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Heatmaps in ggplot2

How to make Heatmaps plots in ggplot2 with Plotly.


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Plotly is a free and open-source graphing library for R. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

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.

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_bin2d() +
  labs(title = "Distribution of Canadian areas by English and French fluency",
       x = "% fluent in English",
       y = "% fluent in French",
       fill = "# of census \nsubdivisions")
ggplotly(p)

geom_raster creates a coloured heatmap, with two variables acting as the x- and y-coordinates and a third variable mapping onto a colour. (It is coded similarly to geom_tile and is generated more quickly.) This uses the volcano dataset that comes pre-loaded with R.

library(reshape2)
library(plotly)

df <- melt(volcano)

p <- ggplot(df, aes(Var1, Var2)) +
  geom_raster(aes(fill=value)) +
  labs(x="West to East",
       y="North to South",
       title = "Elevation map of Maunga Whau")

ggplotly(p)

Customized Colours

This uses the Spectral palette from ColorBrewer; a full list of palettes is here.

library(reshape2)
library(plotly)

df <- melt(volcano)

p <- ggplot(df, aes(Var1, Var2)) +
  geom_raster(aes(fill=value)) +
  scale_fill_distiller(palette = "Spectral", direction = -1) +
  labs(x="West to East",
       y="North to South",
       title = "Elevation map of Maunga Whau",
       fill = "Elevation") +
  theme(text = element_text(family = 'Fira Sans'),
        plot.title = element_text(hjust = 0.5))

ggplotly(p)

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() +
  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")
ggplotly(p)

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() +
  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

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","Québec","Ontario","Prairies","British Columbia"))) +
  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")
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","Québec","Ontario","Prairies","British Columbia"))) +
  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

Basic geom_tile graph

This graph, compiled by Jeff Zimmerman, shows how often hitters swing and miss at fastballs, based on their velocity and spin rate. Colour schemes are from ColorBrewer; a complete list of palettes is available here.

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

p <- ggplot(spinrates, aes(x=velocity, y=spinrate)) +
  geom_tile(aes(fill = swing_miss)) +
  scale_fill_distiller(palette = "YlGnBu") +
  labs(title = "Likelihood of swinging and missing on a fastball",
       y = "spin rate (rpm)")

ggplotly(p)

Adjusting appearance

The direction option sets which side of the colour scheme maps onto the low values and which side maps onto the high; it defaults to -1 but could be adjusted to 1.

Also adjusted the theme.

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

p <- ggplot(spinrates, aes(x=velocity, y=spinrate)) +
  geom_tile(aes(fill = swing_miss)) +
  scale_fill_distiller(palette = "YlGnBu", direction = 1) +
  theme_light() +
  labs(title = "Likelihood of swinging and missing on a fastball",
       y = "spin rate (rpm)")

ggplotly(p)

geom_tile with viridis colour scheme

Viridis colour schemes are uniform in both colour and black-and-white, as well as for those with colour-blindness. There are five colour schemes: "magma" (or "A"), "inferno" (or "B"), "plasma" (or "C"), "viridis" (or "D", the default option) and "cividis" (or "E").

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

p <- ggplot(spinrates, aes(x=velocity, y=spinrate)) +
  geom_tile(aes(fill = swing_miss)) +
  scale_fill_viridis_c(option = "B", direction = -1) +
  labs(title = "Likelihood of swinging and missing on a fastball",
       y = "spin rate (rpm)") +
  theme_light()

ggplotly(p)

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)