groupfilter
Syntax
Description
Table Data
returns the rows of table or timetable G
= groupfilter(T
,groupvars
,method
)T
that satisfy the groupwise
filtering condition specified in method
. The filtering condition
method
is a function handle applied to each nongrouping variable.
Groups are defined by rows in the variables in groupvars
that have the
same unique combination of values. For example, G = groupfilter(T,"Trial",@(x)
numel(x) > 5)
groups the data in T
by
Trial
, and keeps the rows that belong to groups with more than five
trials.
Array Data
returns the rows of vector or matrix B
= groupfilter(A
,groupvars
,method
)A
that satisfy the groupwise
filtering condition specified in method
. The filtering condition
method
is a function handle applied to all column vectors. Groups are
defined by rows in the column vectors in groupvars
that have the same
unique combination of values.
Examples
Filter TwoVariable Table
Create a table containing two variables.
groupID = [1 1 1 2 2 3]'; sample = [3 1 2 9 8 5]'; T = table(groupID,sample)
T=6×2 table
groupID sample
_______ ______
1 3
1 1
1 2
2 9
2 8
3 5
Group by ID number, and return rows corresponding to groups with more than two samples.
Gnumel = groupfilter(T,"groupID",@(x) numel(x) > 2)
Gnumel=3×2 table
groupID sample
_______ ______
1 3
1 1
1 2
Return rows whose group samples are between 0 and 6.
Gvals = groupfilter(T,"groupID",@(x) min(x) > 0 && max(x) < 6)
Gvals=4×2 table
groupID sample
_______ ______
1 3
1 1
1 2
3 5
Group by Largest Values
Create a table containing two variables that represent a day number and temperature.
daynum = [1 1 1 1 2 2 2 2]'; temp = [67 65 71 55 61 79 58 78]'; T = table(daynum,temp)
T=8×2 table
daynum temp
______ ____
1 67
1 65
1 71
1 55
2 61
2 79
2 58
2 78
Group by day number, and return the largest two temperatures for each day.
G = groupfilter(T,"daynum",@(x) ismember(x,maxk(x,2)))
G=4×2 table
daynum temp
______ ____
1 67
1 71
2 79
2 78
Group by Month
Create a table of dates and corresponding profits.
timeStamps = datetime([2017 3 4; 2017 3 2; 2017 3 15; 2017 4 10; ... 2017 4 14; 2017 4 30; 2017 5 25; ... 2017 5 29; 2017 5 21]); profit = [2032 3071 1185 2587 1998 2899 3112 909 2619]'; T = table(timeStamps,profit)
T=9×2 table
timeStamps profit
___________ ______
04Mar2017 2032
02Mar2017 3071
15Mar2017 1185
10Apr2017 2587
14Apr2017 1998
30Apr2017 2899
25May2017 3112
29May2017 909
21May2017 2619
Group the dates by month, and return rows that correspond to the maximum profit for that month.
Gmax = groupfilter(T,"timeStamps","month",@(x) x == max(x))
Gmax=3×3 table
timeStamps profit month_timeStamps
___________ ______ ________________
02Mar2017 3071 Mar2017
30Apr2017 2899 Apr2017
25May2017 3112 May2017
Return rows whose month had an average profit greater than $2300.
Gavg = groupfilter(T,"timeStamps","month",@(x) mean(x) > 2300)
Gavg=3×3 table
timeStamps profit month_timeStamps
___________ ______ ________________
10Apr2017 2587 Apr2017
14Apr2017 1998 Apr2017
30Apr2017 2899 Apr2017
Filter ThreeVariable Table
Create a table T
that contains information about nine individuals.
groupID = [1 2 3 1 2 3 1 2 3]'; Height = [62 61 59 66 70 72 57 67 71]'; HealthStatus = categorical(["Poor"; "Good"; "Fair"; "Poor"; "Fair"; "Excellent"; "Poor"; "Excellent"; "Fair"]); T = table(groupID,Height,HealthStatus)
T=9×3 table
groupID Height HealthStatus
_______ ______ ____________
1 62 Poor
2 61 Good
3 59 Fair
1 66 Poor
2 70 Fair
3 72 Excellent
1 57 Poor
2 67 Excellent
3 71 Fair
Group by ID number, and return rows for groups that contain only members with a minimum height of 60.
G1 = groupfilter(T,"groupID",@(x) min(x) >= 60,"Height")
G1=3×3 table
groupID Height HealthStatus
_______ ______ ____________
2 61 Good
2 70 Fair
2 67 Excellent
Group by ID number, and return rows for groups that contain only members whose health status is Poor
.
G2 = groupfilter(T,"groupID",@(x) all(x == "Poor"),"HealthStatus")
G2=3×3 table
groupID Height HealthStatus
_______ ______ ____________
1 62 Poor
1 66 Poor
1 57 Poor
Filter with Vector Data
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]';
Group by day of the week, and compute the maximum profit for each group. Display the maximum profits and their corresponding groups.
[maxDailyProfit,dayOfWeek] = groupfilter(profit,timeStamps, ... "dayname",@(x) x == max(x))
maxDailyProfit = 5×1
3071
1185
2899
3112
2619
dayOfWeek = 5x1 categorical
Thursday
Wednesday
Friday
Saturday
Tuesday
Input Arguments
T
— Input table
table  timetable
Input table, specified as a table or timetable.
A
— Input array
column vector  matrix
Input array, specified as a column vector or a group of column vectors stored as a matrix.
groupvars
— Grouping variables or vectors
scalar  vector  matrix  cell array  pattern  function handle  table vartype
subscript
Grouping variables or vectors, specified as one of these options:
For array input data,
groupvars
can be either a column vector with the same number of rows asA
or a group of column vectors arranged in a matrix or a cell array.For table or timetable input data,
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.Indexing Scheme Examples Variable names:
A string, character vector, or cell array
A
pattern
object
"A"
or'A'
— A variable namedA
["A","B"]
or{'A','B'}
— Two variables namedA
andB
"Var"+digitsPattern(1)
— Variables named"Var"
followed by a single digit
Variable index:
An index number that refers to the location of a variable in the table
A vector of numbers
A logical vector. Typically, this vector is the same length as the number of variables, but you can omit trailing
0
orfalse
values
3
— The third variable from the table[2 3]
— The second and third variables from the table[false false true]
— The third variable
Function handle:
A function handle that takes a table variable as input and returns a logical scalar
@isnumeric
— All the variables containing numeric values
Variable type:
A
vartype
subscript that selects variables of a specified type
vartype("numeric")
— All the variables containing numeric values
Example: groupfilter(T,"Var3",method)
method
— Filtering method
function handle
Filtering method, specified as a function handle.
method
defines the function used to filter out members from each
group. The function must return a logical scalar or a logical column vector with the
same number of rows as the input data indicating which group members to select.
If the function returns a logical scalar, then either all members of the group are filtered out (when the value is
false
) or none are filtered out (when the value istrue
).If the function returns a logical vector, then members of groups are filtered out when the corresponding element is
false
, and members are kept when the corresponding element istrue
.
To define the function handle, use a syntax of the form @(inputargs)
mymethod
, where mymethod
depends on
inputargs
.
A function can filter for rows corresponding to groups that meet a condition. For example,
@(x) mean(x) > 10
passes to the output only rows corresponding to groups with a group mean greater than 10.A function can filter for rows that meet a condition within their corresponding group. For example,
@(x) x == max(x)
passes to the output only rows corresponding to the maximum value of rows within their group.
For more information, see Create Function Handle and Anonymous Functions.
When groupfilter
applies the method to more than one nongrouping
variable at a time, the method returns a logical scalar or vector for each variable. For
each row, the corresponding values in all returned scalars or vectors must be
true
to pass the row to the output.
groupbins
— Binning scheme for grouping variables or vectors
"none"
(default)  vector of bin edges  number of bins  length of time (bin width)  name of time unit (bin width)  cell array of binning methods
Binning scheme for grouping variables or vectors, specified as one or more of the following binning methods. To apply the same binning method to all grouping variables or vectors, specify one binning method. To apply a different binning method to each grouping variable or vector, specify a cell array of binning methods, where each cell contains the binning method for the corresponding grouping variable or vector.
"none"
— No binning.Vector of bin edges — The bin edges define the bins. You can specify the edges as numeric values or as
datetime
values fordatetime
grouping variables or vectors.Number of bins — The number determines how many equally spaced bins to create. You can specify the number of bins as a positive integer scalar.
Length of time (bin width) — The length of time determines the width of each bin. You can specify the bin width as a
duration
orcalendarDuration
scalar fordatetime
orduration
grouping variables or vectors.Name of time unit (bin width) — The name of the time unit determines the width of each bin. You can specify the bin width as one of the options in this table for
datetime
orduration
grouping variables or vectors.Value Description Data Type "second"
Each bin is 1 second.
datetime
andduration
"minute"
Each bin is 1 minute.
datetime
andduration
"hour"
Each bin is 1 hour.
datetime
andduration
"day"
Each bin is 1 calendar day. This value accounts for daylight saving time shifts.
datetime
andduration
"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
andduration
"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
datavars
— Table variables to operate on
scalar  vector  cell array  pattern  function handle  table vartype
subscript
Table 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 filtering methods to. Other variables not specified by
datavars
pass through to the output without being operated on.
groupfilter
applies the filtering methods to the specified
variables and uses the results to remove rows from all variables. When
datavars
is not specified, groupfilter
operates on each nongrouping variable.
Indexing Scheme  Examples 

Variable names:


Variable index:


Function handle:


Variable type:


Example: groupfilter(T,groupvars,method,["Var1" "Var2"
"Var4"])
LR
— Included bin edge for binning scheme
"left"
(default)  "right"
Included bin edge for binning scheme, specified as either
"left"
or "right"
,
indicating which end of the bin interval is inclusive.
You can specify LR
only if you also specify
groupbins
, and the value applies to all binning
methods for all grouping variables or vectors.
Output Arguments
G
— Output table
table  timetable
Output table for table or timetable input data, returned as a table or timetable.
G
contains the rows in T
that satisfy the
groupwise filtering method.
B
— Output array
vector  matrix
Output array for array input data, returned as a vector or matrix.
B
contains the rows in A
that satisfy the
groupwise filtering method.
BG
— Grouping vectors
column vector  cell array of column vectors
Grouping vectors for array input data, returned as a column vector or cell array of
column vectors. BG
contains the unique grouping vector or binned
grouping vector combinations that correspond to the rows in B
.
Extended Capabilities
Tall Arrays
Calculate with arrays that have more rows than fit in memory.
Usage notes and limitations:
If
A
andgroupvars
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
anddatavars
arguments do not support function handles.The
method
argument must be a valid input forsplitapply
operating on a tall array.When grouping by discretized datetime arrays, the categorical group names are different compared to inmemory
groupfilter
calculations.
For more information, see Tall Arrays.
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
Sparse inputs are not supported.
Binning scheme is not supported for datetime or duration data.
Input tables that contain multidimensional arrays are not supported.
Computation methods must be constant.
Grouping variables must be constant when the first input argument is a table.
Data variables must be constant.
Binning scheme specified as character vectors or strings must be constant.
Namevalue arguments must be constant.
Computation methods cannot return sparse or multidimensional results.
ThreadBased Environment
Run code in the background using MATLAB® backgroundPool
or accelerate code with Parallel Computing Toolbox™ ThreadPool
.
This function fully supports threadbased environments. For more information, see Run MATLAB Functions in ThreadBased Environment.
Version History
Introduced in R2019bR2022b: Code generation support
Generate C or C++ code for the groupfilter
function. For usage
notes and limitations, see C/C++ Code
Generation.
R2022a: Improved performance with small group size
The groupfilter
function shows improved performance, especially
when the data count in each group is small.
For example, this code filters by group a matrix with 500 groups with a count of 10 each. The code is about 2.32x faster than in the previous release.
function timingGroupfilter data = (1:5000)'; groups = repelem(1:length(data)/10,10)'; p = randperm(length(data)); data = data(p); groups = groups(p); tic for k = 1:600 G = groupfilter(data,groups,@(x) x == max(x)); end toc end
The approximate execution times are:
R2021b: 2.32 s
R2022a: 1.00 s
The code was timed on a Windows^{®} 10, Intel^{®}
Xeon^{®} CPU E51650 v4 @ 3.60 GHz test system by calling the
timingGroupfilter
function.
See Also
Functions
groupsummary
groupcounts
grouptransform
findgroups
splitapply
discretize
varfun
rowfun
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