Note the use of the p_load() function from the pacman package!
Load the gapminder dataset into your R environment and take a look at it. Note the location of different variables and include a summary of the average and maximum GDP by continent.
gapminder <- gapminder
library(dplyr)
summary_gapminder <- gapminder %>%
group_by(continent) %>%
summarise(mean_gdp = mean(gdpPercap), max_gdp = max(gdpPercap))
summary_gapminder
## # A tibble: 5 × 3
## continent mean_gdp max_gdp
## <fct> <dbl> <dbl>
## 1 Africa 2194. 21951.
## 2 Americas 7136. 42952.
## 3 Asia 7902. 113523.
## 4 Europe 14469. 49357.
## 5 Oceania 18622. 34435.
Using plotly, create a static bubble chart of the gapminder data. Map
GDP to the x axis, life expectancy to the Y axis, and population to the
size of the bubbles. Each bubble’s color should be based on the region
of that nation. Hint: If size is not working properly for you, you may
want to use: marker = list(sizemode = "diameter"))
#Uncomment and modify the line below this one to create your plot!
static_plot <- plot_ly(data=gapminder, y=~lifeExp, x=~gdpPercap, size=~pop,
color = ~continent,
type="scatter", mode="marker",
marker = list(sizemode = "diameter"))
static_plot
Note here that the plot looks pretty messy because all the years are present on the plot. It would be nice to see the dots by country one year at a time, right?
Create a new plotly object that is similar to the previous one, but
includes animation. This is as easy as adding frame to your
plot_ly command and specifying the varable that should determine the
frame.
# Code goes here
#Uncomment the line below and modify as needed!
animated_plot <- plot_ly(data=gapminder, y=~lifeExp, x=~gdpPercap, size=~pop,
color = ~continent,
type="scatter", mode="marker",
marker = list(sizemode = "diameter"),
frame=~year)
animated_plot
Try adding a few options to alter your animation lightly. As long as you’ve created your plot correctly, all you need to do here is uncomment the code, run the chunk, and explain what each part did.
#Uncomment the lines below to see the updated animation.
animated_chart <- animated_plot %>%
animation_opts(frame = 100, #What did this do?
redraw = TRUE, # What did this do?
easing = "linear")#What did this do)
animated_chart
The frame = 100 argument sped the animation up (decreases the time between each frame). The frame argument is the time between each frame in milliseconds. The redraw argument when set to true, triggers a redraw of the plot at each completion of the transition (each frame). Finally, when easing is set to linear, the animation progresses at a constant speed without speeding up or slowing down as it goes through each year.
Now, let’s add a button to pause the animation. You should spend a bit of time looking at this code and understanding what it does, then try to move the button to a place that makes more sense.
interactive_chart <- animated_chart %>% layout(
updatemenus = list(
list(
type = "buttons",
showactive = FALSE,
x = 1.15,
y = -0.1825,
buttons = list(
list(
label = "Pause",
method = "animate",
args = list(NULL, list(frame = list(duration = 0,
redraw = TRUE),
mode = "immediate"))
)
)
)
)
)
interactive_chart
Add a title, axis labels, any other important annotations to the plot. This could include adding hover text or other elements.
titled_chart <- animated_chart %>%
layout(
title = "Bubble Chart of GDP and Life Expectancy over Time for each Continent",
xaxis = list(title="GDP per Capita"),
yaxis = list(title="Life Expectancy (years)")
)
titled_chart
Practice publishing your final plot to Rpubs (or another location of your choice) and include the link in your submission.