>> d = dataset({1,'name1'})
d =
    name1
    1
>> e = dataset({2,'name2'})
e =
    name2
    2

(These are 1×1 to make things short.)

Now assign e *into an existing subset* of d.

>> d(1,1) = e
d =
    name1
    2

Notice that d’s names don’t change. That’s intentional — it only assigns values. You were doing something more like this:

>> d(:,2) = e
d =
    name1    Var2
    2        2

Even here, e’s name hasn’t carried over, because the same rule applies, for consistency — the names are not propagated from the RHS to the LHS if you *assign into*. So how to do what (I think) you want? You can explicitly specify a new name (even multiple names) as part of the assignment:

>> d(:,'name2') = e
d =
    name1    name2
    1        2

Or you can concatenate:

>> f = [d e]
f =
    name1    name2
    1        2

Both create the name you want as part of the assignment.

If you want to change the name of an existing variable in a dataset array, you can assign directly to the name:

>> d.Properties.VarNames{2} = 'name2'
d =
    name1    name2
    1        2    

The list of variable names is a cell array of strings, so you can assign to them all, or one, or even a single character of one. There’s also a SET method, similar to what you’d do with Handle Graphics.

It’s apparent that the documentation was not sufficiently clear here, I’ll make a note to have that looked into. In the mean time, I hope this helps.

There doesn’t seem to be a way to change individual or subsets of VarNames in a dataset.

You can create a dataset as documented

d(1,2) = dataset({2,'name2'})

d =
    name1
    1

but if you try to add to it in an intuitive fashion the VarName specified is not used:

d(1,2) = dataset({2,'name2'})
d =
    name1    Var2
    1        2

futhermore this doesn’t work either

d = dataset({1,'name1'},{2,'name2'})
d(1,2)   =dataset('VarNames',{'name3'})
??? Error using ==> setvarnames at 21
NEWNAMES must have one name for each variable in A.

Error in ==> dataset.dataset>dataset.dataset at 274

is there any way to specify a 1 or subset of Varnames.

At first glance, it really looks like the basic data type in ‘R statistics language’. Though R language is (in my personal opinion) not modern at all and unintuitive, it has a great advantage on handling data. It is mainly because of its data structure, which is almost identical to ‘dataset’ introduced in this blog.

Another advantage of R is its huge user base (most of them are previous users of SAS or SPSS), and their contribution to R with so many tools.

I don’t want R’s not-modern programming style to creep into MATLAB’s statistical toolbox, but I really hope MATLAB can deal with essential features of R language. ‘dataset’ looks a very good thing to be added. The next thing I want from MATLAB is to provide broad range of essential functions that properly deal ‘NaN’, which is a missing data. If most functions of statistical toolbox has its ‘NaN’ version, it will be great help to most statistician.

The way to do that is more or less just as with any other array: create variables using, for example, ZEROS, and create a dataset array from those. Then overwrite each row as you get the real data.

For categorical variables, it’s hard to say what your code should look like without knowing what data you are converting, but it will definitely be advantageous to “pre-define” the levels you care about, using the third input to the NOMINAL/ORDINAL constructor.

Hope this helps.

I am trying to store both numerical and categorical values to a dataset array. As the data size is big, I wan to initialize the dataset array so that I can speed up the operation. How can I do that?

Many thanks!

>> load hospital % sample file in Statistics Toolbox
>> hospital.Properties.Units
ans =
” ” ‘Yrs’ ‘Lbs’ ” ‘mm Hg’ ‘Counts’
>> hospital.Weight = hospital.Weight/2.2;
>> hospital.Properties.Units{4} = ‘kg’;

The units also show up in the description of each dataset variable if you use the summary method on a dataset array. Note that these are just for the purpose of labelling, there is not any provision for conversions or units checking in math or anything like that.

I’m curious about your comment about large datasets. Certainly if you have data that are homogeneous, you are better off using a matrix. But if your columns have different types, the dataset array is every bit as efficient as a scalar structure, and a good deal more efficient (memory-wise) than a structure array that has one element for every row of your data. It has the benefit over a scalar structure that you can easily subscript across dataset variables, which correspond to fields for the scalar atructure solution. And it allows you to use names for both dataset variables and observations.

Is there any way to associate units with a column in the data set?