A cluster heatmap is a popular graphical method for visualizing high dimensional data. In it, a table of numbers is scaled and encoded as a tiled matrix of colored cells. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by dendrograms and extra columns of categorical annotation. The ongoing development of this iconic visualization, spanning over more than a century, has provided the foundation for one of the most widely used of all bioinformatics displays (Wilkinson and Friendly, 2009). When using the R language for statistical computing (R Core Team, 2016), there are many available packages for producing static heatmaps, such as: stats, gplots, heatmap3, fheatmap, pheatmap and others. Recently released packages also allow for more complex layouts; these include gapmap, superheat and ComplexHeatmap (Gu et al., 2016). The next evolutionary step has been to create interactive cluster heatmaps, and several solutions are already available. However, these solutions, such as the idendro R package (Sieger et al., 2017), are often focused on providing an interactive output that can be explored only on the researcher's personal computer. Some solutions do exist for creating shareable interactive heatmaps. However, these are either dependent on a specific online provider, such as XCMS Online, or require JavaScript knowledge to operate, such as InCHlib. In practice, when publishing in academic journals, the reader is left with a static figure only (often in a png or pdf format).