How to replace NAs in R which get mode from a group

Question

For example I have some data

x	y	Owner	H
1	2	A	1
2	5	A	NA
2	2	A	5
2	5	A	4
2	5	B	4
2	5	C	3
3	3	C	6
3	3	C	NA
3	3	D	NA

I want to replace the Nans with the most common/mode when you group by (x,y,Owner) to get

x	y	Owner	H
1	2	A	1
2	5	A	4
2	2	A	5
2	5	A	4
2	5	B	4
2	5	C	3
3	3	C	6
3	3	C	6
3	3	D	NA

I tried this but I think its replacing everything when I only want to replace the Nas new_data <- my_data %>% group_by('Owner', 'x', 'y') %>% mutate(H=mean(H, na.rm=TRUE))

I've seen this: but it doesn't workhttps://stackoverflow.com/questions/45741879/can-i-replace-nans-with-the-mode-of-a-column-in-a-grouped-data-frame

new_data <- my_data %>% group_by('Owner', 'x', 'y') %>% mutate(H=mean(H, na.rm=TRUE)

But this replaces all the values of H with the mean. Also sometimes there will be no rows which match ie the group will only have 1 row and that has an Na (or maybe that its more than one but they just have NAs), is there a way to just keep the NA?

(I want it to find all the rows which match theOwner, x and y of the row with the NA and take the mode of their H values)

Answer 1

Use the Mode function defined here and currently included in the gtools package

df %>%
   group_by(Owner, x, y) %>%
   mutate(H = replace_na(H, gtools::stat_mode(H, ties = 'first')))

# A tibble: 8 × 4
# Groups:   Owner, x, y [6]
      x     y Owner     H
  <int> <int> <chr> <int>
1     1     2 A         1
2     2     5 A         4
3     2     2 A         5
4     2     5 A         4
5     2     5 B         4
6     2     5 C         3
7     3     3 C         6
8     3     3 C         6