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Heatmaps visualise data through variations in colouring. When applied to a tabular format, Heatmaps are useful for cross-examining multivariate data, through placing variables in the rows and columns and colouring the cells within the table. Heatmaps are good for showing variance across multiple variables, revealing any patterns, displaying whether any variables are similar to each other, and for detecting if any correlations exist.
Typically, all the rows are one category (labels displayed on the left or right side) and all the columns are another category (labels displayed on the top or bottom). The individual rows and columns are divided into the subcategories, which all match up with each other in a matrix. The cells are the intersections of the rows and columns, which can either contain categorical data or numerical data.
A legend is required alongside a Heatmap in order for it to be successfully read. Categorical data is colour-coded, while numerical data requires a colour scale that blends from one colour to another, in order to represent the difference in high and low values. A selection of solid colours can be used to represent multiple value ranges (0-10, 11-20, 21-30, etc) or you can use a gradient scale for a single range (for example 0 - 100) by blending two or more colours together.
Because of their reliance on colour to communicate values, Heatmaps are a chart better suited to displaying a more generalised view of numerical data, as it’s harder to accurately tell the differences between colour shades and to extract specific data points from. However, this problem can be remedied by displaying the data values inside the cells.
Heatmaps can also be used to show the changes in data over time if one of the rows or columns are set to time intervals. An example of this would be to use a Heatmap to compare the temperature changes across the year in multiple cities, to see where’s the hottest or coldest places. So the rows could list the cities to compare, the columns contain each month and the cells would contain the temperature values.
Data over time
Tools to Generate Visualisation
D3.js Graph Gallery (D3.js)
Python Graph Gallery (Python: seaborn)
React Graph Gallery (React/JS)
R Graph Gallery (R: lattice)
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