how to replace missing values with previous year's binned mean - r

Question

I have a data frame as below
p1_bin and f1_bin are calculated by cut function by me with
Bins <- function(x) cut(x, breaks = c(0, seq(1, 1000, by = 5)), labels = 1:200)
binned <- as.data.frame (sapply(df[,-1], Bins))
colnames(binned) <- paste("Bin", colnames(binned), sep = "_")
df<- cbind(df, binned)
Now how to calculate mean/avg for previous two years and replace in NA values with in that bin
for example : at row-5 value is NA for p1 and f1 is 30 with corresponding bin 7.. now replace NA with previous 2 years mean for same bin (7) ,i.e
df
ID year p1 f1 Bin_p1 Bin_f1
1 2013 20 30 5 7
2 2013 24 29 5 7
3 2014 10 16 2 3
4 2014 11 17 2 3
5 2015 NA 30 NA 7
6 2016 10 NA 2 NA
df1
ID year p1 f1 Bin_p1 Bin_f1
1 2013 20 30 5 7
2 2013 24 29 5 7
3 2014 10 16 2 3
4 2014 11 17 2 3
5 2015 **22** 30 NA 7
6 2016 10 **16.5** 2 NA
Thanks in advance

Answer 1

I believe the following code produces the desired output. There's probably a much more elegant way than using mean(rev(lag(f1))[1:2]) to get the average of the last two values of f1 but this should do the trick anyway.
library(dplyr)
df %>%
arrange(year) %>%
mutate_at(c("p1", "f1"), "as.double") %>%
group_by(Bin_p1) %>%
mutate(f1 = ifelse(is.na(f1), mean(rev(lag(f1))[1:2]), f1)) %>%
group_by(Bin_f1) %>%
mutate(p1 = ifelse(is.na(p1), mean(rev(lag(p1))[1:2]), p1)) %>%
ungroup
and the output is:
# A tibble: 6 x 6
ID year p1 f1 Bin_p1 Bin_f1
<int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 2013 20 30.0 5 7
2 2 2013 24 29.0 5 7
3 3 2014 10 16.0 2 3
4 4 2014 11 17.0 2 3
5 5 2015 22 30.0 NA 7
6 6 2016 10 16.5 2 NA