Note the use of the p_load() function from the pacman package!

Q1

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.

library(gapminder)
df <- gapminder %>% group_by(continent)
summarise(df, mean(gdpPercap), max(gdpPercap))
## # A tibble: 5 × 3
##   continent `mean(gdpPercap)` `max(gdpPercap)`
##   <fct>                 <dbl>            <dbl>
## 1 Africa                2194.           21951.
## 2 Americas              7136.           42952.
## 3 Asia                  7902.          113523.
## 4 Europe               14469.           49357.
## 5 Oceania              18622.           34435.

Q2

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(df, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, text = ~country, marker = list(sizemode = "diameter"),
        hoverinfo = "text", type = 'scatter', mode = "markers") %>%
  layout(title = "Life Expectancy vs. GDP per Capita",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy"))
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?

Q3

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(df, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, frame = ~year, text = ~country, marker = list(sizemode = "diameter"),
        type = 'scatter', mode = "markers") %>%
  layout(title = "Life Expectancy vs. GDP per Capita",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy"))
animated_plot

Q4

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?
#                 autoplay = TRUE #What did this do)
animated_chart

Setting frame=100 appears to have sped up the animation from the original, as it reset the frame rate to 100 milliseconds. Setting redraw=TRUE updates the plot with each frame; you can see this change in the slider, which jumps between the points instead of flowing smoothly. Linear easing sets the transition between each frame to the same rate and gives a more continuous flow. While we could run the autoplay, it would’ve automatically started playing the animation as soon the page loaded.

Q5

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 = -.15,  
       y = -.182,  
       buttons = list(
         
        list(
           label = "Pause",
           method = "animate",
           args = list(NULL, list(frame = list(duration = 0, 
                                               redraw = TRUE), 
                                  mode = "immediate"))
         )
       )
     )
   )
 )
 interactive_chart

Q6

Add a title, axis labels, any other important annotations to the plot. This could include adding hover text or other elements. Elements were added in the initial plot and following questions (see Q2 for title, axis labels, and hover text code :) ).

interactive_chart

Q7

Practice publishing your final plot to Rpubs (or another location of your choice) and include the link in your submission.