Life Expectancy vs GDP per Capita by Country Using gganimate()
Isaac Attram
22 July 2022
Introduction
This code through project explores the gganimate() package of ggplot() to add some transition effects over time using the gapminder dataset. I will compare life expectancy to GDP per capita across the various countries from 1952 to 2007.
Content Overview
Specifically, I’ll explain and demonstrate how to load the gganimate() package, add transition effect, add a time frame, show trails of how each point moves across time, edit the graph size, and save the graph as a gif.
Why You Should Care
This topic is valuable because gganimate() gives a 3D view of graphs compared to the traditional static graphs, especially when observed across time. It adds beautiful aesthetics to plots and makes presentations fun. Finally, because we can save the final plot as a gif, sharing on various platforms is easy.
Learning Objectives
Specifically, you’ll learn how to animate plots in R using gganimate() and save the graph as a gif file.
Body Title
Here, we’ll show how life expectancy and GDP per capita vary across time for each country using the gapminder dataset.
Further Exposition
This is based on the work of Ola Rosling, Anna Rosling Rönnlund, and Hans Rosling. Gapminder was created in February 2005 to combine data from multiple sources into unique, coherent time-series. The dataset contains variables such as life expectancy, population, GDP per capita, years, countries, and continents.
Basic Example
This basic example shows how to plot the life expectancy vs. GDP per capita using ggplot() and add a simple transition effect.
plot1 = gapminder %>%
ggplot(aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) +
geom_point(alpha = 0.7, stroke = 0) +
theme_fivethirtyeight() +
scale_size(range = c(2, 12), guide = "none") +
scale_x_log10() +
labs(title = "Life Expectancy vs GDP per Capita by Country",
x = "GDP Per Capita",
y = "Life Expectancy",
color = "Continent",
caption = "Source: Hans Rosling's gapminder data on health and wealth") +
theme(axis.title = element_text(),
text = element_text(family = "serif"),
legend.text = element_text(size = 10)) +
scale_color_brewer(palette = "Set2")
plot1.animate = plot1 +
transition_time(year) # adding the transition effect
plot1.animate
Advanced Examples
More specifically, this code can be used for adding a time frame to the plot to display the changes in time as the points change.
# Add year frame to show what year it is.
plot1.animate = plot1 +
transition_time(year) +
labs(subtitle = "Year: {frame_time}")
plot1.animate
Moreover, this additional code can also be used for adding shadows to each point that serve as trails to demonstrate the paths taken by the points as they change over time.
# Add shadow wake trail to show how the point are moving over time
plot1.animate = plot1 +
transition_time(year) +
labs(subtitle = "Year: {frame_time}") +
shadow_wake(wake_length = 0.1)
plot1.animate