Visualize the distribution of data using plots such as histograms, pie charts, or word clouds. For example, use a histogram to group data into bins and display the number of elements in each bin.
|Bivariate histogram plot|
|Increase number of histogram bins|
|Decrease number of histogram bins|
|Histogram bin counts|
|Bivariate histogram bin counts|
|3-D scatter plot|
|Binned scatter plot|
|Create scatter plot with histograms|
|Visualize sparsity pattern|
|Scatter plot matrix|
|Histogram Properties||Histogram appearance and behavior|
|Histogram2 Properties||Histogram2 appearance and behavior|
|Scatter Properties||Scatter chart appearance and behavior|
|ScatterHistogramChart Properties||Control scatter histogram chart appearance and behavior|
|Binscatter Properties||Binscatter appearance and behavior|
|HeatmapChart Properties||Heatmap chart appearance and behavior|
|WordCloudChart Properties||Control word cloud chart appearance and behavior|
|ParallelCoordinatesPlot Properties||Control parallel coordinates plot appearance and behavior|
This example shows how to add a legend to a pie chart that displays a description for each slice.
This example shows how to create a pie graph and automatically offset the pie slice with the greatest contribution.
When you create a pie chart, MATLAB labels each pie slice with the percentage of the whole that slice represents.
This example shows how to adjust the color scale of a bivariate histogram plot to reveal additional details about the bins.
When you use the Data Cursor tool on a histogram plot, it customizes the data tips it displays in an appropriate way.
This example shows how to use
histogram to effectively view categorical data.
This example shows how to create a heatmap from a table and how to modify the heatmap appearance.
This example shows how to create a word cloud from plain text by reading it into a string array, preprocessing it, and passing it to the
This example shows how to create a parallel coordinates plot from a table and how to modify the appearance of the plot.
discretize are the recommended histogram creation and
computation functions for new code.