Grouping data by value

Hi. I have 432x1 data. I need to grouping the data.

for example;

-0.989088822111364
-1.16881053159657
-0.687960193675098
0.609028518151482
0.853112207247799
1.09867863373770
0.325581735572437
-0.358624505229866
-0.116184636997357
-1.12249570113502
-0.878711918410750
-0.843582079603111
-0.763107095944730
-0.963235098445007
-1.36135857090159
-1.93513836539865
-1.53179202986538
-1.24445835422832
0.643855927191538
0.506078478817649
1.10211477120999
0.119654919704736
-0.186087379373470
0.208608204079503
0.542441939121425
1.09907331469601
0.595582967612814
0.605674642059506
-0.0429774937224157

How can I assign them to groups such as:

moderate= -1.00 to -1.49

severe= -1.50 to -1.99

Answer 1

[KSSV]

Let x be your data. Create arespective y-value which denotes grouping.

y = zeros(size(x)) ; 
% get values  -1.00 to -1.49
idx = x >= -1 && x < -1.49 ;
y(idx) = 1 ;  % say one stand for moderate 
% get values between -1.50 and -1.99
idx = x >= -1.50 && x < -1.99 ;
y(idx) = 2 ;  % say two stand for 

Answer 2

[Walter Roberson]

discretize() with boundaries and 'categorical' and a list of category names.

data = [
-0.989088822111364
-1.16881053159657
-0.687960193675098
0.609028518151482
0.853112207247799
1.09867863373770
0.325581735572437
-0.358624505229866
-0.116184636997357
-1.12249570113502
-0.878711918410750
-0.843582079603111
-0.763107095944730
-0.963235098445007
-1.36135857090159
-1.93513836539865
-1.53179202986538
-1.24445835422832
0.643855927191538
0.506078478817649
1.10211477120999
0.119654919704736
-0.186087379373470
0.208608204079503
0.542441939121425
1.09907331469601
0.595582967612814
0.605674642059506
-0.0429774937224157];

cats = discretize(data, [-1.99, -1.50, -1, 1, 1.5, 1.99], 'categorical', {'severe', 'moderate', 'nomimal', 'good', 'excellent'})

cats = 29×1 categorical array
     nomimal 
     moderate 
     nomimal 
     nomimal 
     nomimal 
     good 
     nomimal 
     nomimal 
     nomimal 
     moderate 
     nomimal 
     nomimal 
     nomimal 
     nomimal 
     moderate 
     severe 
     severe 
     moderate 
     nomimal 
     nomimal 
     good 
     nomimal 
     nomimal 
     nomimal 
     nomimal 
     good 
     nomimal 
     nomimal 
     nomimal