Mean of the n last or first

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A small post today that could be very useful. A colleague ask me how to compute the mean of the 5 oldest trees in a populations. The following code that I have found in stackoverflow.

library(data.table)

treeage <- data.table(age = rnorm(50,200,50), group = rep(LETTERS[1:5],each = 10)) #dataset with trees age in 5 different group


subset_mean_three <- treeage[order(rank(age), group)][,tail(.SD,3),by=group] #subset the 3 oldest trees for each unique group
group_age <- subset_mean_three [, .(age_mean= mean(age )), by = group] #Compute the mean of the tree age from the 3 oldest trees per group

merge(treeage,group_age)[1:15] #adding the mean age of the 3 oldest trees in the treeage data table

As well you can ask for the n quantile age distribution or the youngest trees ages with the following code:

#For the quantile :
subset_quantile  <- treeage[, .(quantile_age = quantile(age,0.90)),by=group] #subset the 90 qauntile age for each unique group

#For the 3 youngest trees:
subset_mean_young <- treeage[order(rank(age), group)][,head(.SD,3),by=group] #subset the 3 youngest trees for each unique group
group_age_young  <- subset_mean_young [, .(age_mean_young = mean(age )), by = group] #compute the mean of the 3 youngest trees for each groups


#merge results
Reduce(merge,list(treeage,group_age,subset_quantile,group_age_young))[1:15] #Merging all the tables together

    group      age age_mean quantile_age age_mean_young
 1:     A 173.3567 303.9826     291.0337       170.4187
 2:     A 262.4292 303.9826     291.0337       170.4187
 3:     A 233.8751 303.9826     291.0337       170.4187
 4:     A 211.6698 303.9826     291.0337       170.4187
 5:     A 193.1739 303.9826     291.0337       170.4187
 6:     A 282.6023 303.9826     291.0337       170.4187
 7:     A 144.7256 303.9826     291.0337       170.4187
 8:     A 366.9163 303.9826     291.0337       170.4187
 9:     A 201.2530 303.9826     291.0337       170.4187
10:     A 206.6334 303.9826     291.0337       170.4187
11:     B 265.5331 292.0307     279.6926       185.2296
12:     B 228.0602 292.0307     279.6926       185.2296
13:     B 200.2322 292.0307     279.6926       185.2296
14:     B 219.2227 292.0307     279.6926       185.2296
15:     B 203.7675 292.0307     279.6926       185.2296

You can also see how to merge multiple data table together using the function Reduce(merge, list(DT1, DT2, DT3, ...)) finding also in stackoverflow.