Basic Heatmap

library(plotly)fig<-plot_ly(z=volcano,type="heatmap")fig

Categorical Axes

m<-matrix(rnorm(9),nrow=3,ncol=3)fig<-plot_ly(x=c("a","b","c"),y=c("d","e","f"),z=m,type="heatmap")fig

Sequential Colorscales: Greys

The colors argument understands color brewer palettes (see RColorBrewer::brewer.pal.info for valid names).

fig<-plot_ly(z=volcano,colors="Greys",type="heatmap")fig

Custom colorscales

The colors argument also accepts a color interpolation function like colorRamp()

fig<-plot_ly(z=volcano,colors=colorRamp(c("red","green")),type="heatmap")fig

Or, you can do the scaling yourself and use the colorscale attribute directly...

vals<-unique(scales::rescale(c(volcano)))o<-order(vals,decreasing=FALSE)cols<-scales::col_numeric("Blues",domain=NULL)(vals)colz<-setNames(data.frame(vals[o],cols[o]),NULL)fig<-plot_ly(z=volcano,colorscale=colz,type="heatmap")fig

What About Dash?

Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash for R at https://dashr.plot.ly/installation.

Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this:

library(plotly)fig<-plot_ly()# fig <- fig %>% add_trace( ... )# fig <- fig %>% layout( ... ) library(dash)library(dashCoreComponents)library(dashHtmlComponents)app<-Dash$new()app$layout(htmlDiv(list(dccGraph(figure=fig))))app$run_server(debug=TRUE,dev_tools_hot_reload=FALSE)