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# geom_bin2d in ggplot2

How to make a 2-dimensional heatmap in ggplot2 using geom_bin2d. Examples of coloured and facetted graphs.

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### Basic 2d Heatmap

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

library(plotly)

p <- ggplot(english_french, aes(x=engperc,y=frenperc)) +
geom_bin2d() +
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)

p <- ggplot(english_french, aes(x=engperc,y=frenperc)) +
geom_bin2d() +
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)

p <- ggplot(english_french, aes(x=engperc, y=frenperc, weight=total)) +
geom_bin2d() +
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


### With Facets

We can facet the graphic with the "region" column, and set "bins" to 20, so that the graph is 20 x 20 sides.

library(plotly)

p <- ggplot(english_french, aes(x=engperc,y=frenperc, weight=total)) +
geom_bin2d(bins = 20) +
facet_wrap(~factor(region, levels = c("Atlantic","Quebec","Ontario","Prairies","British Columbia"))) +
labs(title = "Distribution of Canadian towns 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.

library(plotly)

p <- ggplot(english_french, aes(x=engperc,y=frenperc, weight=total)) +
geom_bin2d(bins = 20) +
facet_wrap(~factor(region, levels = c("Atlantic","Quebec","Ontario","Prairies","British Columbia"))) +
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


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)
)
)
)