You may have other reasons for reshaping your data, but it's easy to compute those means without doing so:
>> monthDates = datetime(2005,1,1,'Format','MM-yyyy') + calmonths(0:(12*11 - 1))';
>> storage = rand(size(t));
>> t = timetable(monthDates,storage)
t =
132×1 timetable
monthDates storage
__________ ________
01-2005 0.64912
02-2005 0.73172
03-2005 0.64775
04-2005 0.45092
05-2005 0.54701
06-2005 0.29632
07-2005 0.74469
[snip]
If you put one more variable in that timetable ...
>> t.monthOfYear = categorical(month(t.monthDates,'shortname'))
t =
132×2 timetable
monthDates storage monthOfYear
__________ ________ ___________
01-2005 0.64912 Jan
02-2005 0.73172 Feb
03-2005 0.64775 Mar
04-2005 0.45092 Apr
05-2005 0.54701 May
06-2005 0.29632 Jun
07-2005 0.74469 Jul
[snip]
... then computing the monthly means is one line:
>> monthlyMeans = varfun(@mean,t,'InputVariables','storage','GroupingVariables','monthOfYear','OutputFormat','table')
monthlyMeans =
12×3 table
monthOfYear GroupCount mean_storage
___________ __________ ____________
Apr 11 0.55913
Aug 11 0.32596
Dec 11 0.51905
Feb 11 0.56055
Jan 11 0.5817
Jul 11 0.49449
Jun 11 0.41813
Mar 11 0.7096
May 11 0.39363
Nov 11 0.43788
Oct 11 0.51204
Sep 11 0.53772
Note that the retime function also supports computing "monthly means", but in a different sense: compute the mean value within each month within each year. That would be appropriate for something like aggregating daily data, but in your case you want to compute means across years, so retime is not the right way.
But to answer your original question, add one more variable to your data, convert to a table, and unstack:
>> t.year = year(t.monthDates)
t =
132×3 timetable
monthDates storage monthOfYear year
__________ ________ ___________ ____
01-2005 0.64912 Jan 2005
02-2005 0.73172 Feb 2005
03-2005 0.64775 Mar 2005
04-2005 0.45092 Apr 2005
05-2005 0.54701 May 2005
06-2005 0.29632 Jun 2005
07-2005 0.74469 Jul 2005
[snip]
>> t = timetable2table(t,'ConvertRowTimes',false)
t =
132×3 table
storage monthOfYear year
________ ___________ ____
0.64912 Jan 2005
0.73172 Feb 2005
0.64775 Mar 2005
0.45092 Apr 2005
0.54701 May 2005
0.29632 Jun 2005
0.74469 Jul 2005
[snip]
>> unstack(t,'storage','year','GroupingVariable','monthOfYear')
Warning: Table variable names were modified to make them valid MATLAB identifiers.
ans =
12×12 table
monthOfYear x2005 x2006 x2007 x2008 x2009 x2010 x2011 x2012 x2013 x2014 x2015
___________ _______ ________ _______ _______ _______ ________ _______ ________ ________ ________ ________
Jan 0.64912 0.78023 0.64432 0.47092 0.90488 0.29668 0.23728 0.037739 0.49417 0.90472 0.97868
Feb 0.73172 0.081126 0.37861 0.23049 0.97975 0.31878 0.45885 0.88517 0.77905 0.60987 0.71269
Mar 0.64775 0.92939 0.81158 0.84431 0.43887 0.42417 0.96309 0.91329 0.71504 0.61767 0.50047
Apr 0.45092 0.77571 0.53283 0.19476 0.11112 0.50786 0.54681 0.79618 0.90372 0.85944 0.47109
May 0.54701 0.48679 0.35073 0.22592 0.25806 0.085516 0.52114 0.098712 0.89092 0.80549 0.059619
Jun 0.29632 0.43586 0.939 0.17071 0.40872 0.26248 0.23159 0.26187 0.33416 0.57672 0.68197
Jul 0.74469 0.44678 0.87594 0.22766 0.5949 0.80101 0.4889 0.33536 0.69875 0.18292 0.042431
Aug 0.18896 0.30635 0.55016 0.4357 0.26221 0.02922 0.62406 0.67973 0.19781 0.23993 0.071445
Sep 0.68678 0.50851 0.62248 0.3111 0.60284 0.92885 0.67914 0.13655 0.030541 0.88651 0.52165
Oct 0.18351 0.51077 0.58704 0.92338 0.71122 0.73033 0.39552 0.72123 0.74407 0.028674 0.09673
Nov 0.36848 0.81763 0.20774 0.43021 0.22175 0.48861 0.36744 0.10676 0.50002 0.4899 0.81815
Dec 0.62562 0.79483 0.30125 0.18482 0.11742 0.57853 0.98798 0.65376 0.47992 0.16793 0.81755
Or
>> unstack(t,'storage','monthOfYear','GroupingVariable','year')
ans =
11×13 table
year Apr Aug Dec Feb Jan Jul Jun Mar May Nov Oct Sep
____ _______ ________ _______ ________ ________ ________ _______ _______ ________ _______ ________ ________
2005 0.45092 0.18896 0.62562 0.73172 0.64912 0.74469 0.29632 0.64775 0.54701 0.36848 0.18351 0.68678
2006 0.77571 0.30635 0.79483 0.081126 0.78023 0.44678 0.43586 0.92939 0.48679 0.81763 0.51077 0.50851
2007 0.53283 0.55016 0.30125 0.37861 0.64432 0.87594 0.939 0.81158 0.35073 0.20774 0.58704 0.62248
2008 0.19476 0.4357 0.18482 0.23049 0.47092 0.22766 0.17071 0.84431 0.22592 0.43021 0.92338 0.3111
2009 0.11112 0.26221 0.11742 0.97975 0.90488 0.5949 0.40872 0.43887 0.25806 0.22175 0.71122 0.60284
2010 0.50786 0.02922 0.57853 0.31878 0.29668 0.80101 0.26248 0.42417 0.085516 0.48861 0.73033 0.92885
2011 0.54681 0.62406 0.98798 0.45885 0.23728 0.4889 0.23159 0.96309 0.52114 0.36744 0.39552 0.67914
2012 0.79618 0.67973 0.65376 0.88517 0.037739 0.33536 0.26187 0.91329 0.098712 0.10676 0.72123 0.13655
2013 0.90372 0.19781 0.47992 0.77905 0.49417 0.69875 0.33416 0.71504 0.89092 0.50002 0.74407 0.030541
2014 0.85944 0.23993 0.16793 0.60987 0.90472 0.18292 0.57672 0.61767 0.80549 0.4899 0.028674 0.88651
2015 0.47109 0.071445 0.81755 0.71269 0.97868 0.042431 0.68197 0.50047 0.059619 0.81815 0.09673 0.52165
