Packages

First, we need to load a few packages:

plotly for the interactive chart
ggplot2 for creating the initial plot
hrbrthemes for the theme
viridis for the color palette
gapminder for the dataset
dplyr for data manipulation

# Visualisation packages
library(ggplot2)
library(plotly)
library(viridis)
library(hrbrthemes)

# Data packages
library(gapminder)
library(dplyr)

Dataset

The gapminder dataset is used in this example. It contains data about life expectancy, population and GDP per capita for many countries over time.

Here, we:

filter the data to keep only the year 2007
remove the year column
reorder the countries to have big bubbles on top

data <- gapminder %>% filter(year=="2007") %>% dplyr::select(-year)
data = data %>%  
  # Reorder countries to having big bubbles on top
  arrange(desc(pop)) %>%
  mutate(country = factor(country, country))

Default plot

p = data %>%
  ggplot( aes(x=gdpPercap, y=lifeExp, size = pop, color = continent)) +
    geom_point(alpha=0.7) +
    scale_size(range = c(1.4, 19), name="Population (M)") +
    scale_color_viridis(discrete=TRUE, guide=FALSE) +
    theme_ipsum() +
    theme(legend.position="none")

# turn ggplot interactive with plotly
pp = ggplotly(p)

# save the widget
#library(htmlwidgets)
#saveWidget(pp, file="HtmlWidget/plotlyDefaultTooltip.html")

Customize tooltip

As you can see, it’s a bit unfortunate that the tooltip does not tell us what is the country. Let’s customize it to add this information.

For this, we:

create a new column text in the dataset that contains the information we want to display in the tooltip using the mutate() function
add text as an aesthetic in the ggplot() function

And that’s it!

data = data %>% 
  mutate(text = paste(
    "Country: ", country,
    "\nPopulation (M): ", pop,
    "\nLife Expectancy: ", lifeExp,
    "\nGdp per capita: ", gdpPercap, sep="")
  )

p = data %>%
  ggplot( aes(x=gdpPercap, y=lifeExp, size=pop, color=continent, text=text)) +
    geom_point(alpha=0.7) +
    scale_size(range = c(1.4, 19), name="Population (M)") +
    scale_color_viridis(discrete=TRUE, guide=FALSE) +
    theme_ipsum() +
    theme(legend.position="none")

# turn ggplot interactive with plotly
pp = ggplotly(p, tooltip="text")

# save the widget
#library(htmlwidgets)
#saveWidget(pp, file="HtmlWidget/plotlyDefaultTooltip2.html")

More advanced customization

The previous example is a good start, but we can go further. Here, we:

use bold text to make the tooltip more readable
add a dollar sign before the GDP per capita
use the round() function to make the numbers more readable
add a title to the tooltip
change the background color of the tooltip to white
change the font color of the tooltip to black

data <- data %>%
  mutate(text = paste(
    "<span style='font-size:16px;'><b>", country, "</b></span><br>",
    "<br><b>Population (M):</b> ", pop,
    "<br><b>Life Expectancy (years):</b> ", round(lifeExp, 2),
    "<br><b>Gdp per capita:</b> $", round(gdpPercap, 2),
    sep=""))

p <- data %>%
  ggplot(aes(x=gdpPercap, y=lifeExp, size=pop, color=continent, text=text)) +
    geom_point(alpha=0.7) +
    scale_size(range = c(1.4, 19), name="Population (M)") +
    scale_color_viridis(discrete=TRUE, guide=FALSE) +
    theme_ipsum() +
    theme(legend.position="none")

pp <- ggplotly(p, tooltip="text")
pp <- pp %>%
  layout(
    hoverlabel = list(bgcolor = "white",
                      font = list(size = 12, color = "black"))
  )

# save the widget
#library(htmlwidgets)
#saveWidget(pp, file="HtmlWidget/plotlyDefaultTooltip3.html")

Conclusion

This post explains how to customize the tooltip in a plotly chart in R.

You can learn more about interactive charts on the R graph gallery.