How to unnest and reorganize a complex nested list to run kmeans

I have a list containing multiple lists of dataframes. In the Rviewer, here is an example of what you see:

image1

note: dataframes B-F has the same named variables.

My questions are:

1. Is there a simple way to transpose the information so that this will be one table so that:

the A:F will become character values under a new variable (e.g., "alphabet") and all of the nested variables will be combined so there aren't any duplicated variable names? For instance, List 1 would be broken up into: (please click link for img ->)

image2

note: all of the variables would be filled, I just left it blank here.

I'm trying to do this to run kmeans specifically on three variables, bm1, bm2, and ls which are in the sample code below.

2. And after doing this, is there a simple way to revert it back to its original structure with some additional variables (e.g., clusters)?

Here is the dput(data) for the example code:

list(A = structure(list(r = c(0, 0, 0, 0, 0, 0), x = c(4300, 
4800, 5300, 4300, 4800, 5300), y = c(4400, 4400, 4400, 4800, 
4800, 4800), fm1 = c(3800, 4400, 5000, 3600, 4200, 5200), fm2 = 
c(3900, 
4600, 5300, 3900, 4400, 5600), bm1 = c(400, 400, 400, 400, 400, 
400), bm2 = c(300, 300, 400, 300, 300, 400), ns = c(3600, 4200, 
4900, 3600, 4100, 5200), sn = c(0, 0, 0, 0, 0, 0), ls = c(0, 
0, 0, 0, 0, 0), fa = c(0, 0, 0, 0, 0, 0), sln = c(0, 0, 0, 0, 
0, 0)), row.names = c(NA, 6L), class = "data.frame"), B = 
structure(list(
r = c(0, 0, 0, 0, 0, 0), x = c(4300, 4800, 5300, 4300, 4800, 
5300), y = c(4500, 4500, 4500, 4900, 4900, 4900), fm1 = c(1300, 
1400, 1500, 1100, 1200, 1200), fm2 = c(1400, 1500, 1500, 
1200, 1300, 1300), bm1 = c(100, 100, 100, 100, 100, 100), 
bm2 = c(100, 100, 100, 100, 100, 100), ns = c(1200, 1400, 
1400, 1100, 1100, 1200), sn = c(0, 0, 100, 100, 0, 100), 
ls = c(0, 0, 0, 0, 0, 0), fa = c(0, 0, 0, 0, 0, 0), sln = c(0, 
0, 0, 0, 0, 0)), row.names = c(NA, 6L), class = "data.frame"), 
C = structure(list(r = c(0, 0, 0, 0, 0, 0), x = c(4300, 4800, 
5300, 4300, 4800, 5300), y = c(4400, 4400, 4400, 4800, 4800, 
4800), fm1 = c(4100, 4400, 4600, 3700, 4100, 3900), fm2 = c(4400, 
4600, 4900, 4000, 4400, 4300), bm1 = c(200, 200, 200, 200, 
200, 200), bm2 = c(200, 200, 200, 200, 200, 200), ns = c(4200, 
4500, 4700, 3800, 4200, 4100), sn = c(0, 100, 100, 0, 0, 
200), ls = c(0, 0, 0, 0, 0, 0), fa = c(0, 0, 0, 0, 0, 0), 
sln = c(0, 0, 0, 0, 0, 0)), row.names = c(NA, 6L), class = 
"data.frame"), 
D = structure(list(r = c(0, 0, 0, 0, 0, 0), x = c(4400, 4900, 
5400, 4400, 4900, 5400), y = c(4500, 4500, 4500, 4900, 4900, 
4900), fm1 = c(3000, 3200, 3300, 2500, 2600, 2600), fm2 = c(3400, 
3600, 3600, 2700, 2900, 2900), bm1 = c(300, 300, 300, 300, 
300, 200), bm2 = c(300, 200, 200, 200, 200, 200), ns = c(3100, 
3400, 3400, 2500, 2700, 2700), sn = c(0, 0, 0, 0, 0, 0), 
ls = c(0, 0, 0, 0, 0, 0), fa = c(0, 0, 0, 0, 0, 0), sln = c(0, 
0, 0, 0, 0, 0)), row.names = c(NA, 6L), class = "data.frame"), 
E = structure(list(r = c(0, 0, 0, 0, 0, 0), x = c(4400, 4900, 
5400, 4400, 4900, 5400), y = c(4500, 4500, 4500, 4900, 4900, 
4900), fm1 = c(2500, 2300, 2400, 2700, 2400, 2300), fm2 = c(2600, 
2400, 2600, 2900, 2600, 2500), bm1 = c(200, 200, 200, 200, 
200, 200), bm2 = c(200, 200, 200, 200, 200, 200), ns = c(2400, 
2200, 2400, 2700, 2400, 2300), sn = c(0, 100, 100, 0, 100, 
100), ls = c(0, 0, 0, 0, 0, 0), fa = c(0, 0, 0, 0, 0, 0), 
sln = c(0, 0, 0, 0, 0, 0)), row.names = c(NA, 6L), class = 
"data.frame"), 
F = structure(list(r = c(0, 0, 0, 0, 0, 0), x = c(4300, 4800, 
5300, 4300, 4800, 5300), y = c(4400, 4400, 4400, 4800, 4800, 
4800), fm1 = c(3300, 3500, 3400, 2700, 3100, 3100), fm2 = c(3500, 
3700, 3700, 2900, 3300, 3400), bm1 = c(200, 200, 200, 200, 
200, 200), bm2 = c(200, 200, 200, 200, 200, 200), ns = c(3300, 
3600, 3500, 2700, 3100, 3200), sn = c(0, 100, 100, 0, 0, 
0), ls = c(0, 0, 0, 0, 0, 0), fa = c(0, 0, 0, 0, 0, 0), sln = c(0, 
0, 0, 0, 0, 0)), row.names = c(NA, 6L), class = "data.frame"))