groupsummary

Group summary computations

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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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     

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    

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 

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 array
     Tuesday 
     Wednesday 
     Thursday 
     Friday 
     Saturday 

dailyCounts = 5×1

     2
     2
     1
     2
     3

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 array
    "female"
    "female"
    "male"
    "male"

BG{2}
ans = 4x1 logical array

   0
   1
   0
   1

BC
BC = 4×1

     2
     0
     0
     3

Input Arguments

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Input data, specified as a table or timetable.

Input array, specified as a vector or matrix.

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

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

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'}

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 can also specify 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'}.

Data variables for table or timetable input, specified as a scalar, vector, cell array, or function handle. 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

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

Example: 'Profit'

Example: {'Income','Expenses'}

Example: @isnumeric

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:

    ValueDescriptionData 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')

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.

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

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

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

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.

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.

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

Introduced in R2018a