Fill in values between start and end value in R - r

Question

W (blue line below) in my data.frame represents where the water level in the river intersects the elevation profile.
In my data.frame, for each group in ID, I need to fill in values between the start and end value (W)
My data
> head(df, 23)
ID elevation code
1 1 150 <NA>
2 1 140 <NA>
3 1 130 W
4 1 120 <NA>
5 1 110 <NA>
6 1 120 <NA>
7 1 130 W
8 1 140 <NA>
9 1 150 <NA>
10 2 90 <NA>
11 2 80 <NA>
12 2 70 <NA>
13 2 66 W
14 2 60 <NA>
15 2 50 <NA>
16 2 66 W
17 2 70 <NA>
18 2 72 <NA>
19 2 68 W
20 2 65 <NA>
21 2 60 <NA>
22 2 68 W
23 2 70 <NA>
I want the final result to look like below
ID elevation code
1 1 150 <NA>
2 1 140 <NA>
3 1 130 W
4 1 120 W
5 1 110 W
6 1 120 W
7 1 130 W
8 1 140 <NA>
9 1 150 <NA>
10 2 90 <NA>
11 2 80 <NA>
12 2 70 <NA>
13 2 66 W
14 2 60 W
15 2 50 W
16 2 66 W
17 2 70 <NA>
18 2 72 <NA>
19 2 68 W
20 2 65 W
21 2 60 W
22 2 68 W
23 2 70 <NA>
I tried many things but my trials were not successful. Your help will be appreciated.
DATA
> dput(df)
structure(list(ID = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), elevation = c(150L,
140L, 130L, 120L, 110L, 120L, 130L, 140L, 150L, 90L, 80L, 70L,
66L, 60L, 50L, 66L, 70L, 72L, 68L, 65L, 60L, 68L, 70L), code = c(NA,
NA, "W", NA, NA, NA, "W", NA, NA, NA, NA, NA, "W", NA, NA, "W",
NA, NA, "W", NA, NA, "W", NA)), class = "data.frame", row.names = c(NA,
-23L))

Answer 1

You could do:
df %>%
group_by(ID)%>%
mutate(code = coalesce(code, c(NA, "W")[cumsum(!is.na(code)) %% 2 + 1]))
ID elevation code
1 1 150 <NA>
2 1 140 <NA>
3 1 130 W
4 1 120 W
5 1 110 W
6 1 120 W
7 1 130 W
8 1 140 <NA>
9 1 150 <NA>
10 2 90 <NA>
11 2 80 <NA>
12 2 70 <NA>
13 2 66 W
14 2 60 W
15 2 50 W
16 2 66 W
17 2 70 <NA>
18 2 72 <NA>
19 2 68 W
20 2 65 W
21 2 60 W
22 2 68 W
23 2 70 <NA>

Answer 2

We can try replace + cumsum
df %>%
group_by(ID) %>%
mutate(code = replace(code, cumsum(!is.na(code)) %% 2 == 1, "W")) %>%
ungroup()
which gives
# A tibble: 23 x 3
ID elevation code
<int> <int> <chr>
1 1 150 NA
2 1 140 NA
3 1 130 W
4 1 120 W
5 1 110 W
6 1 120 W
7 1 130 W
8 1 140 NA
9 1 150 NA
10 2 90 NA
# ... with 13 more rows

Answer 3

You can create a helper function that creates a sequence between each start and end and assigns 'W' to it.
assign_w <- function(code) {
inds <- which(code == 'W')
code[unlist(Map(seq, inds[c(TRUE, FALSE)], inds[c(FALSE, TRUE)]))] <- 'W'
code
}
and apply it for each ID :
library(dplyr)
df %>%
group_by(ID) %>%
mutate(result = assign_w(code)) %>%
ungroup
# ID elevation code result
#1 1 150 <NA> <NA>
#2 1 140 <NA> <NA>
#3 1 130 W W
#4 1 120 <NA> W
#5 1 110 <NA> W
#6 1 120 <NA> W
#7 1 130 W W
#8 1 140 <NA> <NA>
#9 1 150 <NA> <NA>
#10 2 90 <NA> <NA>
#11 2 80 <NA> <NA>
#12 2 70 <NA> <NA>
#13 2 66 W W
#14 2 60 <NA> W
#15 2 50 <NA> W
#16 2 66 W W
#17 2 70 <NA> <NA>
#18 2 72 <NA> <NA>
#19 2 68 W W
#20 2 65 <NA> W
#21 2 60 <NA> W
#22 2 68 W W
#23 2 70 <NA> <NA>

Answer 4

library(dplyr)
df %>%
group_by(ID) %>%
mutate(water_flag = (1 * !is.na(code)) * if_else(elevation < lag(elevation, default = 0), 1, -1),
water = if_else(cumsum(water_flag) == 1, "W", NA_character_))

Answer 5

First I tried to use fill but had no success. Then I learned here about the benefit of R's recycling property Rename first and second occurence of the same specific value in a column iteratively (Thanks to Ronak!)
# prepare data with renaming `start` and `stop` sequence
df$code[is.na(df$code)] <- "NA"
df$code[df$code == 'W'] <- c('start', 'end')
df$code[df$code=="NA"]<-NA
# Now with different names of start and stop sequence I was able to implement `cumsum`
library(tidyverse)
df <- df %>%
group_by(grp = cumsum(!is.na(code))) %>%
dplyr::mutate(code = replace(code, first(code) == 'start', 'W'),
code = replace(code, code=='end', 'W')) %>%
ungroup() %>%
select(-grp)
Output:
# A tibble: 23 x 3
ID elevation code
<int> <int> <chr>
1 1 150 NA
2 1 140 NA
3 1 130 W
4 1 120 W
5 1 110 W
6 1 120 W
7 1 130 W
8 1 140 NA
9 1 150 NA
10 2 90 NA
11 2 80 NA
12 2 70 NA
13 2 66 W
14 2 60 W
15 2 50 W
16 2 66 W
17 2 70 NA
18 2 72 NA
19 2 68 W
20 2 65 W
21 2 60 W
22 2 68 W
23 2 70 NA

Answer 6

This answer is similar to #Onyambu's: create an 'index' (ind) that increases by one each time a non-NA is encountered in the 'code' column. If the index value is divisible by 2 (i.e. it is an even number) insert "NA" into the new column. If the index is not divisible by 2, add a "W" into the new column. Then if there is a "W" in the 'code' or 'new' columns, replace the NA in the 'code' column with W and drop the 'new' column from the dataframe.
df %>%
mutate(ind = ifelse(cumsum(!is.na(code)) %% 2 == 0, NA, "W")) %>%
mutate(code = ifelse(ind == "W" | code == "W", "W", NA)) %>%
select(-c(ind))
#> ID elevation code
#>1 1 150 <NA>
#>2 1 140 <NA>
#>3 1 130 W
#>4 1 120 W
#>5 1 110 W
#>6 1 120 W
#>7 1 130 W
#>8 1 140 <NA>
#>9 1 150 <NA>
#>10 2 90 <NA>
#>11 2 80 <NA>
#>12 2 70 <NA>
#>13 2 66 W
#>14 2 60 W
#>15 2 50 W
#>16 2 66 W
#>17 2 70 <NA>
#>18 2 72 <NA>
#>19 2 68 W
#>20 2 65 W
#>21 2 60 W
#>22 2 68 W
#>23 2 70 <NA>

Answer 7

Though the question has been marked as solved(answer accepted) yet for further/future reference, there is a function fill_run in library runner which does exactly this.
fill_run replaces NA values if they were surrounded by pair of identical values. Since our additional requirement is to look at elevation too we can do something like this
df %>% group_by(ID) %>%
mutate(code = runner::fill_run(ifelse(!is.na(code), paste(elevation,code), code), only_within = T))
# A tibble: 23 x 3
# Groups: ID [2]
ID elevation code
<int> <int> <chr>
1 1 150 NA
2 1 140 NA
3 1 130 130 W
4 1 120 130 W
5 1 110 130 W
6 1 120 130 W
7 1 130 130 W
8 1 140 NA
9 1 150 NA
10 2 90 NA
# ... with 13 more rows
Needless to say, you can again mutate non-NA values from code to W very easily, if required.