groupsummary

Group summary computations

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Syntax

G = groupsummary(T,groupvars)

G = groupsummary(T,groupvars,method)

G = groupsummary(T,groupvars,method,datavars)

G = groupsummary(T,groupvars,groupbins)

G = groupsummary(T,groupvars,groupbins,method)

G = groupsummary(T,groupvars,groupbins,method,datavars)

G = groupsummary(___,Name,Value)

B = groupsummary(A,groupvars,method)

B = groupsummary(A,groupvars,groupbins,method)

B = groupsummary(___,Name,Value)

[B,BG] = groupsummary(A,___)

[B,BG,BC] = groupsummary(A,___)

Description

example

G = groupsummary(T,groupvars) returns a table containing the computed groups and the number of elements in each group for data in a table or timetable T. A group contains the unique combinations of grouping variables in groupvars. For example, G = groupsummary(T,'Gender') returns the number of Male elements and the number of Female elements in the variable Gender.

example

G = groupsummary(T,groupvars,method) also returns the computations specified in method. For example, G = groupsummary(T,'Gender','median') returns the median of all nongrouping variables in T for both genders, in addition to the number of elements in each group.

example

G = groupsummary(T,groupvars,method,datavars) specifies the variables to apply the computations to.

G = groupsummary(T,groupvars,groupbins) specifies how to bin the data in groupvars. For example, G = groupsummary(T,'SaleDate','year') gives the group counts for all sales in T within each year according to the grouping variable SaleDate.

G = groupsummary(T,groupvars,groupbins,method) bins the data according to groupbins for the computations specified in method.

example

G = groupsummary(T,groupvars,groupbins,method,datavars) bins the data according to groupbins and specifies the variables to apply the computations to.

example

G = groupsummary(___,Name,Value) specifies additional grouping properties using one or more name-value pairs for any of the previous syntaxes. For example, G = groupsummary(T,'Category1','IncludeMissingGroups',false) excludes the group made from missing categorical data indicated by <undefined>.

B = groupsummary(A,groupvars,method) returns the computations according to the unique combinations of grouping vectors in groupvars when A is a vector or matrix. groupvars can be a column vector, matrix, or cell array of column vectors.

example

B = groupsummary(A,groupvars,groupbins,method) bins the data according to groupbins.

example

B = groupsummary(___,Name,Value) specifies additional grouping properties using one or more name-value pairs for either of the previous array syntaxes.

[B,BG] = groupsummary(A,___) also returns the groups for each grouping vector.

example

[B,BG,BC] = groupsummary(A,___) also returns the group counts for each group.

Examples

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Summary Statistics

Open Live Script

Compute summary statistics on table variables.

Create a table T that contains information about five individuals.

Gender = ["male";"female";"female";"male";"male"];
Age = [38;43;38;40;49];
Height = [71;69;64;67;64];
Weight = [176;163;131;133;119];
T = table(Gender,Age,Height,Weight)

T=5×4 table
     Gender     Age    Height    Weight
    ________    ___    ______    ______

    "male"      38       71       176  
    "female"    43       69       163  
    "female"    38       64       131  
    "male"      40       67       133  
    "male"      49       64       119

Compute the counts of males and females by specifying Gender as the grouping variable.

G = groupsummary(T,"Gender")

G=2×2 table
     Gender     GroupCount
    ________    __________

    "female"        2     
    "male"          3

Compute the mean age, height, and weight of females and males separately.

G = groupsummary(T,"Gender","mean")

G=2×5 table
     Gender     GroupCount    mean_Age    mean_Height    mean_Weight
    ________    __________    ________    ___________    ___________

    "female"        2            40.5         66.5            147   
    "male"          3          42.333       67.333         142.67

Still grouping by gender, compute the median height only.

G = groupsummary(T,"Gender","median","Height")

G=2×3 table
     Gender     GroupCount    median_Height
    ________    __________    _____________

    "female"        2             66.5     
    "male"          3               67

Multiple Grouping Variables

Open Live Script

Group table data using two grouping variables.

Create a table T that contains information about five individuals.

Gender = ["male";"female";"male";"female";"male"];
Smoker = logical([1;0;1;0;1]);
Weight = [176;163;131;133;119];
T = table(Gender,Smoker,Weight)

T=5×3 table
     Gender     Smoker    Weight
    ________    ______    ______

    "male"      true       176  
    "female"    false      163  
    "male"      true       131  
    "female"    false      133  
    "male"      true       119

Compute the mean weight, grouped by gender and smoking status. By default, two combinations of gender and smoking status are not represented in the output because they are empty groups.

G = groupsummary(T,{'Gender','Smoker'},'mean','Weight')

G=2×4 table
     Gender     Smoker    GroupCount    mean_Weight
    ________    ______    __________    ___________

    "female"    false         2             148    
    "male"      true          3             142

Set the 'IncludeEmptyGroups' parameter value to true in order to see all group combinations, including the empty ones.

G = groupsummary(T,{'Gender','Smoker'},'mean','Weight','IncludeEmptyGroups',true)

G=4×4 table
     Gender     Smoker    GroupCount    mean_Weight
    ________    ______    __________    ___________

    "female"    false         2             148    
    "female"    true          0             NaN    
    "male"      false         0             NaN    
    "male"      true          3             142

Specify Group Bins

Open Live Script

Group data according to specified bins.

Create a timetable containing sales information for days within a single month.

TimeStamps = datetime([2017 3 4; 2017 3 2; 2017 3 15; 2017 3 10;...
                       2017 3 14; 2017 3 31; 2017 3 25;...
                       2017 3 29; 2017 3 21; 2017 3 18]);
Profit = [2032 3071 1185 2587 1998 2899 3112 909 2619 3085]';
TotalItemsSold = [14 13 8 5 10 16 8 6 7 11]';
TT = timetable(TimeStamps,Profit,TotalItemsSold)

TT=10×2 timetable
    TimeStamps     Profit    TotalItemsSold
    ___________    ______    ______________

    04-Mar-2017     2032           14      
    02-Mar-2017     3071           13      
    15-Mar-2017     1185            8      
    10-Mar-2017     2587            5      
    14-Mar-2017     1998           10      
    31-Mar-2017     2899           16      
    25-Mar-2017     3112            8      
    29-Mar-2017      909            6      
    21-Mar-2017     2619            7      
    18-Mar-2017     3085           11

Compute the mean profit grouped by the total items sold, binning the groups into intervals of item numbers.

format shorte
G = groupsummary(TT,'TotalItemsSold',[0 4 8 12 16],'mean','Profit')

G=3×3 table
    disc_TotalItemsSold    GroupCount    mean_Profit
    ___________________    __________    ___________

         [4, 8)            3.0000e+00    2.0383e+03 
         [8, 12)           4.0000e+00    2.3450e+03 
         [12, 16]          3.0000e+00    2.6673e+03

Compute the mean profit grouped by day of the week.

G = groupsummary(TT,'TimeStamps','dayname','mean','Profit')

G=5×3 table
    dayname_TimeStamps    GroupCount    mean_Profit
    __________________    __________    ___________

        Tuesday           2.0000e+00    2.3085e+03 
        Wednesday         2.0000e+00    1.0470e+03 
        Thursday          1.0000e+00    3.0710e+03 
        Friday            2.0000e+00    2.7430e+03 
        Saturday          3.0000e+00    2.7430e+03

Group Operations with Vector Data

Open Live Script

Create a vector of dates and a vector of corresponding profit values.

timeStamps = datetime([2017 3 4; 2017 3 2; 2017 3 15; 2017 3 10; ...
                       2017 3 14; 2017 3 31; 2017 3 25; ...
                       2017 3 29; 2017 3 21; 2017 3 18]);
profit = [2032 3071 1185 2587 1998 2899 3112 909 2619 3085]';

Compute the mean profit by day of the week. Display the means, the group names, and the number of members in each group.

format shorte
[meanDailyProfit,dayOfWeek,dailyCounts] = groupsummary(profit,timeStamps,'dayname','mean')

meanDailyProfit = 5×1

   2.3085e+03
   1.0470e+03
   3.0710e+03
   2.7430e+03
   2.7430e+03

dayOfWeek = 5x1 categorical
     Tuesday 
     Wednesday 
     Thursday 
     Friday 
     Saturday

dailyCounts = 5×1

     2
     2
     1
     2
     3

Multiple Grouping Vectors for Vector Input

Open Live Script

Compute the mean weights for four groups based on their gender and smoker status.

Store patient information as three vectors of different types.

Gender = ["male";"female";"male";"female";"male"];
Smoker = logical([1;0;1;0;1]);
Weight = [176;163;131;133;119];

Grouping by gender and smoker status, compute the mean weights. B contains the mean for each group (NaN for empty groups). BG is a cell array containing two vectors that describe the groups as you look at their elements rowwise. For instance, the first row of BG{1} says that the patients in the first group are female, and the first row of BG{2} says that they are nonsmokers. Finally, BC contains the number of members in each group for the corresponding groups in BG.

[B,BG,BC] = groupsummary(Weight,{Gender,Smoker},'mean','IncludeEmptyGroups',true);
B

B = 4×1

   148
   NaN
   NaN
   142

BG{1}

ans = 4x1 string
    "female"
    "female"
    "male"
    "male"

BG{2}

ans = 4x1 logical array

   0
   1
   0
   1

BC

Method Function Handle with Multiple Inputs

Open Live Script

Load data containing patient information and create a table describing each patient's gender, systolic and diastolic blood pressure, height, and weight.

load patients
T = table(Gender,Systolic,Diastolic,Height,Weight)

T=100×5 table
      Gender      Systolic    Diastolic    Height    Weight
    __________    ________    _________    ______    ______

    {'Male'  }      124          93          71       176  
    {'Male'  }      109          77          69       163  
    {'Female'}      125          83          64       131  
    {'Female'}      117          75          67       133  
    {'Female'}      122          80          64       119  
    {'Female'}      121          70          68       142  
    {'Female'}      130          88          64       142  
    {'Male'  }      115          82          68       180  
    {'Male'  }      115          78          68       183  
    {'Female'}      118          86          66       132  
    {'Female'}      114          77          68       128  
    {'Female'}      115          68          66       137  
    {'Male'  }      127          74          71       174  
    {'Male'  }      130          95          72       202  
    {'Female'}      114          79          65       129  
    {'Male'  }      130          92          71       181  
      ⋮

Grouping by gender, compute the correlation between patient height and weight and the correlation between systolic and diastolic blood pressure. Use the xcov function as the method to compute the correlation. The first two input arguments to xcov describe the data to correlate, the third argument describes the lag size, and the fourth argument describes the type of normalization. For each group computation, the x and y arguments passed into xcov are specified pairwise by variable from the two cell elements ["Height","Systolic"] and ["Weight","Diastolic"].

G = groupsummary(T,"Gender",@(x,y)xcov(x,y,0,'coeff'),{["Height","Systolic"],["Weight","Diastolic"]})

G=2×4 table
      Gender      GroupCount    fun1_Height_Weight    fun1_Systolic_Diastolic
    __________    __________    __________________    _______________________

    {'Female'}        53             0.071278                 0.48731        
    {'Male'  }        47             0.047571                 0.50254

Alternatively, if your data is in vector or matrix form instead of in a table, you can provide the data to correlate as the first input argument of groupsummary.

[G,GR,GC] = groupsummary({[Height,Systolic],[Weight,Diastolic]},Gender,@(x,y)xcov(x,y,0,'coeff'))

G = 2×2

    0.0713    0.4873
    0.0476    0.5025

GR = 2x1 cell
    {'Female'}
    {'Male'  }

Input Arguments

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`T` — Input data
table | timetable

Input data, specified as a table or timetable.

`A` — Input array
vector | matrix | cell array

Input array, specified as a vector, matrix, or cell array of vectors or matrices.

When you specify a function handle for method that takes more than one input argument, the input data A must be a cell array of vectors or matrices. In each call to the function by group, the input arguments are the corresponding columns of each element in the cell array. For example:

groupsummary({x1, y1},groupvars,@(x,y) myFun(x,y)) calculates myFun(x1,y1) for each group.
groupsummary({[x1 x2], [y1 y2]},groupvars,@(x,y) myFun(x,y)) first calculates myFun(x1,y1) for each group, and then calculates myFun(x2,y2) for each group.

`groupvars` — Grouping variables or vectors
scalar | vector | matrix | cell array | function handle | table `vartype` subscript

Grouping variables or vectors, specified as a scalar, vector, matrix, cell array, function handle, or table vartype subscript.

For table or timetable input data, groupvars indicates which columns to use to compute the groups, and can be one of the following:

A character vector or scalar string specifying a single table variable name
A cell array of character vectors or a string array, where each element is a table variable name
A vector of table variable indices
A logical vector whose elements each correspond to a table variable, where true includes the corresponding variable and false excludes it
A function handle that takes a table variable as input and returns a logical scalar
A table vartype subscript

For array input, groupvars can be either a column vector with the same number of rows as A or a group of column vectors arranged in a matrix or cell array.

Example: 'Age'

Example: {'Height','Weight'}

Example: vartype('numeric')

`method` — Computation method
`'sum'` | `'mean'` | `'median'` | `'mode'` | `'var'` | `'std'` | `'min'` | `'max'` | `'range'` | `'nummissing'` | `'nnz'` | `'all'` | function handle | cell array

Computation method, specified as one of the following:

'sum' — sum
'mean' — mean
'median' — median
'mode' — mode
'var' — variance
'std' — standard deviation
'min' — minimum
'max' — maximum
'range' — maximum minus minimum
'nummissing' — number of missing elements
'nnz' — number of nonzero and non-NaN elements
'all' — all computations previously listed

You also can specify method as a function handle that returns one entity per group whose first dimension has length 1.

To specify multiple computations at a time, list the options in a cell array, such as {'mean','median'} or {myFun1, myFun2}.

When the input data is a table T and you specify a function handle for method that takes more than one input argument, you must specify datavars. The datavars argument must be a cell array whose elements indicate the table variables to use for each input into the method. In each call to the function by group, the input arguments are the corresponding table variables of the cell array elements. For example:

groupsummary(T,groupvars,@(x,y) myFun(x,y),{"x1", "y1"}) calculates myFun(T.x1,T.y1) for each group.
groupsummary(T,groupvars,@(x,y) myFun(x,y),{["x1" "x2"],["y1" "y2"]}) first calculates myfun(T.x1,T.y1) for each group, and then calculates myfun(T.x2,T.y2) for each group.

When the input data is in vector or matrix form and you specify a function handle for method that takes more than one input argument, the input data A must be a cell array of vectors or matrices. In each call to the function, the input arguments are the corresponding columns of each element in the cell array. For example:

groupsummary({x1, y1},groupvars,@(x,y) myFun(x,y)) calculates myFun(x1,y1) for each group.
groupsummary({[x1 x2], [y1 y2]},groupvars,@(x,y) myFun(x,y)) first calculates myFun(x1,y1) for each group, and then calculates myFun(x2,y2) for each group.

`datavars` — Data variables
scalar | vector | cell array | function handle | table `vartype` subscript

Data variables for table or timetable input, specified as a scalar, vector, cell array, function handle, or table vartype subscript. datavars indicates which variables of the input table to apply the methods to, and can be one of the following options:

A character vector or scalar string specifying a single table variable name
A cell array of character vectors or string array where each element is a table variable name
A vector of table variable indices
A logical vector whose elements each correspond to a table variable, where true includes the corresponding variable and false excludes it
A function handle that takes a table variable as input and returns a logical scalar
A table vartype subscript

groupsummary(T,groupvars,@(x,y) myFun(x,y),{"x1", "y1"}) calculates myFun(T.x1,T.y1) for each group.
groupsummary(T,groupvars,@(x,y) myFun(x,y),{["x1" "x2"],["y1" "y2"]}) first calculates myfun(T.x1,T.y1) for each group, and then calculates myfun(T.x2,T.y2) for each group.

When datavars is not specified, groupsummary applies the computations to each nongrouping variable.

Example: 'Profit'

Example: {'Income','Expenses'}

Example: @isnumeric

Example: vartype('numeric')

`groupbins` — Binning scheme
`'none'` (default) | vector | scalar | cell array

Binning scheme, specified as one of the following options:

'none', indicating no binning
A list of bin edges, specified as a numeric vector, or a datetime vector for datetime grouping variables or vectors
A number of bins, specified as an integer scalar
A time duration, specified as a scalar of type duration or calendarDuration indicating bin widths (for datetime or duration grouping variables or vectors only)

A time bin for datetime and duration grouping variables or vectors only, specified as one of the following character vectors:

Value	Description	Data Type
`'second'`	Each bin is 1 second.	`datetime` and `duration`
`'minute'`	Each bin is 1 minute.	`datetime` and `duration`
`'hour'`	Each bin is 1 hour.	`datetime` and `duration`
`'day'`	Each bin is 1 calendar day. This value accounts for Daylight Saving Time shifts.	`datetime` and `duration`
`'week'`	Each bin is 1 calendar week.	`datetime` only
`'month'`	Each bin is 1 calendar month.	`datetime` only
`'quarter'`	Each bin is 1 calendar quarter.	`datetime` only
`'year'`	Each bin is 1 calendar year. This value accounts for leap days.	`datetime` and `duration`
`'decade'`	Each bin is 1 decade (10 calendar years).	`datetime` only
`'century'`	Each bin is 1 century (100 calendar years).	`datetime` only
`'secondofminute'`	Bins are seconds from 0 to 59.	`datetime` only
`'minuteofhour'`	Bins are minutes from 0 to 59.	`datetime` only
`'hourofday'`	Bins are hours from 0 to 23.	`datetime` only
`'dayofweek'`	Bins are days from 1 to 7. The first day of the week is Sunday.	`datetime` only
`'dayname'`	Bins are full day names such as `'Sunday'`.	`datetime` only
`'dayofmonth'`	Bins are days from 1 to 31.	`datetime` only
`'dayofyear'`	Bins are days from 1 to 366.	`datetime` only
`'weekofmonth'`	Bins are weeks from 1 to 6.	`datetime` only
`'weekofyear'`	Bins are weeks from 1 to 54.	`datetime` only
`'monthname'`	Bins are full month names such as `'January'`.	`datetime` only
`'monthofyear'`	Bins are months from 1 to 12.	`datetime` only
`'quarterofyear'`	Bins are quarters from 1 to 4.	`datetime` only

A cell array listing binning rules for each grouping variable or vector

When multiple grouping variables are specified, you can provide a single binning rule that is applied to all grouping variables, or a cell array containing a binning method for each grouping variable such as {'none',[0 2 4 Inf]}.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example:

G =
                    groupsummary(T,groupvars,groupbins,'IncludedEdge','right')

`'IncludedEdge'` — Included bin edge
`'left'` (default) | `'right'`

Included bin edge, specified as either 'left' or 'right', indicating which end of the bin interval is inclusive.

This name-value pair can only be specified when groupbins is specified, and the value is applied to all binning schemes for all grouping variables or vectors.

`'IncludeMissingGroups'` — Missing groups indicator
`true` (default) | `false`

Missing groups indicator, specified as true or false. When the parameter value is true, groupsummary displays groups made up of missing values, such as NaN. When the parameter value is false, groupsummary does not display the missing groups.

Data Types: logical

`'IncludeEmptyGroups'` — Empty groups indicator
`false` (default) | `true`

Empty groups indicator, specified as true or false. When the parameter value is false, groupsummary does not display groups with zero elements. When the parameter value is true, groupsummary displays the empty groups.

Data Types: logical

Output Arguments

collapse all

`G` — Output table
table

Output table, returned as a table containing the specified computations for each group.

`B` — Output array
vector | matrix

Output array, returned as a vector or matrix containing the group computations. When you specify multiple methods, groupsummary horizontally concatenates the computations in the order that they were listed.

`BG` — Groups
column vector | cell array of column vectors

Groups for array input data, returned as a column vector or cell array of column vectors each corresponding to a grouping vector.

When you provide more than one grouping vector, BG is a cell array containing column vectors of equal length. The group information can be found by looking at the elements rowwise across all vectors in BG. Each group maps to the corresponding row of the output array B.

`BC` — Group counts
column vector

Group counts for array input data, returned as a column vector containing the number of elements in each group. The length of BC is the same as the length of the group column vectors returned in BG.

Tips

When making many calls to groupsummary, consider converting grouping variables to type categorical or logical when possible for improved performance. For example, if you have a grouping variable of type char (such as Gender with elements 'Male' and 'Female'), you can convert it to a categorical value using the command categorical(Gender).

Extended Capabilities

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

Usage notes and limitations:

If A and groupvars are both tall matrices, then they must have the same number of rows.
If the first input is a tall matrix, then groupvars can be a cell array containing tall grouping vectors.
The groupvars and datavars arguments do not support function handles.
The 'IncludeEmptyGroups' name-value pair is not supported.
The 'median' and 'mode' methods are not supported.
For tall datetime arrays, the 'std' method is not supported.
If the method argument is a function handle, then it must be a valid input for splitapply operating on a tall array. If the function handle takes multiple inputs, then the first input to groupsummary must be a tall table.
The order of the groups might be different compared to in-memory groupsummary calculations.
When grouping by discretized datetime arrays, the categorical group names are different compared to in-memory groupsummary calculations.

For more information, see Tall Arrays.

Documentation

groupsummary

Syntax

Description

Examples

Summary Statistics

Multiple Grouping Variables

Specify Group Bins

Group Operations with Vector Data

Multiple Grouping Vectors for Vector Input

Method Function Handle with Multiple Inputs

Input Arguments

`T` — Input data
table | timetable

`A` — Input array
vector | matrix | cell array

`groupvars` — Grouping variables or vectors
scalar | vector | matrix | cell array | function handle | table `vartype` subscript

`method` — Computation method
`'sum'` | `'mean'` | `'median'` | `'mode'` | `'var'` | `'std'` | `'min'` | `'max'` | `'range'` | `'nummissing'` | `'nnz'` | `'all'` | function handle | cell array

`datavars` — Data variables
scalar | vector | cell array | function handle | table `vartype` subscript

`groupbins` — Binning scheme
`'none'` (default) | vector | scalar | cell array

Name-Value Pair Arguments

`'IncludedEdge'` — Included bin edge
`'left'` (default) | `'right'`

`'IncludeMissingGroups'` — Missing groups indicator
`true` (default) | `false`

`'IncludeEmptyGroups'` — Empty groups indicator
`false` (default) | `true`

Output Arguments

`G` — Output table
table

`B` — Output array
vector | matrix

`BG` — Groups
column vector | cell array of column vectors

`BC` — Group counts
column vector

Tips

Extended Capabilities

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

See Also

MATLAB Documentation

Support

Documentation

groupsummary

Syntax

Description

Examples

Summary Statistics

Multiple Grouping Variables

Specify Group Bins

Group Operations with Vector Data

Multiple Grouping Vectors for Vector Input

Method Function Handle with Multiple Inputs

Input Arguments

T — Input data table | timetable

A — Input array vector | matrix | cell array

groupvars — Grouping variables or vectors scalar | vector | matrix | cell array | function handle | table vartype subscript

method — Computation method 'sum' | 'mean' | 'median' | 'mode' | 'var' | 'std' | 'min' | 'max' | 'range' | 'nummissing' | 'nnz' | 'all' | function handle | cell array

datavars — Data variables scalar | vector | cell array | function handle | table vartype subscript

groupbins — Binning scheme 'none' (default) | vector | scalar | cell array

Name-Value Pair Arguments

'IncludedEdge' — Included bin edge 'left' (default) | 'right'

'IncludeMissingGroups' — Missing groups indicator true (default) | false

'IncludeEmptyGroups' — Empty groups indicator false (default) | true

Output Arguments

G — Output table table

B — Output array vector | matrix

BG — Groups column vector | cell array of column vectors

BC — Group counts column vector

Tips

Extended Capabilities

Tall Arrays Calculate with arrays that have more rows than fit in memory.

See Also

MATLAB Documentation

Support

`T` — Input data
table | timetable

`A` — Input array
vector | matrix | cell array

`groupvars` — Grouping variables or vectors
scalar | vector | matrix | cell array | function handle | table `vartype` subscript

`method` — Computation method
`'sum'` | `'mean'` | `'median'` | `'mode'` | `'var'` | `'std'` | `'min'` | `'max'` | `'range'` | `'nummissing'` | `'nnz'` | `'all'` | function handle | cell array

`datavars` — Data variables
scalar | vector | cell array | function handle | table `vartype` subscript

`groupbins` — Binning scheme
`'none'` (default) | vector | scalar | cell array

`'IncludedEdge'` — Included bin edge
`'left'` (default) | `'right'`

`'IncludeMissingGroups'` — Missing groups indicator
`true` (default) | `false`

`'IncludeEmptyGroups'` — Empty groups indicator
`false` (default) | `true`

`G` — Output table
table

`B` — Output array
vector | matrix

`BG` — Groups
column vector | cell array of column vectors

`BC` — Group counts
column vector

Tall Arrays
Calculate with arrays that have more rows than fit in memory.