I have a table in R that looks like this:
"Dimension","Config","Result"
"3","1","6.43547800901942e-12"
"3","1","3.10671396584125e-15"
"3","1","5.86997050075184e-07"
"3","2","1.57865350726808"
"3","2","0.125293574811717"
"3","2","0.096173751923243"
"4","1","3.33845065295529e-08"
"4","1","4.57511389653726e-07"
"4","1","2.58918409465438e-07"
"4","2","3.23375251723051"
"4","2","2.13142950121767"
"4","2","0.510008166587752"
As it can be observed, I always have 6 values for each dimension, and for each dimension I have 3 values for config 1 and 3 values for config 2. Is it possible to "double aggregate" this table so it would output the means for config 1 for each dimension and the mean for config 2 for each dimension as well?
If I use this comand line:
a <- aggregate(d[,3], list(d$Dimension), mean)
I get this result:
Group.1 x
1 3 3.000202e-01
2 4 9.791985e-01
But I want something like this:
Group.1 Config x
1 3 1 <mean value for this row>
2 3 2 <mean value for this row>
3 4 1 <mean value for this row>
4 4 2 <mean value for this row>