Infil NaN for missing years in time series

Hello:

I have two matrices, A and B. Matrix B has some years missing in it. I want to concatenate horizontally two matrices, A and B to a new matrix C, which can be filled with NaN for the missing years.

A = 
1981	0.79	1.56	0.90	1.15
1982	0.62	0.83	0.84	0.74
1983	0.81	0.71	0.71	0.70
1984	1.06	0.74	0.61	0.76
1985	1.23	0.86	0.67	0.61
1986	1.32	0.56	1.11	0.76
1987	0.75	1.06	0.56	1.15
1988	1.76	1.09	0.88	0.67
1989	0.90	0.77	0.94	0.77
1990	0.52	0.52	1.15	0.88

and B = 
1981	1.0617
1982	1.0682
1985	1.0149
1986	0.6607
1987	0.5642
1988	0.6194
1989	0.6693
1990	0.6966

Desired output, C = 

1981	1.0617	0.79	1.56	0.90	1.15	
1982	1.0682	0.62	0.83	0.84	0.74	
1983	NaN	0.81	0.71	0.71	0.70	
1984	NaN	1.06	0.74	0.61	0.76	
1985	1.0149	1.23	0.86	0.67	0.61	
1986	0.6607	1.32	0.56	1.11	0.76	
1987	0.5642	0.75	1.06	0.56	1.15	
1988	0.6194	1.76	1.09	0.88	0.67	
1989	0.6693	0.90	0.77	0.94	0.77	
1990	0.6966	0.52	0.52	1.15	0.88	

Answer 1

[Neuropragmatist]

If you don't mind converting your data to tables you can use outerjoin:

A = [1981	0.79	1.56	0.90	1.15;
1982	0.62	0.83	0.84	0.74;
1983	0.81	0.71	0.71	0.70;
1984	1.06	0.74	0.61	0.76;
1985	1.23	0.86	0.67	0.61;
1986	1.32	0.56	1.11	0.76;
1987	0.75	1.06	0.56	1.15;
1988	1.76	1.09	0.88	0.67;
1989	0.90	0.77	0.94	0.77;
1990	0.52	0.52	1.15	0.88];

B = [1981	1.0617;
1982	1.0682;
1985	1.0149;
1986	0.6607;
1987	0.5642;
1988	0.6194;
1989	0.6693;
1990	0.6966];


At = array2table(A);
Bt = array2table(B);

C = outerjoin(At,Bt,'Keys',1,'RightVariables',2);
C = C(:,[1 6 2:5])


C = 

  10×6 table

     A1       B2       A2      A3      A4      A5 
    ____    ______    ____    ____    ____    ____

    1981    1.0617    0.79    1.56     0.9    1.15
    1982    1.0682    0.62    0.83    0.84    0.74
    1983       NaN    0.81    0.71    0.71     0.7
    1984       NaN    1.06    0.74    0.61    0.76
    1985    1.0149    1.23    0.86    0.67    0.61
    1986    0.6607    1.32    0.56    1.11    0.76
    1987    0.5642    0.75    1.06    0.56    1.15
    1988    0.6194    1.76    1.09    0.88    0.67
    1989    0.6693     0.9    0.77    0.94    0.77
    1990    0.6966    0.52    0.52    1.15    0.88

[Neuropragmatist]

Or using indexing:

A = [1981	0.79	1.56	0.90	1.15;
1982	0.62	0.83	0.84	0.74;
1983	0.81	0.71	0.71	0.70;
1984	1.06	0.74	0.61	0.76;
1985	1.23	0.86	0.67	0.61;
1986	1.32	0.56	1.11	0.76;
1987	0.75	1.06	0.56	1.15;
1988	1.76	1.09	0.88	0.67;
1989	0.90	0.77	0.94	0.77;
1990	0.52	0.52	1.15	0.88];

B = [1981	1.0617;
1982	1.0682;
1985	1.0149;
1986	0.6607;
1987	0.5642;
1988	0.6194;
1989	0.6693;
1990	0.6966];

A = [A NaN(size(A(:,1)))];
[~,LOCB] = ismember(B(:,1),A(:,1));
A(LOCB(LOCB>0),6) = B(LOCB>0,2);
A = A(:,[1 6 2 3 4 5])



A =

  Columns 1 through 4

                      1981                    1.0617                      0.79                      1.56
                      1982                    1.0682                      0.62                      0.83
                      1983                       NaN                      0.81                      0.71
                      1984                       NaN                      1.06                      0.74
                      1985                    1.0149                      1.23                      0.86
                      1986                    0.6607                      1.32                      0.56
                      1987                    0.5642                      0.75                      1.06
                      1988                    0.6194                      1.76                      1.09
                      1989                    0.6693                       0.9                      0.77
                      1990                    0.6966                      0.52                      0.52

  Columns 5 through 6

                       0.9                      1.15
                      0.84                      0.74
                      0.71                       0.7
                      0.61                      0.76
                      0.67                      0.61
                      1.11                      0.76
                      0.56                      1.15
                      0.88                      0.67
                      0.94                      0.77
                      1.15                      0.88