plotly.r-docs/ggplot2/2021-08-04-bubblecloud.Rmd at master · plotly/plotly.r-docs · GitHub

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---
name: Bubblecloud Plots
permalink: ggplot2/bubblecloud/
description: How to make a Bubblecloud Plots using ggplotly with Plotly.
layout: base
thumnail_github: bubblecloud.png
language: ggplot2
page_type: u-guide
display_as: basic
order: 21
output:
  html_document:
    keep_md: true
---

```{r, echo = FALSE, message=FALSE}
knitr::opts_chunk$set(message = FALSE, warning=FALSE)
```
### Basic Text Graph
Sources: [International IDEA](https://www.idea.int/data-tools/continent-view/Europe/40?st=par#rep) for national turnout and [European Parliament](https://election-results.eu/turnout/) for European turnout, while regional classifications are based on [EuroVoc](https://publications.europa.eu/en/web/eu-vocabularies/th-concept-scheme/-/resource/eurovoc/100277?target=Browse).

```{r}
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.

```{r}
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.

```{r}
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).

```{r}
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.

```{r}
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
```