Advantages of Using Tables

Conveniently Store Mixed-Type Data in Single Container

You can use the table data type to collect mixed-type data and metadata properties, such as variable name, row names, descriptions, and variable units, in a single container. Tables are suitable for column-oriented or tabular data that is often stored as columns in a text file or in a spreadsheet. For example, you can use a table to store experimental data, with rows representing different observations and columns representing different measured variables.

Tables consist of rows and column-oriented variables. Each variable in a table can have a different data type and a different size, but each variable must have the same number of rows.

For example, load sample patients data.

load patients

Then, combine the workspace variables, Systolic and Diastolic into a single BloodPressure variable and convert the workspace variable, SelfAssessedHealthStatus, from a cell array of character vectors to a categorical array.

BloodPressure = [Systolic Diastolic];
SelfAssessedHealthStatus = categorical(SelfAssessedHealthStatus);

whos("Age","Smoker","BloodPressure","SelfAssessedHealthStatus")

  Name                            Size            Bytes  Class          Attributes

  Age                           100x1               800  double                   
  BloodPressure                 100x2              1600  double                   
  SelfAssessedHealthStatus      100x1               560  categorical              
  Smoker                        100x1               100  logical                  

The variables Age, BloodPressure, SelfAssessedHealthStatus, and Smoker have varying data types and are candidates to store in a table since they all have the same number of rows, 100.

Now, create a table from the variables and display it.

T = table(Age,Smoker,BloodPressure,SelfAssessedHealthStatus)

T=100×4 table
    Age    Smoker    BloodPressure    SelfAssessedHealthStatus
    ___    ______    _____________    ________________________

    38     true       124     93             Excellent        
    43     false      109     77             Fair             
    38     false      125     83             Good             
    40     false      117     75             Fair             
    49     false      122     80             Good             
    46     false      121     70             Good             
    33     true       130     88             Good             
    40     false      115     82             Good             
    28     false      115     78             Excellent        
    31     false      118     86             Excellent        
    45     false      114     77             Excellent        
    42     false      115     68             Poor             
    25     false      127     74             Poor             
    39     true       130     95             Excellent        
    36     false      114     79             Good             
    48     true       130     92             Good             
      ⋮

The table displays in a tabular format with the variable names at the top.

Each variable in a table is a single data type. If you add a new row to the table, MATLAB® forces consistency of the data type between the new data and the corresponding table variables. For example, if you try to add information for a new patient where the first column contains the patient's health status instead of age, as in the expression T(end+1,:) = {"Poor",true,[130 84],37}, then you receive the error:

Right hand side of an assignment to a categorical array must be a categorical or text representing a category name.

The error occurs because MATLAB® cannot assign numeric data, 37, to the categorical array, SelfAssessedHealthStatus.

For comparison of tables with structures, consider the structure array, StructArray, that is equivalent to the table, T.

StructArray = table2struct(T)

StructArray=100×1 struct array with fields:
    Age
    Smoker
    BloodPressure
    SelfAssessedHealthStatus

Structure arrays organize records using named fields. Each field's value can have a different data type or size. Now, display the named fields for the first element of StructArray.

StructArray(1)

ans = struct with fields:
                         Age: 38
                      Smoker: 1
               BloodPressure: [124 93]
    SelfAssessedHealthStatus: Excellent

Fields in a structure array are analogous to variables in a table. However, unlike with tables, you cannot enforce homogeneity within a field. For example, you can have some values of S.SelfAssessedHealthStatus that are categorical array elements, such as Poor or Good, others that are strings, such as "Poor" and "Good", and others that are integers, such as 0 or 1.

Now consider the same data stored in a scalar structure, with four fields each containing one variable from the table.

ScalarStruct = struct(...
    "Age",Age,...
    "Smoker",Smoker,...
    "BloodPressure",BloodPressure,...
    "SelfAssessedHealthStatus",SelfAssessedHealthStatus)

ScalarStruct = struct with fields:
                         Age: [100x1 double]
                      Smoker: [100x1 logical]
               BloodPressure: [100x2 double]
    SelfAssessedHealthStatus: [100x1 categorical]

Unlike with tables, you cannot enforce that the data is rectangular. For example, the field ScalarStruct.Age can be a different length than the other fields.

A table allows you to maintain the rectangular structure (like a structure array) and enforce homogeneity of variables (like fields in a scalar structure). Although cell arrays do not have named fields, they have many of the same disadvantages as structure arrays and scalar structures. If you have rectangular data that is homogeneous in each variable, consider using a table. Then you can use numeric or named indexing, and you can use table properties to store metadata.

Access Data Using Numeric or Named Indexing

You can index into a table using parentheses, curly braces, or dot indexing. Parentheses allow you to select a subset of the data in a table and preserve the table container. Curly braces and dot indexing allow you to extract data from a table. Within each table indexing method, you can specify the rows or variables to access by name or by numeric index.

Consider the sample table from above. Each row in the table, T, represents a different patient. The workspace variable, LastName, contains unique identifiers for the 100 rows. Add row names to the table by setting the RowNames property to LastName and display the first five rows of the updated table.

T.Properties.RowNames = LastName;
T(1:5,:)

ans=5×4 table
                Age    Smoker    BloodPressure    SelfAssessedHealthStatus
                ___    ______    _____________    ________________________

    Smith       38     true       124     93             Excellent        
    Johnson     43     false      109     77             Fair             
    Williams    38     false      125     83             Good             
    Jones       40     false      117     75             Fair             
    Brown       49     false      122     80             Good             

In addition to labeling the data, you can use row and variable names to access data in the table. For example, use named indexing to display the age and blood pressure of the patients Williams and Brown.

T(["Williams","Brown"],["Age","BloodPressure"])

ans=2×2 table
                Age    BloodPressure
                ___    _____________

    Williams    38      125     83  
    Brown       49      122     80  

Now, use numeric indexing to return an equivalent subtable. Return the third and fifth rows from the first and third variables.

T([3 5],[1 3])

ans=2×2 table
                Age    BloodPressure
                ___    _____________

    Williams    38      125     83  
    Brown       49      122     80  

With cell arrays or structures, you do not have the same flexibility to use named or numeric indexing.

• With a cell array, you must use strcmp to find desired named data, and then you can index into the array.

• With a scalar structure or structure array, it is not possible to refer to a field by number. Furthermore, with a scalar structure, you cannot easily select a subset of variables or a subset of observations. With a structure array, you can select a subset of observations, but you cannot select a subset of variables.

• With a table, you can access data by named index or by numeric index. Furthermore, you can easily select a subset of variables and a subset of rows.

For more information on table indexing, see Access Data in Tables.

Use Table Properties to Store Metadata

In addition to storing data, tables have properties to store metadata, such as variable names, row names, descriptions, and variable units. You can access a property using T.Properties.PropName, where T is the name of the table and PropName is the name of a table property.

For example, add a table description, variable descriptions, and variable units for Age.

T.Properties.Description = "Simulated Patient Data";

T.Properties.VariableDescriptions = ...
    ["" ...
     "true or false" ...
     "Systolic/Diastolic" ...
     "Status Reported by Patient"];

T.Properties.VariableUnits("Age") = "Yrs";

Individual empty strings within VariableDescriptions indicate that the corresponding variable does not have a description. For more information, see the Properties section of table.

To print a table summary, use the summary function.

summary(T)

Description:  Simulated Patient Data

Variables:

    Age: 100x1 double

        Properties:
            Units:  Yrs
        Values:

            Min          25   
            Median       39   
            Max          50   

    Smoker: 100x1 logical

        Properties:
            Description:  true or false
        Values:

            True        34   
            False       66   

    BloodPressure: 100x2 double

        Properties:
            Description:  Systolic/Diastolic
        Values:
                      Column 1    Column 2
                      ________    ________

            Min         109           68  
            Median      122         81.5  
            Max         138           99  

    SelfAssessedHealthStatus: 100x1 categorical

        Properties:
            Description:  Status Reported by Patient
        Values:

            Excellent       34   
            Fair            15   
            Good            40   
            Poor            11   

Structures and cell arrays do not have properties for storing metadata.