Histogram in Matlab

Updated March 28, 2023

Introduction to Histogram in Matlab

Histogram is a representation of any statistical information showing the frequency of data items in successive intervals. MATLAB supports plotting histogram feature that enables the user to create a bar graph for any vector or matrix and grouping the data into bins using an automatic binning algorithm. For each bin, the area represents the frequency of occurrence of the data, not the height. It supports customization in histogram presentation.

Syntax:

In earlier versions, hist() and histc() were used to generate histogram plots. In later versions those functions are replaced with new functions with advanced capabilities i.e. histogram(), histcounts() and discretize().

The syntax for the above-recommended functions are as follows:

• histogram(X, a_1,a_2,…..,a_n)
• histcounts(X, a_1,a_2,…..,a_n)
• discretize(X, a_1,a_2,…..,a_n)

Where X: Data input in the form of vector or matrix.

a_1,a_2,…..,a_n: Attribute values (optional)

Creation of Histogram in Matlab: MATLAB makes it a single click action to create a histogram for any data. A histogram can be created by using the inbuilt function histogram().

Example:

The below code is written to generate 100 random numbers and histogram() is used to plot a histogram for the generated data.

Code:

data = randn(100,1);
h = histogram(data)

Output:

Once any histogram object is created, it can be altered by altering its property values, that makes changes in the properties of bins and thus in the display.

Properties of Histogram in Matlab

Various properties that are featured for the histogram in MATLAB, are as follows:

1. Bins

Parameter	Description
NumBins	Decides the number of bins to be generated.
BinWidth	Decides the width of each bin.
BinEdges	A vector of which the first element of the vector determines the edge of the first bin and the last element decides the edges of the last bin of the histogram.
Binlimits	Sets the limits for the input vector/matrix values.
BinLimitModes	Decides mode of setting the limits.
BinMethod	Choose the algorithm to configure bin width.

2. Categories

This property allows to plot histogram for each category defined in the input categorical array. If bin count is specified, categories sets the associated category descriptions in the plot.

This property contains parameters such as mentioned below:

Parameter	Description
DisplayOrder	Decides the order of bars based on height.
NumDisplayBins	Decides the number of categories to be displayed.
ShowOthers	Decides about the visibility of the additional bar which contains excluded elements of selected categories.

3. Data

This value gets distributed over a histogram plot among the bins. This property consists of different parameters such as:

Parameter	Description
Values	Decides the number of data elements to be added to a specific bin.
Normalization	Applies a specific type of normalization on the data such as count, probability, countdensity, pdf, cumcount, etc.
BinCounts	Accepts the bin count as input from an external bin calculation method instead of histogram data binning.
BinCountsMode	Represents the mode of deciding bin counts.

4. Color and Styling

Parameter	Description
DisplayStyle	Decides the style to impose on the histogram display.
Orientation	Decides upon the orientation of the bars on the histogram plot- vertical or horizontal.
BarWidth	Regulates the separation of categorical bars.
FaceColor	Sets the color of the bars.
EdgeColor	Sets the color of the edges.
FaceAlpha	Decides on the transparency of the bars.
EdgeAlpha	Decides on the transparency of the edges.
LineStyle	Sets style of the bar outlines.
LineWidth	Sets the width of the bar outlines.

5. Legend

This property in the MATLAB adds descriptive labels to the plots. It comprises of:

Parameter	Description
DisplayName	Sets the text to be added to the description for the axes.
Annotation	Controls the inclusion of the objects in a legend and sets excluded objects as an annotation object.

6. Interactivity

Parameter	Description
Visible	Sets the visibility of an object.
DataTipTemplate	Decides on the content that appears on a data tip.
UIContextMenu	Sets the context menu for an object, displayed on right-click over the object.
Selected	Manages the selection mode of the object.
SelectionHighlight	Decides the visibility of selection handlers around an object.

7. Callbacks

Parameter	Description
ButtonDownFcn	Accepts a function as a value which is to be executed when an object is clicked.
CreateFcn	Accepts a function as a value which is to be executed when an object is created.
DeleteFcn	Accepts a function as a value which is to be executed when an object is deleted.

8. Callback Execution Control

Parameter	Description
Interruptible	Determines whether the callback function can be interrupted or not.
BusyAction	Determines how the interruption in callback function will be handled.
PickableParts	Sets the context menu for an object, displayed on right-click over the object.
Selected	Used to enable/disable capturing mouse clicks.
HitTest	Decides response on the captured mouse clicks on the Histogram plot.
BeingDeleted	Used to store the status of the execution of DeleteFcn callback.

9. Parent/Child

Parameter	Description
Parent	Axes, Polar axes, Transform objects or Group objects are specified as a parent.
Child	This property is a read-only element which is used to view a list of data tips that are plotted in the histogram.
HandleVisibility	Accepts a function as a value which is to be executed when an object is deleted.

10. Identifiers

Parameter	Description
Type	Represents the type of graphic object.
Tag	Serves as an object identifier.
UserData	Stores arbitrary data on an object.

Examples of Histogram in Matlab

Let’s understand the usage of different attributes referring to various examples given below:

Example #1 – Changing Bin Counts

Code:

data = randn(100,1);
nbins = 10;
h = histogram(data,nbins)

Output:

Example #2 – Changing Bin Width

Code:

data = randn(100,1);
histogram(data,'BinWidth',2)

Output:

Example #3 – Changing Normalization Type

Code:

data = randn(100,1);
h = histogram(data,'Normalization','countdensity')

Output:

Example #4 – Changing Display Style

Code:

data = randn(100,1);
h = histogram(data,'DisplayStyle','stairs')

Output:

Example #5 – Changing Color of the Bars

Code:

data = randn(100,1);
h = histogram(data,'FaceColor','#A2142F')

Output:

A histogram plot lets you to understand and to analyze the set of continuous data under a frequency distribution. It is advantageous over a bar chart as it allows to divide data into classes in terms of bins which helps to do inspection over a specific category of data as required.

Additional Note:

• The function histogram() creates a histogram object having modifiable properties within.
• Histogram() and histcount() have common built-in options, automatic binning and normalization features.
• The primary calculation function for histogram i.e. histcounts() exhibits consistent behavior.
• Discretize() has extended feature about deciding placements of the bin for each element.

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