R > Basic Charts > Bubble Charts
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Bubble Charts in R

How to make a bubble chart in R. A bubble chart is a scatter plot whose markers have variable color and size.


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Simple Bubble Chart

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers',
        marker = list(size = ~Gap, opacity = 0.5))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE))

fig
6070809010011080100120140160
Gender Gap in Earnings per UniversityWomenMen

Setting Markers Color

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers',
        marker = list(size = ~Gap, opacity = 0.5, color = 'rgb(255, 65, 54)'))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE))

fig
6070809010011080100120140160
Gender Gap in Earnings per UniversityWomenMen

Setting Multiple Colors

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

colors <- c('rgba(204,204,204,1)', 'rgba(222,45,38,0.8)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)',
            'rgba(204,204,204,1)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)',
            'rgba(204,204,204,1)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)',
            'rgba(204,204,204,1)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)', 'rgba(204,204,204,1)',
            'rgba(204,204,204,1)')
# Note: The colors will be assigned to each observations based on the order of the observations in the dataframe.


fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers',
        marker = list(size = ~Gap, opacity = 0.5, color = colors))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE))

fig
6070809010011080100120140160
Gender Gap in Earnings per UniversityWomenMen

Mapping a Color Variable (Continuous)

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers', color = ~Gap, colors = 'Reds',
        marker = list(size = ~Gap, opacity = 0.5))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE))

fig
6070809010011080100120140160
1020304050GapGender Gap in Earnings per UniversityWomenMen

Mapping a Color Variable (Categorical)

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

data$State <- as.factor(c('Massachusetts', 'California', 'Massachusetts', 'Pennsylvania', 'New Jersey', 'Illinois', 'Washington DC',
                          'Massachusetts', 'Connecticut', 'New York', 'North Carolina', 'New Hampshire', 'New York', 'Indiana',
                          'New York', 'Michigan', 'Rhode Island', 'California', 'Georgia', 'California', 'California'))

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers', size = ~Gap, color = ~State, colors = 'Paired',
        marker = list(opacity = 0.5, sizemode = 'diameter'))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE),
         showlegend = FALSE)

fig
6070809010011012080100120140160180
Gender Gap in Earnings per UniversityWomenMen

Scaling the Size of Bubble Charts

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

data$State <- as.factor(c('Massachusetts', 'California', 'Massachusetts', 'Pennsylvania', 'New Jersey', 'Illinois', 'Washington DC',
                          'Massachusetts', 'Connecticut', 'New York', 'North Carolina', 'New Hampshire', 'New York', 'Indiana',
                          'New York', 'Michigan', 'Rhode Island', 'California', 'Georgia', 'California', 'California'))

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers', size = ~Gap, color = ~State, colors = 'Paired',
        #Choosing the range of the bubbles' sizes:
        sizes = c(10, 50),
        marker = list(opacity = 0.5, sizemode = 'diameter'))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE),
         showlegend = FALSE)

fig
6070809010011080100120140160
Gender Gap in Earnings per UniversityWomenMen

Scaling using Sizeref

To scale the bubble size, use the attribute sizeref. We recommend using the following formula to calculate a sizeref value:

sizeref = 2. * max(array of size values) / (desired maximum marker size ** 2)

Note that setting sizeref to a value greater than 1 decreases the rendered marker sizes, while setting sizeref to less than 1 increases the rendered marker sizes. See https://plotly.com/python/reference/#scatter-marker-sizeref for more information.

Additionally, we recommend setting the sizemode attribute: https://plotly.com/python/reference/#scatter-marker-sizemode to area.

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

data$State <- as.factor(c('Massachusetts', 'California', 'Massachusetts', 'Pennsylvania', 'New Jersey', 'Illinois', 'Washington DC',
                          'Massachusetts', 'Connecticut', 'New York', 'North Carolina', 'New Hampshire', 'New York', 'Indiana',
                          'New York', 'Michigan', 'Rhode Island', 'California', 'Georgia', 'California', 'California'))

#Use the ideal sizeref value
desired_maximum_marker_size <- 40
your_list_of_size_values <- data['Gap']
sizeref <- 2.0 * max(your_list_of_size_values) / (desired_maximum_marker_size**2)

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers', color = ~State, colors = 'Paired',
        sizes = c(10, 50),
        marker = list(size = your_list_of_size_values, opacity = 0.5, sizemode = 'area', sizeref = sizeref))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE),
         showlegend = FALSE)

fig
607080901001108090100110120130140150160170
Gender Gap in Earnings per UniversityWomenMen

Scaling V2

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

data$State <- as.factor(c('Massachusetts', 'California', 'Massachusetts', 'Pennsylvania', 'New Jersey', 'Illinois', 'Washington DC',
                          'Massachusetts', 'Connecticut', 'New York', 'North Carolina', 'New Hampshire', 'New York', 'Indiana',
                          'New York', 'Michigan', 'Rhode Island', 'California', 'Georgia', 'California', 'California'))

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers', size = ~Gap, color = ~State, colors = 'Paired',
        #Choosing the range of the bubbles' sizes:
        sizes = c(10, 50),
        marker = list(opacity = 0.5, sizemode = 'diameter'))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE),
         showlegend = FALSE)

fig
6070809010011080100120140160
Gender Gap in Earnings per UniversityWomenMen

Hover Text with Bubble Charts

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

data$State <- as.factor(c('Massachusetts', 'California', 'Massachusetts', 'Pennsylvania', 'New Jersey', 'Illinois', 'Washington DC',
                          'Massachusetts', 'Connecticut', 'New York', 'North Carolina', 'New Hampshire', 'New York', 'Indiana',
                          'New York', 'Michigan', 'Rhode Island', 'California', 'Georgia', 'California', 'California'))

fig <- plot_ly(data, x = ~Women, y = ~Men, type = 'scatter', mode = 'markers', size = ~Gap, color = ~State, colors = 'Paired',
        sizes = c(10, 50),
        marker = list(opacity = 0.5, sizemode = 'diameter'),
        hoverinfo = 'text',
        text = ~paste('School:', School, '<br>Gender Gap:', Gap))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE),
         showlegend = FALSE)

fig
6070809010011080100120140160
Gender Gap in Earnings per UniversityWomenMen

Styled Buble Chart

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv")

data_2007 <- data[which(data$year == 2007),]
data_2007 <- data_2007[order(data_2007$continent, data_2007$country),]
slope <- 2.666051223553066e-05
data_2007$size <- sqrt(data_2007$pop * slope)
colors <- c('#4AC6B7', '#1972A4', '#965F8A', '#FF7070', '#C61951')

fig <- plot_ly(data_2007, x = ~gdpPercap, y = ~lifeExp, color = ~continent, size = ~size, colors = colors,
        type = 'scatter', mode = 'markers', sizes = c(min(data_2007$size), max(data_2007$size)),
        marker = list(symbol = 'circle', sizemode = 'diameter',
                      line = list(width = 2, color = '#FFFFFF')),
        text = ~paste('Country:', country, '<br>Life Expectancy:', lifeExp, '<br>GDP:', gdpPercap,
                      '<br>Pop.:', pop))
fig <- fig %>% layout(title = 'Life Expectancy v. Per Capita GDP, 2007',
         xaxis = list(title = 'GDP per capita (2000 dollars)',
                      gridcolor = 'rgb(255, 255, 255)',
                      range = c(2.003297660701705, 5.191505530708712),
                      type = 'log',
                      zerolinewidth = 1,
                      ticklen = 5,
                      gridwidth = 2),
         yaxis = list(title = 'Life Expectancy (years)',
                      gridcolor = 'rgb(255, 255, 255)',
                      range = c(36.12621671352166, 91.72921793264332),
                      zerolinewidth = 1,
                      ticklen = 5,
                      gridwith = 2),
         paper_bgcolor = 'rgb(243, 243, 243)',
         plot_bgcolor = 'rgb(243, 243, 243)')

fig
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AfricaAmericasAsiaEuropeOceaniaLife Expectancy v. Per Capita GDP, 2007GDP per capita (2000 dollars)Life Expectancy (years)

Reference

See https://plotly.com/r/reference/#scatter for more information and chart attribute options!

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)