R - How to run average & max on different data.table columns based on multiple factors & return original colnames

In a similar fashion as @David Arenburg, but using .SDcols in order to simplify the notation. Also I show the code until the merge.

DTaves <- DT[, lapply(.SD, mean), .SDcols = VarToAve, by = c(GrpVar1, GrpVar2)]
DTmaxs <- DT[, lapply(.SD, max), .SDcols = VarToMax, by = c(GrpVar1, GrpVar2)]
merge(DTmaxs, DTaves)
##    a e b c          d
## 1: A Y 6 4  0.2230091
## 2: B Z 7 6  0.5909434
## 3: C Z 8 7 -0.4828223
## 4: D Z 9 8 -1.3591240

Alternatively, you can do this in one go by subsetting the .SD using the .. notation to look for VarToAve in the parent frame of .SD (as opposed to a column named VarToAve)

DT[, c(lapply(.SD[, ..VarToAve], mean), 
       lapply(.SD[, ..VarToMax], max)), 
   by = c(GrpVar1, GrpVar2)]
##    a e c          d b
## 1: A Y 4  0.2230091 6
## 2: B Z 6  0.5909434 7
## 3: C Z 7 -0.4828223 8
## 4: D Z 8 -1.3591240 9

Here's my humble attempt

DT[, as.list(c(setNames(max(get(VarToMax)), VarToMax), 
               lapply(.SD[, ..VarToAve], mean))), 
     c(GrpVar1, GrpVar2)]    
#    a e b c          d
# 1: A Y 6 4 -0.8000173
# 2: B Z 7 6  0.2508633
# 3: C Z 8 7  1.1966517
# 4: D Z 9 8  1.7291615

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Or, for maximum efficiency you could use colMeans and eval(as.name()) combination instead of lapply and get

DT[, as.list(c(setNames(max(eval(as.name(VarToMax))), VarToMax), 
             colMeans(.SD[, ..VarToAve]))), 
     c(GrpVar1, GrpVar2)]   
#    a e b c          d
# 1: A Y 6 4 -0.8000173
# 2: B Z 7 6  0.2508633
# 3: C Z 8 7  1.1966517
# 4: D Z 9 8  1.7291615