geom_hex in ggplot2

How to make a hexagonal two-dimensional heatmap in ggplot2 using geom_hex. Examples of coloured and facetted graphs.

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Basic 2d Heatmap

See also geom_bin2d for a similar geom with rectangular bins. Note: facetting is supported in geom_bin2d but not geom_hex.

Source: Department of Canadian Heritage

library(plotly)

english_french <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/english_french.csv",stringsAsFactors = FALSE)

p <- ggplot(english_french, aes(x=engperc,y=frenperc)) + 
  geom_hex() +
  labs(title = "Distribution of Canadian areas by English and French fluency",
       x = "% fluent in English",
       y = "% fluent in French",
       fill = "# of census \nsubdivisions")
fig <- ggplotly(p)

fig

Customized Colours

Let's flip the colour scheme so that lighter colours denote larger numbers than darker colours. We should also move to a logarithmic scale, since as it is, the very large value in the bottom right overshadows all other values.

library(plotly)

english_french <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/english_french.csv",stringsAsFactors = FALSE)

p <- ggplot(english_french, aes(x=engperc,y=frenperc)) + 
  geom_hex() +
  scale_fill_gradient(low="lightblue1",high="darkblue",trans="log10") +
  labs(title = "Distribution of Canadian towns by English and French fluency",
       x = "% fluent in English",
       y = "% fluent in French",
       fill = "# of census \nsubdivisions")
fig <- ggplotly(p)

fig

Weighted Data

In the previous graphs, each observation represented a single census subdivision - this counted small towns of 500 people equally with cities like Montreal and Toronto. We can weight the data by the "total" column (i.e. total population) to make this a graph of population.

library(plotly)

english_french <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/english_french.csv",stringsAsFactors = FALSE)

p <- ggplot(english_french, aes(x=engperc, y=frenperc, weight=total)) + 
  geom_hex() +
  scale_fill_gradient(low="lightblue1",high="darkblue",trans="log10") +
  labs(title = "Distribution of the Canadian population by English and French fluency",
       x = "% fluent in English",
       y = "% fluent in French",
       fill = "population")
fig <- ggplotly(p)

fig

Customized Appearance

We can modify the graph's appearance - for example, if the grey background makes it difficult to make out the paler shades of blue, we can change the theme to one with a white background. Included also is a way to change the font. You can find a list here of all the theme elements that you can modify.

library(plotly)

english_french <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/english_french.csv",stringsAsFactors = FALSE)

p <- ggplot(english_french, aes(x=engperc,y=frenperc, weight=total)) + 
  geom_hex(bins = 20) +
  scale_fill_gradient(low="lightblue1",high="darkblue",trans="log10") +
  labs(title = "Distribution of Canadian towns by English and French fluency",
       x = "% fluent in English",
       y = "% fluent in French",
       fill = "population") +
  theme_bw() +
  theme(text = element_text(family = 'Fira Sans'))
fig <- ggplotly(p)

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)