A table in MATLAB represents a datatype for collecting heterogeneous elements with its metadata properties, such as variable names, row and column headers, descriptions, and unit of measurement of the variables, all in one container. Tables are particularly suitable for containing tabular data that is often placed in columns in a text file or spreadsheet. Each variable in the table can host a different type of data, but must have the same number of rows. A typical use of a table is to store experimental data, in which the rows represent the different observations of the phenomenon and the columns represent the different measured variables.

Here is a dataset example:

Let's consider now how to create a table from variables in the MATLAB workspace and how to proceed with its visualization. Alternatively, we can use the import tool or the readtable() function to create a table from an existing spreadsheet or text file. When importing data from an external file with these tools, each column becomes a table variable. To get the data to be included in the table, quickly and easily, we can draw on the many examples in the software distribution. In this case, we load in the MATLAB workspace the MAT-file hospital.mat (present in software distribution), which contains simulated hospital data for 100 patients. After doing this we will be able to see all variables contained in the MATLAB workspace (Figure 2.12):

>> load hospital
>> whos
  Name               Size            Bytes  Class      Attributes
  Description        1x23               46  char   
  hospital         100x7             46480  dataset             

We can access variable data, or select a subset of variables, by using variable (column) names and dot indexing. For our needs we will extract individual fields in separate variables:

>> LastName=hospital.LastName;
>> Sex=hospital.Sex;
>> Age=hospital.Age;
>> Weight=hospital.Weight;

Now we will create a table and populate it with data contained in the fields (columns) Gender, Smoker, Height, and Weight already present as work variables, so we will only view the top five rows for space reasons:

>> TablePatients = table(LastName,Sex,Age,Weight);
>> TablePatients(1:5,:)
ans =
  10×4 table
     LastName      Sex      Age    Weight
    __________    ______    ___    ______
    'SMITH'       Male      38     176
    'JOHNSON'     Male      43     163
    'WILLIAMS'    Female    38     131
    'JONES'       Female    40     133
    'BROWN'       Female    49     119  

We can add new fields using dot notation. First, we extract the blood pressure from the starting dataset, dividing it into two variables:

>> BlPrMax=hospital.BloodPressure(:,1);
>> BlPrMin=hospital.BloodPressure(:,2);

 Now add the two new variables to the table:

>> TablePatients.BlPrMax=BlPrMax;
>> TablePatients.BlPrMin=BlPrMin;

To confirm the correctness of the operations, we print the first five rows of the table and only three columns:

>> TablePatients(1:5,[1 5:6])
ans =
  5×3 table
     LastName     BlPrMax    BlPrMin
    __________    _______    _______    
    'SMITH'       124        93                 
    'JOHNSON'     109        77        
    'WILLIAMS'    125        83             
    'JONES'       117        75             
    'BROWN'       122        80 

We can create a summary table to see the datatype, description, units of measurement, and other descriptive statistics for each variable, using the summary() function:

>> summary(TablePatients)
Variables:
    LastName: 100×1 cell array of character vectors
    Sex: 100×1 categorical
        Values:
            Female    53 
            Male      47  
    Age: 100×1 double
        Values:
            Min       25  
            Median    39  
            Max       50  
    Weight: 100×1 double
        Values:
            Min         111  
            Median    142.5  
            Max         202  
    BlPrMax: 100×1 double
        Values:
            Min       109              
            Median    122              
            Max       138              
    BlPrMin: 100×1 double
        Values:
            Min         68             
            Median    81.5             
            Max         99