splitapply - Split data into groups and apply function - MATLAB (original) (raw)

Split data into groups and apply function

Syntax

Description

To split data into groups and apply a function to the groups, use thefindgroups and splitapply functions together. For more information about calculations on groups of data, see Calculations on Groups of Data.

Y = splitapply([func](#butc687-func),[X](#butc687-X),[G](#butc687-G)) splits X into groups specified by G and applies the function func to each group. Thensplitapply returns Y as an array that contains the concatenated outputs from func for the groups split out of X. The input argument G is a vector of positive integers that specifies the groups to which corresponding elements ofX belong.

The output Y and the group numbers G have the same ordering.

If any elements of G are NaNs, thensplitapply omits the corresponding values inX when it splits X into groups.

To create G, first use the findgroups function. Then usesplitapply.

example

Y = splitapply([func](#butc687-func),[X](#butc687-X)1,...,[X](#butc687-X)N,[G](#butc687-G)) splits X1,...,XN into groups and applies func. The splitapply function calls func once per group, with corresponding elements from X1,...,XN as theN input arguments to func.

example

Y = splitapply([func](#butc687-func),[T](#butc687-T),[G](#butc687-G)) splits variables of table T into groups, appliesfunc, and returns Y as an array. Thesplitapply function treats the variables ofT as vectors, matrices, or cell arrays, depending on the data types and sizes of the table variables. If T hasN variables, then func must acceptN input arguments.

example

[Y1,...,YM] = splitapply(___) splits variables into groups and applies func to each group.func returns multiple output arguments.Y1,...,YM contains the concatenated outputs fromfunc for the groups split out of the input data variables.func can return output arguments that belong to different classes, but the class of each output must be the same each timefunc is called. You can use this syntax with any of the input arguments of the previous syntaxes.

The number of output arguments from func need not be the same as the number of input arguments specified by X1,...,XN.

example

Examples

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Use group numbers to split patient weight measurements into groups of weights for smokers and nonsmokers. Then calculate the mean weight for each group of patients.

Load patient data from the sample file patients.mat.

load patients whos Smoker Weight

Name Size Bytes Class Attributes

Smoker 100x1 100 logical
Weight 100x1 800 double

Specify groups with findgroups. Each element of G is a group number that specifies which group a patient is in. Group 1 contains nonsmokers and group 2 contains smokers.

G = 100×1

Display the weights of the patients.

Weight = 100×1

176 163 131 133 119 142 142 180 183 132 128 137 174 202 129 ⋮

Split the Weight array into two groups of weights using G. Apply the mean function. The mean weight of the nonsmokers is a bit less than the mean weight of the smokers.

meanWeights = splitapply(@mean,Weight,G)

meanWeights = 2×1

149.9091 161.9412

Calculate the variances of the differences in blood pressure readings for groups of patients, and display the results. The blood pressure readings are contained in two data variables. To calculate the differences, use a function that takes two input arguments.

Load blood pressure readings and smoking data for 100 patients from the data file patients.mat.

load patients whos Systolic Diastolic Smoker

Name Size Bytes Class Attributes

Diastolic 100x1 800 double
Smoker 100x1 100 logical
Systolic 100x1 800 double

Define func as a function that calculates the variances of the differences between systolic and diastolic blood-pressure readings for smokers and nonsmokers. func requires two input arguments.

func = functionhandle with value: @(x,y)var(x-y)

Use findgroups and splitapply to split the patient data into groups and calculate the variances of the differences. findgroups also returns group identifiers in smokers. The splitapply function calls func once per group, with Systolic and Diastolic as the two input arguments.

[G,smokers] = findgroups(Smoker); varBP = splitapply(func,Systolic,Diastolic,G)

varBP = 2×1

44.4459 48.6783

Create a table that contains the variances of the differences, with the number of patients in each group.

numPatients = splitapply(@numel,Smoker,G); T = table(smokers,numPatients,varBP)

T=2×3 table smokers numPatients varBP _______ ___________ ______

 false         66         44.446
 true          34         48.678

Calculate the minimum, median, and maximum weights for groups of patients and return these results as arrays for each group. splitapply concatenates the output arguments so that you can distinguish output for each group from output for the other groups.

Define a function that returns the minimum, median, and maximum as a row vector.

mystats = @(x)[min(x) median(x) max(x)]

mystats = functionhandle with value: @(x)[min(x),median(x),max(x)]

Load patient weights, hospital locations, and statuses as smokers from the sample file patients.mat.

load patients whos Weight Location Smoker

Name Size Bytes Class Attributes

Location 100x1 15808 cell
Smoker 100x1 100 logical
Weight 100x1 800 double

Use findgroups and splitapply to split the patient weights into groups and calculate statistics for each group.

G = findgroups(Location,Smoker); Y = splitapply(mystats,Weight,G)

Y = 6×3

111.0000 137.0000 194.0000 120.0000 170.5000 189.0000 118.0000 134.0000 189.0000 115.0000 170.0000 191.0000 117.0000 140.0000 189.0000 126.0000 178.0000 202.0000

In this example, you can return nonscalar output as row vectors because the data and grouping variables are column vectors. Each row of Y contains statistics for a different group of patients.

Calculate the mean body-mass-index (BMI) from tables of patient data. Group the patients by hospital locations and statuses as smokers or nonsmokers.

Load patient data and grouping variables from the sample file patients.mat into tables. (Convert the hospital locations to a string array.)

load patients DT = table(Height,Weight); Location = string(Location); GT = table(Location,Smoker);

Define a function that calculates mean BMI from the weights and heights of groups or patients.

meanBMIFcn = @(h,w)mean((w ./ (h.^2)) * 703)

meanBMIFcn = functionhandle with value: @(h,w)mean((w./(h.^2))*703)

Create a table that contains the mean BMI for each group.

[G,results] = findgroups(GT); meanBMI = splitapply(meanBMIFcn,DT,G); results.meanBMI = meanBMI

results=6×3 table Location Smoker meanBMI ___________________________ ______ _______

"County General Hospital"      false     23.774 
"County General Hospital"      true      24.865 
"St. Mary's Medical Center"    false     22.968 
"St. Mary's Medical Center"    true      24.905 
"VA Hospital"                  false     23.946 
"VA Hospital"                  true      24.227

Calculate the minimum, mean, and maximum weights for groups of patients and return results in a table.

Load patient data into a table.

load patients T = table(Smoker,Weight)

T=100×2 table Smoker Weight ______ ______

true       176  
false      163  
false      131  
false      133  
false      119  
false      142  
true       142  
false      180  
false      183  
false      132  
false      128  
false      137  
false      174  
true       202  
false      129  
true       181  
  ⋮

Group patient weights by smoker status. The attached supporting function, multiStats, returns the minimum, mean, and maximum values from an input array as three outputs. Apply multiStats to the smokers and nonsmokers. Create a table that contains the outputs from multiStats for each group.

[G,smoker] = findgroups(T.Smoker); [minWeight,meanWeight,maxWeight] = splitapply(@multiStats,T.Weight,G); result = table(smoker,minWeight,meanWeight,maxWeight)

result=2×4 table smoker minWeight meanWeight maxWeight ______ _________ __________ _________

false        111         149.91         194   
true         115         161.94         202

function [lo,avg,hi] = multiStats(x) lo = min(x); avg = mean(x); hi = max(x); end

Input Arguments

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Function to apply to groups of data, specified as a function handle.

If func returns a nonscalar output argument, then the argument must be oriented so that splitapply can concatenate the output arguments from successive calls tofunc. For example, if the input data variables are column vectors, then func must return either a scalar or a row vector as an output argument.

Example: Y = splitapply(@sum,X,G) returns the sums of the groups of data in X.

Data variable, specified as a vector, matrix, or cell array. The elements of X belong to groups specified by the corresponding elements of G.

If X is a matrix, splitapply treats each column or row as a separate data variable. The orientation ofG determines whether splitapply treats the columns or rows of X as data variables.

Group numbers, specified as a vector of positive integers. ForN groups specified by group numbers, every integer between 1 and N must occur at least once in G.

If any elements of G are NaNs, thensplitapply omits the corresponding values inX when it splits X into groups. To include such values, consider using the groupsummary function instead.

If X is a vector or cell array, thenG must be the same length asX.
If X is a matrix and G is a row vector, then the length of G must equal the number of columns of X.
If X is a matrix and G is a column vector, then the length of G must equal the number of rows of X.
If the input argument is table T, thenG must be a column vector. The length ofG must be equal to the number of rows ofT.

Data variables, specified as a table. splitapply treats each table variable as a separate data variable.

More About

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In data analysis, you commonly perform calculations on groups of data. For such calculations, you split one or more data variables into groups of data, perform a calculation on each group, and combine the results into one or more output variables. You can specify the groups using one or more_grouping variables_. The unique values in the grouping variables define the groups that the corresponding values of the data variables belong to.

For example, the diagram shows a simple grouped calculation that splits a 6-by-1 numeric vector into two groups of data, calculates the mean of each group, and then combines the outputs into a 2-by-1 numeric vector. The 6-by-1 grouping variable has two unique values, AB andXYZ.

Calculation that splits a data variable based on a grouping variable, performs calculations on individual groups of data by applying the same function, and then concatenates the outputs of those function calls

You can specify grouping variables that have numbers, text, dates and times, categories, or bins.

Extended Capabilities

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Thesplitapply function supports tall arrays with the following usage notes and limitations:

The specified function must not rely on any state, such as persistent variables or random number functions like rand.

For more information, see Tall Arrays.

Version History

Introduced in R2015b