Group summary computations
G = groupsummary(T,groupvars)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.
G = groupsummary(___,Name,Value)G =
groupsummary(T,'Category1','IncludeMissingGroups',false) excludes
the group made from missing categorical data indicated by
<undefined>.
B = groupsummary(___,Name,Value)
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
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);
BB = 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
BC = 4×1
2
0
0
3
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' }
GC = 2×1
53
47
T — Input dataInput data, specified as a table or timetable.
A — Input arrayInput 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 vectorsvartype subscriptGrouping variables or vectors, specified as one of these options:
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.
For table or timetable inputs, groupvars
indicates which variables to use to compute groups in the data.
You can specify the grouping variables with any of the options
in this table.
| Option | Description | Examples |
|---|---|---|
| Scalar variable name | A character vector or scalar string specifying a single table variable name. |
|
| Vector of variable names | A cell array of character vectors or string array where each element is a table variable name. |
|
| Scalar or vector of variable indices | A scalar or vector of table variable indices. |
|
| Logical scalar or vector | A logical vector whose elements each
correspond to a table variable, where
|
|
| Function handle | A function handle that takes a table variable as input and returns a logical scalar. |
|
vartype subscript | A table subscript generated by the
|
|
Example: groupsummary(T,"Var3")
method — Computation method'sum' | 'mean' | 'median' | 'mode' | 'var' | 'std' | 'min' | 'max' | 'range' | 'nummissing' | 'nnz' | 'all' | function handle | cell arrayComputation 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. For table
input data, the function operates on each table variable separately.
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.
To specify multiple computations at a time, list the options in a cell
array, such as {'mean','median'} or
{myFun1,myFun2}.
NaN values in the input data are automatically omitted
when using the method names described here, with the exception of
'nummissing'. To include NaN
values, consider using a function handle for the method, such as
@sum instead of 'sum'.
Data Types: char | string | cell | function_handle
datavars — Table variables to operate onvartype subscriptTable variables to operate on, specified as one of the options in this
table. datavars indicates which variables of the input
table or timetable to apply the methods to. Other variables not specified by
datavars are not operated on and do not pass through
to the output. When datavars is not specified,
groupsummary operates on each nongrouping
variable.
| Option | Description | Examples |
|---|---|---|
| Variable name | A character vector or scalar string specifying a single table variable name |
|
| Vector of variable names | A cell array of character vectors or string array where each element is a table variable name |
|
| Scalar or vector of variable indices | A scalar or vector of table variable indices |
|
| Logical vector | A logical vector whose elements each correspond to a table variable, where
|
|
| Function handle | A function handle that takes a table variable as input and returns a logical scalar |
|
vartype subscript | A table subscript generated by the |
|
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
are any of the options in the table. The cell array 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.
Example: groupsummary(T,groupvars,method,["Var1" "Var2"
"Var4"])
groupbins — Binning scheme'none' (default) | vector | scalar | cell arrayBinning 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]}.
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.
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 indicatortrue (default) | falseMissing 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 indicatorfalse (default) | trueEmpty 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
G — Output tableOutput table, returned as a table containing the specified computations for each group.
B — Output arrayOutput 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 — GroupsGroups 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 countsGroup 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.
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).
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.
convertvars | discretize | findgroups | groupcounts | groupfilter | grouptransform | rowfun | splitapply | varfun | vartype
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