Main Content

**Class: **dataset

(Not Recommended) Unstack dataset array from single variable into multiple variables

**The dataset data type is not recommended. To work with heterogeneous data,
use the MATLAB ^{®}
table data type instead. See MATLAB
table documentation for more information.**

`A = unstack(B,datavar,indvar)`

[A,iB] = unstack(B,datavar,indvar)

A = unstack(B,datavar,indvar,* 'Parameter'*,

`value`

`A = unstack(B,datavar,indvar)`

unstacks a single variable in dataset
array `B`

into multiple variables in `A`

. In general
`A`

contains more variables, but fewer observations, than
`B`

.

`datavar`

specifies the data variable in `B`

to unstack.
`indvar`

specifies an indicator variable in `B`

that
determines which variable in `A`

each value in `datavar`

is
unstacked into. `unstack`

treats the remaining variables in `B`

as grouping variables. Each unique combination of their values defines a group of observations in
`B`

that will be unstacked into a single observation in
`A`

.

`unstack`

creates `m`

data variables in
`A`

, where `m`

is the number of group levels in
`indvar`

. The values in `indvar`

indicate which of those
`m`

variables receive which values from `datavar`

. The
`j`

-th data variable in `A`

contains the values from
`datavar`

that correspond to observations whose `indvar`

value
was the `j`

-th of the `m`

possible levels. Elements of those
`m`

variables for which no corresponding data value in `B`

exists contain a default value.

`datavar`

is a positive integer, a character vector, a string scalar, or a
logical vector containing a single true value. `indvar`

is a positive integer, a
variable name, or a logical vector containing a single true value.

`[A,iB] = unstack(B,datavar,indvar)`

returns an index vector
`iB`

indicating the correspondence between observations in `A`

and those in `B`

. For each observation in `A`

,
`iB`

contains the index of the first in the corresponding group of observations
in `B`

.

For more information on grouping variables, see Grouping Variables.

`A = unstack(B,datavar,indvar,`

uses the following parameter name/value pairs to control how * 'Parameter'*,

`value`

`unstack`

converts
variables in `B`

to variables in `A`

:`'GroupVars'` | Grouping variables in `B` that define groups of observations.
`groupvars` is a positive integer, a vector of positive integers, a
character vector, a string array, a cell array of character vectors, or a logical vector.
The default is all variables in `B` not listed in
`datavar` or `indvar` . |

`'NewDataVarNames'` | A string array or cell array of character vectors containing names for the data
variables `unstack` should create in `A` . Default is the
group names of the grouping variable specified in `indvar` . |

`'AggregationFun'` | A function handle that accepts a subset of values from `datavar` and
returns a single value. `stack` applies this function to observations from
the same group that have the same value of `indvar` . The function must
aggregate the data values into a single value, and in such cases it is not possible to
recover `B` from `A` using `stack` . The
default is `@sum` for numeric data variables. For non-numeric variables,
there is no default, and you must specify `'AggregationFun'` if multiple
observations in the same group have the same values of `indvar` . |

`'ConstVars'` | Variables in `B` to copy to `A` without unstacking.
The values for these variables in `A` are taken from the first observation
in each group in `B` , so these variables should typically be constant
within each group. `ConstVars` is a positive integer, a vector of positive
integers, a character vector, a string array, a cell array of character vectors, or a
logical vector. The default is no variables. |

You can also specify more than one data variable in `B`

, each of
which becomes a set of `m`

variables in `A`

. In this case,
specify `datavar`

as a vector of positive integers, a string array or cell array
containing variable names, or a logical vector. You may specify only one variable with
`indvar`

. The names of each set of data variables in `A`

are
the name of the corresponding data variable in `B`

concatenated with the names
specified in `'NewDataVarNames'`

. The function specified in
`'AggregationFun'`

must return a value with a single row.

Combine several variables for estimated influenza rates into a single variable. Then unstack the estimated influenza rates by date.

load flu % FLU has a 'Date' variable, and 10 variables for estimated influenza rates % (in 9 different regions, estimated from Google searches, plus a % nationwide estimate from the CDC). Combine those 10 variables into an % array that has a single data variable, 'FluRate', and an indicator % variable, 'Region', that says which region each estimate is from. [flu2,iflu] = stack(flu, 2:11, 'NewDataVarName','FluRate', ... 'IndVarName','Region') % The second observation in FLU is for 10/16/2005. Find the observations % in FLU2 that correspond to that date. flu(2,:) flu2(iflu==2,:) % Use the 'Date' variable from that array to split 'FluRate' into 52 % separate variables, each containing the estimated influenza rates for % each unique date. The new array has one observation for each region. In % effect, this is the original array FLU "on its side". dateNames = cellstr(datestr(flu.Date,'mmm_DD_YYYY')); [flu3,iflu2] = unstack(flu2, 'FluRate', 'Date', ... 'NewDataVarNames',dateNames) % Since observations in FLU3 represent regions, IFLU2 indicates the first % occurrence in FLU2 of each region. flu2(iflu2,:)