This example shows how to create a categorical array. `categorical`

is a data type for storing data with values from a finite set of discrete categories. These categories can have a natural order, but it is not required. A categorical array provides efficient storage and convenient manipulation of data, while also maintaining meaningful names for the values. Categorical arrays are often used in a table to define groups of rows.

By default, categorical arrays contain categories that have no mathematical ordering. For example, the discrete set of pet categories `{'dog' 'cat' 'bird'}`

has no meaningful mathematical ordering, so MATLAB® uses the alphabetical ordering `{'bird' 'cat' 'dog'}`

. *Ordinal* categorical arrays contain categories that have a meaningful mathematical ordering. For example, the discrete set of size categories `{'small', 'medium', 'large'}`

has the mathematical ordering `small < medium < large`

.

You can use the `categorical`

function to create a categorical array from a numeric array, logical array, cell array of character vectors, or an existing categorical array.

Create a 1-by-11 cell array of character vectors containing state names from New England.

state = {'MA','ME','CT','VT','ME','NH','VT','MA','NH','CT','RI'};

Convert the cell array, `state`

, to a categorical array that has no mathematical order.

state = categorical(state) class(state)

state = Columns 1 through 9 MA ME CT VT ME NH VT MA NH Columns 10 through 11 CT RI ans = categorical

List the discrete categories in the variable `state`

.

categories(state)

ans = 'CT' 'MA' 'ME' 'NH' 'RI' 'VT'

The categories are listed in alphabetical order.

Create a 1-by-8 cell array of character vectors containing the sizes of eight objects.

AllSizes = {'medium','large','small','small','medium',... 'large','medium','small'};

The cell array, `AllSizes`

, has three distinct values: `'large'`

, `'medium'`

, and `'small'`

. With the cell array of character vectors, there is no convenient way to indicate that `small < medium < large`

.

Convert the cell array, `AllSizes`

, to an ordinal categorical array. Use `valueset`

to specify the values `small`

, `medium`

, and `large`

, which define the categories. For an ordinal categorical array, the first category specified is the smallest and the last category is the largest.

valueset = {'small','medium','large'}; sizeOrd = categorical(AllSizes,valueset,'Ordinal',true) class(sizeOrd)

sizeOrd = Columns 1 through 6 medium large small small medium large Columns 7 through 8 medium small ans = categorical

The order of the values in the categorical array, `sizeOrd`

, remains unchanged.

List the discrete categories in the categorical variable, `sizeOrd`

.

categories(sizeOrd)

ans = 'small' 'medium' 'large'

The categories are listed in the specified order to match the mathematical ordering `small < medium < large`

.

Create a vector of 100 random numbers between zero and 50.

x = rand(100,1)*50;

Use the `discretize`

function to create a categorical array by binning the values of `x`

. Put all values between zero and 15 in the first bin, all the values between 15 and 35 in the second bin, and all the values between 35 and 50 in the third bin. Each bin includes the left endpoint, but does not include the right endpoint.

catnames = {'small','medium','large'}; binnedData = discretize(x,[0 15 35 50],'categorical',catnames);

`binnedData`

is a 100-by-1 ordinal categorical array with three categories, such that `small < medium < large`

.

Use the `summary`

function to print the number of elements in each category.

summary(binnedData)

small 30 medium 35 large 35

`categorical`

| `categories`

| `discretize`

| `summary`

- Convert Table Variables Containing Character Vectors to Categorical
- Access Data Using Categorical Arrays
- Compare Categorical Array Elements

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