Cells or bins are colored based on data. As with all visualization types that use color as the main visual encoding, choose shades carefully.
There are existing functions and packages to make heatmaps in R, but when the data is irregular, it's worth going custom.
Heatmaps quickly translate data tables into a visual form, making them a great tool to explore a new dataset.
Using color as the visual encoding, show changes over time in two dimensions.
Things have a way of repeating themselves, and it can be useful to highlight these patterns in data.
Add interaction so that you can show different segments of the data and allow comparisons.
You saw how to make basic heat maps a while back, but you might want more flexibility for a specific data set. Once you understand the components of a heat map, the rest is straightforward.
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