := NULL][]
id c1 c2
1: 1 1 NA
2: 1 1 NA
3: 1 1 10
4: 1 1 10
5: 1 1 10
6: 1 1 10
7: 1 1 NA
8: 1 1 NA
9: 1 1 11
10: 1 1 11
11: 1 1 11
12: 1 1 11
13: 2 1 NA
14: 2 1 12
15: 2 1 12
16: 2 1 12
17: 2 1 12
18: 2 1 NA
19: 2 1 NA
20: 2 1 10
21: 2 1 10
22: 2 1 10
23: 2 1 10
24: 2 1 NA
25: 2 1 NA
id c1 c2
Data
library(data.table)
DT <- fread("id c1 c2
1 1 NA
1 1 NA
1 1 10
1 1 NA
1 1 NA
1 1 10
1 1 NA
1 1 NA
1 1 11
1 1 NA
1 1 NA
1 1 11
2 1 NA
2 1 12
2 1 NA
2 1 NA
2 1 12")
Expanded data set (note the repeated appearance of c2 == 10):
DT2 <- fread("id c1 c2
1 1 NA
1 1 NA
1 1 10
1 1 NA
1 1 NA
1 1 10
1 1 NA
1 1 NA
1 1 11
1 1 NA
1 1 NA
1 1 11
2 1 NA
2 1 12
2 1 NA
2 1 NA
2 1 12
2 1 NA
2 1 NA
2 1 10
2 1 NA
2 1 NA
2 1 10
2 1 NA
2 1 NA")

Answer 3

Okay (new/edited answer), we can make use of the fact that the desired property of a solution is that filling up should yield the same result as filling down:
library(tidyverse)
df %>%
mutate(filled_down = c2, filled_up = c2) %>%
fill(filled_down, .direction="down") %>%
fill(filled_up, .direction="up") %>%
mutate(c2 = ifelse(filled_down == filled_up, filled_down, c2)) %>%
select(-filled_down, -filled_up)