Managing Data

R2026b

Data import and export, and grouping variables

Transfer data into and out of MATLAB^® using several different file formats. Valid formats include tabular data, tab-delimited files, Microsoft^® Excel^® spreadsheets, and SAS^® XPORT files. For a table of supported file formats and their associated import and export functions, see Supported File Formats for Import and Export. Alternatively, you can import data interactively by using the Import Tool. Statistics and Machine Learning Toolbox™ supports many, but not all, of the data types available in MATLAB. For more information, see Supported Data Types.

For greater cross-product compatibility, use the categorical or table data types available in MATLAB. For more information, see Create Categorical Arrays or Create Tables and Assign Data to Them, or watch Tables and Categorical Arrays.

Functions

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Data Import and Export

`caseread`	Read case names from file
`casewrite`	Write case names to file
`tblread`	Read tabular data from file
`tblwrite`	Write tabular data to file
`tdfread`	Read tab-delimited file
`xptread`	Create table from data stored in SAS XPORT format file

Dataset Arrays

`dataset`	(Not Recommended) Arrays for statistical data
`dataset2table`	Convert dataset array to table

Categorical 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
`nominal`	(Not Recommended) Arrays for nominal data
`ordinal`	(Not Recommended) Arrays for ordinal data
`countPredictorsAfterCategoricalEncoding`	Number of predictors in tabular data after encoding categorical variables (Since R2026a)

Topics

Statistics and Machine Learning Toolbox Example Data Sets
Use various data sets to try software features available in Statistics and Machine Learning Toolbox.
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.
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.
Linear Regression with Categorical Covariates
Perform a regression with categorical covariates using categorical arrays and fitlm.