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Split data into groups and apply function

`Y = splitapply(func,X,G)`

`Y = splitapply(func,X1,...,XN,G)`

`Y = splitapply(func,T,G)`

`[Y1,...,YM] = splitapply(___)`

`Y = splitapply(`

splits `func`

,`X`

,`G`

)`X`

into
groups specified by `G`

and applies the function `func`

to
each group. `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`

, you can use the `findgroups`

function.

`splitapply`

combines two steps in the Split-Apply-Combine Workflow.

`[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`

.

`arrayfun`

| `findgroups`

| `rowfun`

| `unique`

| `varfun`

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