Split data into groups and apply function (2024)

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 patientswhos 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 = findgroups(Smoker)

G = 100×1 2 1 1 1 1 1 2 1 1 1 ⋮

Display the weights of the patients.

Weight

Weight = 100×1 176 163 131 133 119 142 142 180 183 132 ⋮

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 patientswhos 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 = @(x,y) var(x-y)

func = function_handle 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 = function_handle 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 patientswhos Weight Location Smoker

 Name Size Bytes Class Attributes Location 100x1 14208 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 patientsDT = 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 = function_handle 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 patientsT = 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