If I understand your situation correctly, data{1:end} is a "comma-separated list" of tables, and so mean(data{1:end}) in effect calls mean like this:
t1 = array2table(rand(5,3));
t2 = array2table(rand(5,3));
try, mean(t1,t2), catch, lasterr, end
ans =
'Error using tabular/mean
Argument at position 2 cannot be of type 'table'.'
I don't know of a function in MATLAB that takes the mean across multiple inputs. You'd normally have one array and take the mean across one or more dimensions of that array. In this case, tables can only be 2-D, so that exact thing is not an option. You can of course write a loop
for i = 1:5, C{i} = array2table(rand(10,3)); end
M = M ./ length(C)
M =
Var1 Var2 Var3
_______ _______ _______
0.56204 0.38388 0.56167
0.42211 0.66402 0.37256
0.66128 0.37039 0.35738
0.61288 0.36267 0.43646
0.4515 0.54206 0.49305
0.51918 0.81702 0.57308
0.58045 0.57443 0.43987
0.66521 0.65924 0.351
0.68491 0.49471 0.73781
0.44043 0.59032 0.47729
Another possibility is to recognize that while you cannot create a 3-D table, you can get the same effect in a couple ways. Here's one: You have 31 tables, each 100x14. Let's go smaller, make it 5 tables, each 10x3. You can store that as one 10x3 table, whose variables themselves have 5 columns.
T = table(rand(10,5),rand(10,5),rand(10,5))
M = varfun(@(x)mean(x,2),T)
M =
Fun_Var1 Fun_Var2 Fun_Var3
________ ________ ________
0.49291 0.50638 0.37899
0.54882 0.47199 0.65506
0.59953 0.41482 0.56803
0.53781 0.41795 0.51371
0.30719 0.47534 0.58123
0.49272 0.68066 0.57302
0.5379 0.50542 0.62638
0.48108 0.4226 0.49564
0.61277 0.53108 0.46204
0.35273 0.47081 0.52851
That "embed multiple tables in one" storage scheme may be less convenient for whatever else you need to do, but it's worth considering. It's a reasonable scheme if your 31 tables are something like "replicate data".
