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# splitapply

Split data into groups and apply function

## Syntax

``Y = splitapply(func,X,G)``
``Y = splitapply(func,X1,...,XN,G)``
``Y = splitapply(func,T,G)``
``[Y1,...,YM] = splitapply(___)``

## Description

To split data into groups and apply a function to the groups, use the `findgroups` and `splitapply` functions together. For more information about calculations on groups of data, see Calculations on Groups of Data.

example

````Y = splitapply(func,X,G)` splits `X` into groups specified by `G` and applies the function `func` to each group. Then `splitapply` returns `Y` as an array that contains the concatenated outputs from `func` for the groups split out of `X`. The input argument `G` is a vector of positive integers that specifies the groups to which corresponding elements of `X` belong. If `G` contains `NaN` values, `splitapply` omits the corresponding values in `X` when it splits `X` into groups.To create `G`, first use the `findgroups` function. Then use `splitapply`.```

example

````Y = splitapply(func,X1,...,XN,G)` splits `X1,...,XN` into groups and applies `func`. The `splitapply` function calls `func` once per group, with corresponding elements from `X1,...,XN` as the `N` input arguments to `func`.```

example

````Y = splitapply(func,T,G)` splits variables of table `T` into groups, applies `func`, and returns `Y` as an array. The `splitapply` function treats the variables of `T` as vectors, matrices, or cell arrays, depending on the data types and sizes of the table variables. If `T` has `N` variables, then `func` must accept `N` input arguments.```

example

````[Y1,...,YM] = splitapply(___)` splits variables into groups and applies `func` to each group. `func` returns multiple output arguments. `Y1,...,YM` contains the concatenated outputs from `func` for the groups split out of the input data variables. `func` can return output arguments that belong to different classes, but the class of each output must be the same each time `func` is called. You can use this syntax with any of the input arguments of the previous syntaxes.The number of output arguments from `func` need not be the same as the number of input arguments specified by `X1,...,XN`.```

## Examples

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Use group numbers to split patient weight measurements into groups of weights for smokers and nonsmokers. Then calculate the mean weight for each group of patients.

Load patient data from the sample file `patients.mat`.

```load patients whos Smoker Weight```
``` Name Size Bytes Class Attributes Smoker 100x1 100 logical Weight 100x1 800 double ```

Specify groups with `findgroups`. Each element of `G` is a group number that specifies which group a patient is in. Group `1` contains nonsmokers and group `2` contains smokers.

`G = findgroups(Smoker)`
```G = 100×1 2 1 1 1 1 1 2 1 1 1 ⋮ ```

Display the weights of the patients.

`Weight`
```Weight = 100×1 176 163 131 133 119 142 142 180 183 132 ⋮ ```

Split the `Weight` array into two groups of weights using `G`. Apply the `mean` function. The mean weight of the nonsmokers is a bit less than the mean weight of the smokers.

`meanWeights = splitapply(@mean,Weight,G)`
```meanWeights = 2×1 149.9091 161.9412 ```

Calculate the variances of the differences in blood pressure readings for groups of patients, and display the results. The blood pressure readings are contained in two data variables. To calculate the differences, use a function that takes two input arguments.

Load blood pressure readings and smoking data for 100 patients from the data file `patients.mat`.

```load patients whos Systolic Diastolic Smoker```
``` Name Size Bytes Class Attributes Diastolic 100x1 800 double Smoker 100x1 100 logical Systolic 100x1 800 double ```

Define `func` as a function that calculates the variances of the differences between systolic and diastolic blood-pressure readings for smokers and nonsmokers. `func` requires two input arguments.

`func = @(x,y) var(x-y)`
```func = function_handle with value: @(x,y)var(x-y) ```

Use `findgroups` and `splitapply` to split the patient data into groups and calculate the variances of the differences. `findgroups` also returns group identifiers in `smokers`. The `splitapply` function calls `func` once per group, with `Systolic` and `Diastolic` as the two input arguments.

```[G,smokers] = findgroups(Smoker); varBP = splitapply(func,Systolic,Diastolic,G)```
```varBP = 2×1 44.4459 48.6783 ```

Create a table that contains the variances of the differences, with the number of patients in each group.

```numPatients = splitapply(@numel,Smoker,G); T = table(smokers,numPatients,varBP)```
```T=2×3 table smokers numPatients varBP _______ ___________ ______ false 66 44.446 true 34 48.678 ```

Calculate the minimum, median, and maximum weights for groups of patients and return these results as arrays for each group. `splitapply` concatenates the output arguments so that you can distinguish output for each group from output for the other groups.

Define a function that returns the minimum, median, and maximum as a row vector.

`mystats = @(x)[min(x) median(x) max(x)]`
```mystats = function_handle with value: @(x)[min(x),median(x),max(x)] ```

Load patient weights, hospital locations, and statuses as smokers from the sample file `patients.mat`.

```load patients whos Weight Location Smoker```
``` Name Size Bytes Class Attributes Location 100x1 14208 cell Smoker 100x1 100 logical Weight 100x1 800 double ```

Use `findgroups` and `splitapply` to split the patient weights into groups and calculate statistics for each group.

```G = findgroups(Location,Smoker); Y = splitapply(mystats,Weight,G)```
```Y = 6×3 111.0000 137.0000 194.0000 120.0000 170.5000 189.0000 118.0000 134.0000 189.0000 115.0000 170.0000 191.0000 117.0000 140.0000 189.0000 126.0000 178.0000 202.0000 ```

In this example, you can return nonscalar output as row vectors because the data and grouping variables are column vectors. Each row of `Y` contains statistics for a different group of patients.

Calculate the mean body-mass-index (BMI) from tables of patient data. Group the patients by hospital locations and statuses as smokers or nonsmokers.

Load patient data and grouping variables from the sample file `patients.mat` into tables. (Convert the hospital locations to a string array.)

```load patients DT = table(Height,Weight); Location = string(Location); GT = table(Location,Smoker);```

Define a function that calculates mean BMI from the weights and heights of groups or patients.

`meanBMIFcn = @(h,w)mean((w ./ (h.^2)) * 703)`
```meanBMIFcn = function_handle with value: @(h,w)mean((w./(h.^2))*703) ```

Create a table that contains the mean BMI for each group.

```[G,results] = findgroups(GT); meanBMI = splitapply(meanBMIFcn,DT,G); results.meanBMI = meanBMI```
```results=6×3 table Location Smoker meanBMI ___________________________ ______ _______ "County General Hospital" false 23.774 "County General Hospital" true 24.865 "St. Mary's Medical Center" false 22.968 "St. Mary's Medical Center" true 24.905 "VA Hospital" false 23.946 "VA Hospital" true 24.227 ```

Calculate the minimum, mean, and maximum weights for groups of patients and return results in a table.

Load patient data into a table.

```load patients T = table(Smoker,Weight)```
```T=100×2 table Smoker Weight ______ ______ true 176 false 163 false 131 false 133 false 119 false 142 true 142 false 180 false 183 false 132 false 128 false 137 false 174 true 202 false 129 true 181 ⋮ ```

Group patient weights by smoker status. The attached supporting function, `multiStats`, returns the minimum, mean, and maximum values from an input array as three outputs. Apply `multiStats` to the smokers and nonsmokers. Create a table that contains the outputs from `multiStats` for each group.

```[G,smoker] = findgroups(T.Smoker); [minWeight,meanWeight,maxWeight] = splitapply(@multiStats,T.Weight,G); result = table(smoker,minWeight,meanWeight,maxWeight)```
```result=2×4 table smoker minWeight meanWeight maxWeight ______ _________ __________ _________ false 111 149.91 194 true 115 161.94 202 ```
```function [lo,avg,hi] = multiStats(x) lo = min(x); avg = mean(x); hi = max(x); end```

## Input Arguments

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Function to apply to groups of data, specified as a function handle.

If `func` returns a nonscalar output argument, then the argument must be oriented so that `splitapply` can concatenate the output arguments from successive calls to `func`. For example, if the input data variables are column vectors, then `func` must return either a scalar or a row vector as an output argument.

Example: `Y = splitapply(@sum,X,G)` returns the sums of the groups of data in `X`.

Data variable, specified as a vector, matrix, or cell array. The elements of `X` belong to groups specified by the corresponding elements of `G`.

If `X` is a matrix, `splitapply` treats each column or row as a separate data variable. The orientation of `G` determines whether `splitapply` treats the columns or rows of `X` as data variables.

Group numbers, specified as a vector of positive integers.

• If `X` is a vector or cell array, then `G` must be the same length as `X`.

• If `X` is a matrix, then the length of `G` must be equal to the number of columns or rows of `X`, depending on the orientation of `G`.

• If the input argument is table `T`, then `G` must be a column vector. The length of `G` must be equal to the number of rows of `T`.

Data variables, specified as a table. `splitapply` treats each table variable as a separate data variable.

## More About

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### Calculations on Groups of Data

In data analysis, you commonly perform calculations on groups of data. For such calculations, you split one or more data variables into groups of data, perform a calculation on each group, and combine the results into one or more output variables. You can specify the groups using one or more grouping variables. The unique values in the grouping variables define the groups that the corresponding values of the data variables belong to.

For example, the diagram shows a simple grouped calculation that splits a 6-by-1 numeric vector into two groups of data, calculates the mean of each group, and then combines the outputs into a 2-by-1 numeric vector. The 6-by-1 grouping variable has two unique values, `AB` and `XYZ`.

You can specify grouping variables that have numbers, text, dates and times, categories, or bins.

## Version History

Introduced in R2015b