This example shows how to perform calculations on tables.
The functions rowfun and varfun each apply a specified function to a table, yet many other functions require numeric or homogeneous arrays as input arguments. You can extract data from individual variables using dot indexing or from one or more variables using curly braces. The extracted data is then an array that you can use as input to other functions.
Read data from a comma-separated text file, testScores.csv, into a table using the readtable function. testScores.csv contains test scores for several students. Use the student names in the first column of the text file as row names in the table.
T = readtable('testScores.csv','ReadRowNames',true)
T=10×4 table
Gender Test1 Test2 Test3
__________ _____ _____ _____
HOWARD {'male' } 90 87 93
WARD {'male' } 87 85 83
TORRES {'male' } 86 85 88
PETERSON {'female'} 75 80 72
GRAY {'female'} 89 86 87
RAMIREZ {'female'} 96 92 98
JAMES {'male' } 78 75 77
WATSON {'female'} 91 94 92
BROOKS {'female'} 86 83 85
KELLY {'male' } 79 76 82
T is a table with 10 rows and four variables.
View the data type, description, units, and other descriptive statistics for each variable by using the summary function to summarize the table.
summary(T)
Variables:
Gender: 10x1 cell array of character vectors
Test1: 10x1 double
Values:
Min 75
Median 86.5
Max 96
Test2: 10x1 double
Values:
Min 75
Median 85
Max 94
Test3: 10x1 double
Values:
Min 72
Median 86
Max 98
The summary contains the minimum, median, and maximum score for each test.
Extract the data from the second, third, and fourth variables using curly braces, {}, find the average of each row, and store it in a new variable, TestAvg.
T.TestAvg = mean(T{:,2:end},2)T=10×5 table
Gender Test1 Test2 Test3 TestAvg
__________ _____ _____ _____ _______
HOWARD {'male' } 90 87 93 90
WARD {'male' } 87 85 83 85
TORRES {'male' } 86 85 88 86.333
PETERSON {'female'} 75 80 72 75.667
GRAY {'female'} 89 86 87 87.333
RAMIREZ {'female'} 96 92 98 95.333
JAMES {'male' } 78 75 77 76.667
WATSON {'female'} 91 94 92 92.333
BROOKS {'female'} 86 83 85 84.667
KELLY {'male' } 79 76 82 79
Alternatively, you can use the variable names, T{:,{'Test1','Test2','Test3'}} or the variable indices, T{:,2:4} to select the subset of data.
Compute the mean and maximum of TestAvg by gender of the students.
varfun(@mean,T,'InputVariables','TestAvg',... 'GroupingVariables','Gender')
ans=2×3 table
Gender GroupCount mean_TestAvg
__________ __________ ____________
{'female'} 5 87.067
{'male' } 5 83.4
The maximum score for each test is 100. Use curly braces to extract the data from the table and convert the test scores to a 25 point scale.
T{:,2:end} = T{:,2:end}*25/100T=10×5 table
Gender Test1 Test2 Test3 TestAvg
__________ _____ _____ _____ _______
HOWARD {'male' } 22.5 21.75 23.25 22.5
WARD {'male' } 21.75 21.25 20.75 21.25
TORRES {'male' } 21.5 21.25 22 21.583
PETERSON {'female'} 18.75 20 18 18.917
GRAY {'female'} 22.25 21.5 21.75 21.833
RAMIREZ {'female'} 24 23 24.5 23.833
JAMES {'male' } 19.5 18.75 19.25 19.167
WATSON {'female'} 22.75 23.5 23 23.083
BROOKS {'female'} 21.5 20.75 21.25 21.167
KELLY {'male' } 19.75 19 20.5 19.75
Change the variable name from TestAvg to Final.
T.Properties.VariableNames{end} = 'Final'T=10×5 table
Gender Test1 Test2 Test3 Final
__________ _____ _____ _____ ______
HOWARD {'male' } 22.5 21.75 23.25 22.5
WARD {'male' } 21.75 21.25 20.75 21.25
TORRES {'male' } 21.5 21.25 22 21.583
PETERSON {'female'} 18.75 20 18 18.917
GRAY {'female'} 22.25 21.5 21.75 21.833
RAMIREZ {'female'} 24 23 24.5 23.833
JAMES {'male' } 19.5 18.75 19.25 19.167
WATSON {'female'} 22.75 23.5 23 23.083
BROOKS {'female'} 21.5 20.75 21.25 21.167
KELLY {'male' } 19.75 19 20.5 19.75
findgroups | rowfun | splitapply | summary | table | varfun