How to Calculate Industry Medians with Own Firm Excluded - r

Question

I need to create a new column with the median ETR variable within a certain industry (SIC) for a sample of firms.
However, I need to exclude the own firm before calculating the industry (SIC) median for ETR.
Does anyone have any suggestions on how I could accomplish this?
Any help would be appreciated.
Thank you!
Sample Data:
Firm SIC ETR
1 20 10
2 20 15
3 20 20
4 20 25
5 20 30
6 21 50
7 21 55
8 21 60
9 21 65
10 21 70
Should Become:
Firm SIC ETR ETR_Median
1 20 10 22.5
2 20 15 22.5
3 20 20 20
4 20 25 17.5
5 20 30 17.5
6 21 50 62.5
7 21 55 62.5
8 21 60 60
9 21 65 57.5
10 21 70 57.5
So, firm #4, for example, have an industry (SIC) median of 17.5 when only considering the other firms in the same industry (SIC).

Answer 1

Consider splitting by SIC groups and run across all its Firm values to exclude from median calculation. Specifically, using:
by (for grouping into subset dfs)
sapply (to iterate across Firm values and call median)
unlist (to convert list to vector for df column binding)
Altogether:
df$ETR_median <- unlist(by(df, df$SIC, function(sub)
sapply(sub$Firm, function(f) median(sub$ETR[sub$Firm != f]))
))
df
# Firm SIC ETR ETR_median
# 1 1 20 10 22.5
# 2 2 20 15 22.5
# 3 3 20 20 20.0
# 4 4 20 25 17.5
# 5 5 20 30 17.5
# 6 6 21 50 62.5
# 7 7 21 55 62.5
# 8 8 21 60 60.0
# 9 9 21 65 57.5
# 10 10 21 70 57.5

Answer 2

You could create a function that excludes the current observation before conducting the median calculation:
median_excl <- function(x){
# pre-allocate our result vector:
med_excl <- vector(length = length(x))
# loop through our vector, excluding the current index and taking the median:
for(i in seq_along(x)){
x_excl <- x[-i]
med <- median(x_excl)
med_excl[i] <- med
}
return(med_excl)
}
Then simply apply it using dplyr or however you chose:
df %>% group_by(SIC) %>% mutate(ETR_Median = median_excl(ETR))
# Firm SIC ETR ETR_median
# 1 1 20 10 22.5
# 2 2 20 15 22.5
# 3 3 20 20 20.0
# 4 4 20 25 17.5
# 5 5 20 30 17.5
# 6 6 21 50 62.5
# 7 7 21 55 62.5
# 8 8 21 60 60.0
# 9 9 21 65 57.5
# 10 10 21 70 57.5