Bubblecloud Plots in ggplot2

How to make a Bubblecloud Plots using ggplotly with Plotly.

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Basic Text Graph

Sources: International IDEA for national turnout and European Parliament for European turnout, while regional classifications are based on EuroVoc.

recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))

library(plotly)
p <- recent_turnout %>%
  ggplot(aes(x=nat_turnout,y=euro_turnout)) + 
  geom_text(aes(size=population/3.5, label=abbreviation, colour=region), alpha=1) +
  labs(title = "Recent turnout in European Union countries",
       x = "Latest legislative or presidential election (whichever had higher turnout)",
       y = "May 2019 European Parliament election")
fig <- ggplotly(p)

fig

Overlaid Points

Colour-coding the text itself might present readability issues. Another possible use of geom_text is to keep the text grey, but overlay it on a coloured point graph.

Adding the text option within aes() allows us to control the text that appears when hovering over a point.

recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))

library(plotly)
p <- recent_turnout %>%
  ggplot(aes(x=nat_turnout,y=euro_turnout)) + 
  geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
  geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
  labs(title = "Recent turnout in European Union countries",
       x = "Latest legislative or presidential election (whichever had higher turnout)",
       y = "May 2019 European Parliament election")
fig <- ggplotly(p)

fig

Customed Colour and Size Scale

Let's use the LaCroixColoR package to spruce up the colour scheme. In addition, by using scale_size_continuous, we can make sure that none of the text is too small.

recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))

library(plotly)
library(LaCroixColoR)
p <- recent_turnout %>%
  ggplot(aes(x=nat_turnout,y=euro_turnout)) + 
  geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
  geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
  scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
  scale_size_continuous(range = c(3, 8)) +
  labs(title = "Recent turnout in European Union countries",
       x = "Latest legislative or presidential election (whichever had higher turnout)",
       y = "May 2019 European Parliament election")
fig <- ggplotly(p)

fig

Adding a regression

Adding a regression line as well as a label. geom_smooth does not allow for adjusting the transparency of the line (using alpha), which is why stat_smooth is used here. annotate is used to include a single text label (geom_text would create one label for every data point, all overlapped with each other).

recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))
m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)

library(plotly)
library(LaCroixColoR)
p <- recent_turnout %>%
  ggplot(aes(x=nat_turnout,y=euro_turnout)) + 
  stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) + 
  geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
  geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1) +
  scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
  scale_size_continuous(range = c(3, 8)) +
  labs(title = "Recent turnout in European Union countries",
       x = "Latest legislative or presidential election (whichever had higher turnout)",
       y = "May 2019 European Parliament election") +
  annotate(geom="text", x=60, y=80, label = paste("European turnout = \n",
                                                  round(unname(coef(m)[2]),2),
                                                  "x national turnout",
                                                  round(unname(coef(m)[1]),1)))
fig <- ggplotly(p)

fig

Customized Formatting

Changed the font of the geom_text and of the graph (these must be done separately!), corrected the size label, centre-aligned the title.

recent_turnout <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/european_turnout.csv",stringsAsFactors = FALSE)
recent_turnout$region <- factor(recent_turnout$region, levels=c("British","Northern","Western","Mediterranean","Central/Eastern"))
m <- lm(euro_turnout ~ nat_turnout, data = recent_turnout)

library(plotly)
library(LaCroixColoR)
p <- recent_turnout %>%
  ggplot(aes(x=nat_turnout,y=euro_turnout)) + 
  stat_smooth(geom="line", method="lm", alpha=0.3, se=FALSE) + 
  geom_point(aes(size=population, colour=region, text=paste("country:", country)), alpha=0.4) +
  geom_text(aes(size=population/3.5, label=abbreviation), colour="gray20", alpha=1, family="Fira Sans") +
  scale_colour_manual(values=lacroix_palette(n=6, name="PeachPear")) +
  scale_size_continuous(range = c(3, 8)) +
  labs(title = "Recent turnout in European Union countries",
       x = "Latest legislative or presidential election (whichever had higher turnout)",
       y = "May 2019 European Parliament election",
       size = "") +
  annotate(geom="text", x=60, y=80, label = paste("European turnout = \n",
                                                  round(unname(coef(m)[2]),2),
                                                  "x national turnout",
                                                  round(unname(coef(m)[1]),1))) +
  theme(plot.title = element_text(hjust = 0.5)) +
  guides(size=guide_legend(""), fill = FALSE) +
  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)