Calculate monthly averages without reshaping

i have a time series of data and i would like to calculate monthly averages but keeping the vector lengths the same. (the variables also contain many nans)

For example if i have a date vector (yyyymm):

date=
199901
199901
199901
199905
199905
200107
200107
200107

variable=
1
2
3
4
5
5
6
7

How can i get

variable_montlyav=
2
2
2
4.5
4.5
6
nan (or 6 i dont mind)
6

so that i can use my new montly averaged variable with some existing codes i have.

[Stephen23]

What exactly do you mean by a "date vector"? In MATLAB the term "date vector" has a specific meaning, which is a numeric vector consisting |y,m,d,H,M,S.F] values. If your data really was a date vector, it would looks like this:

        1999           1           1           0           0           0
        1999           1           1           0           0           0
        1999           1           1           0           0           0
        1999           5           1           0           0           0
        1999           5           1           0           0           0
        2001           7           1           0           0           0
        2001           7           1           0           0           0
        2001           7           1           0           0           0

Answer 1

[Stephen23]

Note: this code works if date is a cell array of strings, and also if date is a numeric vector of values representing year and month.

date = {'199901','199901','199901','199905','199905','200107','200107','200107'};
variable = [1,2,3,4,5,5,6,7];
[~,~,idx] = unique(date);
tmp = accumarray(idx(:), variable, [], @mean);
tmp(idx)

creates this output:

ans =
    2.0000
    2.0000
    2.0000
    4.5000
    4.5000
    6.0000
    6.0000
    6.0000

[aaron Harvey]

worked perfectly! i dunno how it works but it does :)

Answer 2

[Roger Wohlwend]

A = accumarray(date, variable, [], @mean);
MeanValues = A(date);

[Stephen23]

This is not a very memory-efficient solution, as it will create temporary arrays with more than 200000 elements in them. See my answer for a more memory-efficient solution.