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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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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.

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)