Data Types

Grouping variables, categorical data, and dataset arrays

Statistics and Machine Learning Toolbox™ provides two additional data types. Work with ordered and unordered discrete, nonnumeric data using the nominal and ordinal data types. Store multiple variables, including those with different data types, into a single object using the dataset array data type. However, these data types are unique to Statistics and Machine Learning Toolbox. For greater cross-product compatibility, use the categorical or table data types, respectively, available in MATLAB^®. For more information see Create Categorical Arrays, Create Tables and Assign Data to Them, or watch Tables and Categorical Arrays.

Functions

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Categorical Data

`nominal`	(Not Recommended) Arrays for nominal data
`ordinal`	(Not Recommended) Arrays for ordinal data
`dummyvar`	Create dummy variables
`onehotencode`	Encode data labels into one-hot vectors
`onehotdecode`	Decode probability vectors into class labels
`gplotmatrix`	Matrix of scatter plots by group
`grp2idx`	Create index vector from grouping variable
`gscatter`	Scatter plot by group

Dataset Arrays

`mat2dataset`	(Not Recommended) Convert matrix to dataset array
`cell2dataset`	(Not Recommended) Convert cell array to dataset array
`struct2dataset`	(Not Recommended) Convert structure array to dataset array
`table2dataset`	(Not Recommended) Convert table to dataset array
`dataset2cell`	(Not Recommended) Convert dataset array to cell array
`dataset2struct`	(Not Recommended) Convert dataset array to structure
`dataset2table`	Convert dataset array to table
`export`	(Not Recommended) Write dataset array to file
`ismissing`	(Not Recommended) Find dataset array elements with missing values
`join`	(Not Recommended) Merge dataset array observations

Classes

dataset (Not Recommended) Arrays for statistical data

Topics

Categorical Data

Nominal and Ordinal Arrays
Nominal and ordinal arrays store data that have a finite set of discrete levels, which might or might not have a natural order.
Advantages of Using Nominal and Ordinal Arrays
Easily manipulate category levels, carry out statistical analysis, and reduce memory requirements.
Grouping Variables
Grouping variables are utility variables used to group or categorize observations.
Dummy Variables
Dummy variables let you adapt categorical data for use in classification and regression analysis.
Other MATLAB Functions Supporting Nominal and Ordinal Arrays
Learn about MATLAB functions that support nominal and ordinal arrays.
Create Nominal and Ordinal Arrays
Create nominal and ordinal arrays using nominal and ordinal, respectively.
Categorize Numeric Data
Categorize numeric data into a categorical ordinal array using ordinal.
Change Category Labels
Change the labels for category levels in nominal or ordinal arrays using setlabels.
Add and Drop Category Levels
Add and drop levels from a nominal or ordinal array.
Merge Category Levels
Merge categories in a nominal or ordinal array using mergelevels.
Reorder Category Levels
Reorder the category levels in nominal or ordinal arrays using reorderlevels.
Sort Ordinal Arrays
Determine sorting order for ordinal arrays.
Plot Data Grouped by Category
Plot data grouped by the levels of a categorical variable.
Summary Statistics Grouped by Category
Compute summary statistics grouped by levels of a categorical variable.
Test Differences Between Category Means
Test for significant differences between category (group) means using a t-test, two-way ANOVA (analysis of variance), and ANOCOVA (analysis of covariance) analysis.
Index and Search Using Nominal and Ordinal Arrays
Index and search data by its category, or group.
Linear Regression with Categorical Covariates
Perform a regression with categorical covariates using categorical arrays and fitlm.

Dataset Arrays

Dataset Arrays
Dataset arrays store data with heterogeneous types.
Create a Dataset Array from Workspace Variables
Create a dataset array from a numeric array or heterogeneous variables existing in the MATLAB workspace.
Create a Dataset Array from a File
Create a dataset array from the contents of a tab-delimited or a comma-separated text, or an Excel file.
Add and Delete Observations
Add and delete observations in a dataset array.
Add and Delete Variables
Add and delete variables in a dataset array.
Access Data in Dataset Array Variables
Work with dataset array variables and their data.
Select Subsets of Observations
Select an observation or subset of observations from a dataset array.
Sort Observations in Dataset Arrays
Sort observations (rows) in a dataset array using the command line.
Merge Dataset Arrays
Merge dataset arrays using join.
Stack or Unstack Dataset Arrays
Reformat dataset arrays using stack and unstack.
Clean Messy and Missing Data
Find, clean, and delete observations with missing data in a dataset array.
Calculations on Dataset Arrays
Perform calculations on dataset arrays, including averaging and summarizing with a grouping variable.
Export Dataset Arrays
Export a dataset array from the MATLAB workspace to a text or spreadsheet file.
Dataset Arrays in the Variables Editor
The MATLAB Variables editor provides a convenient interface for viewing, modifying, and plotting dataset arrays.
Index and Search Dataset Arrays
Learn the many ways to index into dataset arrays.
Regression Using Dataset Arrays
This example shows how to perform linear and stepwise regression analyses using dataset arrays.

Data Types

Functions

Categorical Data

Dataset Arrays

Classes

Topics

Categorical Data

Dataset Arrays

Related Information