Principal component analysis (princomp, principal, etc.) on a 3D array - r

Question

I have used PCA on 2D arrays before, and I use the first PC score vector that best best describes the variance of all the other columns in analyses. Below is a R example that shows the Comp.1 vector that would best describe the variance of the 2D array of interest.
data <- array(data=sample(12), c(4,3))
data
[,1] [,2] [,3]
[1,] 11 2 12
[2,] 4 3 10
[3,] 8 7 1
[4,] 6 9 5
output=princomp(data)
output$scores
Comp.1 Comp.2 Comp.3
[1,] 6.422813 2.865390 0.4025040
[2,] 3.251842 -3.617633 -0.9814571
[3,] -5.856500 1.848419 -1.3819379
[4,] -3.818155 -1.096176 1.9608909
My question is how can I do this same procedure on a 3D array? For example, if I have an array that the size is 4 x 5 x 3 how could I get the 4 x 5 2D array that is equivalent to the Comp.1 vector found above?
I have provided an R example below with code and outputs. When I look at the scores it only outputs one component (not 3 as expected), and the length is 60. Does that mean that the first 20 elements correspond to the first PC, the next 20 to the 2nd PC, and the last 20 to the 3rd PC? If so how does princomp arrange the entries, so I can get back to the original 4 x 5 2D array using the first 20 elements (1st PC)? Thank you for your assistance.
data=array(data=sample(48), c(4,5,3))
data
, , 1
[,1] [,2] [,3] [,4] [,5]
[1,] 47 21 45 41 34
[2,] 1 16 32 31 37
[3,] 39 8 35 10 6
[4,] 48 14 25 3 11
, , 2
[,1] [,2] [,3] [,4] [,5]
[1,] 12 43 15 36 23
[2,] 17 4 7 26 46
[3,] 2 13 33 20 40
[4,] 18 19 28 44 38
, , 3
[,1] [,2] [,3] [,4] [,5]
[1,] 42 24 47 21 45
[2,] 5 22 1 16 32
[3,] 30 29 39 8 35
[4,] 27 9 48 14 25
output=princomp(data)
output$scores
Comp.1
[1,] 21.8833333
[2,] -24.1166667
[3,] 13.8833333
[4,] 22.8833333
[5,] -4.1166667
[6,] -9.1166667
[7,] -17.1166667
[8,] -11.1166667
[9,] 19.8833333
[10,] 6.8833333
[11,] 9.8833333
[12,] -0.1166667
[13,] 15.8833333
[14,] 5.8833333
[15,] -15.1166667
[16,] -22.1166667
[17,] 8.8833333
[18,] 11.8833333
[19,] -19.1166667
[20,] -14.1166667
[21,] -13.1166667
[22,] -8.1166667
[23,] -23.1166667
[24,] -7.1166667
[25,] 17.8833333
[26,] -21.1166667
[27,] -12.1166667
[28,] -6.1166667
[29,] -10.1166667
[30,] -18.1166667
[31,] 7.8833333
[32,] 2.8833333
[33,] 10.8833333
[34,] 0.8833333
[35,] -5.1166667
[36,] 18.8833333
[37,] -2.1166667
[38,] 20.8833333
[39,] 14.8833333
[40,] 12.8833333
[41,] 16.8833333
[42,] -20.1166667
[43,] 4.8833333
[44,] 1.8833333
[45,] -1.1166667
[46,] -3.1166667
[47,] 3.8833333
[48,] -16.1166667
[49,] 21.8833333
[50,] -24.1166667
[51,] 13.8833333
[52,] 22.8833333
[53,] -4.1166667
[54,] -9.1166667
[55,] -17.1166667
[56,] -11.1166667
[57,] 19.8833333
[58,] 6.8833333
[59,] 9.8833333
[60,] -0.1166667