Split data into groups and apply function

`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`

.

`accumarray`

| `arrayfun`

| `convertvars`

| `discretize`

| `findgroups`

| `groupsummary`

| `histcounts`

| `rowfun`

| `unique`

| `varfun`

| `vartype`