heatmap

Create a heatmap from a table of data for medical patients.

Load the patients data set and create a table from a subset of the variables loaded into the workspace. Then create a heatmap that counts the total number of patients with the same set of Smoker and SelfAssessedHealthStatus values.

load patients
tbl = table(LastName,Age,Gender,SelfAssessedHealthStatus,...
    Smoker,Weight,Location);
h = heatmap(tbl,'Smoker','SelfAssessedHealthStatus');

Create a heatmap and reorder the labels along the y-axis.

Load the patients data set and create a heatmap from the data. Assign the HeatmapChart object to the variable h.

load patients
tbl = table(LastName,Age,Gender,SelfAssessedHealthStatus,...
    Smoker,Weight,Location);
h = heatmap(tbl,'Smoker','SelfAssessedHealthStatus');

Reorder the labels along the y-axis.

h.YDisplayData = {'Excellent','Good','Fair','Poor'};

Alternatively, you can reorder the labels by changing the data to categorical data and then reordering the categories using the reordercats function. Similarly, you can add, remove, or rename the heatmap labels using the addcats, removecats, or renamecats functions, respectively.

Create a heatmap and specify the table variable to use when determining the heatmap cell colors.

Load the patients data set and create a heatmap from the data. Color each cell using the average age of patients with a particular pair of Smoker and SelfAssessedHealthStatus values by setting the ColorVariable option to 'Age'.

load patients
tbl = table(LastName,Age,Gender,SelfAssessedHealthStatus,...
    Smoker,Weight,Location);

h = heatmap(tbl,'Smoker','SelfAssessedHealthStatus',...
    'ColorVariable','Age');

Create a heatmap and specify the table variable and calculation method to use when determining the heatmap cell colors.

Load the patients data set and create a heatmap from the data. Color each cell using the median age of patients with a particular pair of Smoker and SelfAssessedHealthStatus values. Specify the ColorVariable option as 'Age' and the ColorMethod option as 'median'.

load patients
tbl = table(LastName,Age,Gender,SelfAssessedHealthStatus,...
    Smoker,Weight,Location);
h = heatmap(tbl,'Smoker','SelfAssessedHealthStatus',...
    'ColorVariable','Age','ColorMethod','median');

Create a matrix of data. Then create a heatmap of the matrix values. The default labels along the x-axis and y-axis appear as 1, 2, 3, and so on.

cdata = [45 60 32; 43 54 76; 32 94 68; 23 95 58];
h = heatmap(cdata);

Create a matrix of data. Then create a heatmap of the matrix values. Use custom labels along the x-axis and y-axis by specifying the first two input arguments as the labels you want. Specify the title and axis labels by setting properties of the HeatmapChart object.

cdata = [45 60 32; 43 54 76; 32 94 68; 23 95 58];
xvalues = {'Small','Medium','Large'};
yvalues = {'Green','Red','Blue','Gray'};
h = heatmap(xvalues,yvalues,cdata);

h.Title = 'T-Shirt Orders';
h.XLabel = 'Sizes';
h.YLabel = 'Colors';

Since R2023b

To use LaTeX (or TeX) markup in the title, axis labels, or data tips, set the Interpreter property of the HeatmapChart object.

For example, create a heatmap chart with a title that uses LaTeX to display Greek letters. Set the Interpreter property to "latex" when you create the chart. Then include the markup when you call the title function.

heatmap(randi(100,5,3),Interpreter="latex")
title("$\hat{\psi_1}$ Values for ${1\le\alpha\le3}$")

Create a heatmap and normalize the colors along each column or row by setting the ColorScaling property.

Read the sample file outages.csv into a table. The sample file contains data representing electric utility outages in the Unites States. The table contains six columns: Region, OutageTime, Loss, Customers, RestorationTime, and Cause. Display the first five rows of each column.

T = readtable('outages.csv');
T(1:5,:)

ans=5×6 table
       Region           OutageTime        Loss     Customers     RestorationTime            Cause       
    _____________    ________________    ______    __________    ________________    ___________________

    {'SouthWest'}    2002-02-01 12:18    458.98    1.8202e+06    2002-02-07 16:50    {'winter storm'   }
    {'SouthEast'}    2003-01-23 00:49    530.14    2.1204e+05                 NaT    {'winter storm'   }
    {'SouthEast'}    2003-02-07 21:15     289.4    1.4294e+05    2003-02-17 08:14    {'winter storm'   }
    {'West'     }    2004-04-06 05:44    434.81    3.4037e+05    2004-04-06 06:10    {'equipment fault'}
    {'MidWest'  }    2002-03-16 06:18    186.44    2.1275e+05    2002-03-18 23:23    {'severe storm'   }

Create a heatmap that shows the different regions along the x-axis and the different outage causes along the y-axis. In each cell, show how many times each region experienced a power outage due to a particular cause.

h = heatmap(T,'Region','Cause');

Normalize the colors along each column. The smallest value in each column maps to the first color in the colormap and the largest value maps to the last color. The last color indicates the cause that caused the most power outages for each region.

h.ColorScaling = 'scaledcolumns';

Normalize the colors along each row instead. The smallest value in each row maps to the first color in the colormap and the largest value maps to the last color. The last color indicates the region that experienced the most power outages due to each cause.

h.ColorScaling = 'scaledrows';

Create a heatmap and color the cells using data computed with a custom aggregation method. Use the pivot function to compute the color data.

Read the sample file TemperatureData.csv into a table. The file contains four columns: Year, Month, Day, and TemperatureF.

tbl = readtable('TemperatureData.csv');

Compute the color data as the maximum temperature for each month and year combination using the pivot function. Use NaN for missing month and year combinations.

P = pivot(tbl,Columns="Month",Rows="Year",DataVariable="TemperatureF",Method=@(x) mean(x,"includenan"))

P=2×13 table
    Year    April     August    December    February    January     July      June     March      May      November    October    September
    ____    ______    ______    ________    ________    _______    ______    ______    ______    ______    ________    _______    _________

    2015    43.267    69.903     40.548      15.448     21.355     69.161    59.967    28.484    57.548      43.8      49.323      64.467  
    2016    42.267       NaN        NaN      29.966      27.71     68.824    63.733    37.806    53.806       NaN         NaN         NaN  

Rearrange the variables in the pivoted table.

P = P(:,["Year" "January" "February" "March" "April" "May" "June" "July" "August" "September" "October" "November" "December"]);

Create the heatmap, and color the heatmap cells using the computed data values.

xvar = P.Properties.VariableNames(2:end);
yvar = string(P.(1));
cvar = P{:,2:end};
heatmap(xvar,yvar,cvar)
title("Maximum Temperatures (F)")

Heatmap charts use the 256-sampling of the sky colormap by default. You can change the sampling by specifying the Colormap name-value argument.

For example, create two heatmap charts in a 2-by-1 tiled chart layout. The top chart uses the default set of 256 colors, and the bottom chart uses only two colors.

tiledlayout(2,1,TileSpacing="compact")

% Top chart with default colors
nexttile
heatmap(magic(10))

% Bottom chart with two colors
nexttile
heatmap(magic(10),Colormap=sky(2))