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.

gapData <- gapminder

gapData %>% 
  group_by(continent) %>% 
  summarize(averageGDP = mean(gdpPercap),
            maximumGDP = max(gdpPercap))
## # A tibble: 5 × 3
##   continent averageGDP maximumGDP
##   <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"))

static_plot <- plot_ly(gapData, x = ~gdpPercap, y = ~lifeExp, size = ~pop,
                       type = 'scatter', mode = 'markers', color = ~continent,
                       marker = list(opacity = 0.5,
                                     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?

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 variable that should determine the frame.

animated_plot <- plot_ly(gapData, x = ~gdpPercap, y = ~lifeExp, size = ~pop,
                       frame = ~year, type = 'scatter', mode = 'markers',
                       color = ~continent, marker = list(opacity = 0.5,
                                                        sizemode = "diameter"))

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.

animated_chart <- animated_plot %>%
  animation_opts(
    # The "frame" parameter specifies the amount of time between frames, in
    # milliseconds. Thus, the time between years is 100 milliseconds.
    frame = 100,
    
    # The "redraw" parameter erases the data on the plot and creates new
    # markers to fit the data at the completion of a frame transition. The data
    # pertains to the current frame. 
    redraw = TRUE,
    
    # The "easing" parameter controls the duration in which each year, or
    # frame, is held on screen. With a linear transition ease, each year is
    # shown for an equal amount of time.
    easing = "linear")
    
    # The "autoplay" parameter determines whether or not the animation should
    # start upon execution.
    #autoplay = TRUE)

animated_chart

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 = 1.12,
        y = -0.2,
        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.

interactive_chart_final <- plot_ly(gapData, x = ~gdpPercap, y = ~lifeExp,
                                   size = ~pop, frame = ~year, text = ~country,
                                   hoverinfo = "text", type = 'scatter',
                                   mode = 'markers', color = ~continent,
                                   marker = list(opacity = 0.5, 
                                                 sizemode = "diameter")) %>% 
  animation_opts(frame = 100, redraw = TRUE, easing = "linear") %>%
  layout(title = "Life Expectancy vs. GDP per Capita by Year",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy"),
         updatemenus = list(
           list(
             type = "buttons",
             showactive = FALSE,
             x = 1.12,
             y = -0.2,
             buttons = list(
          
               list(
                 label = "Pause",
                 method = "animate",
                 args = list(NULL, list(frame = list(duration = 0,
                                                     redraw = TRUE),
                                        mode = "immediate"))
                 )
          
               )
             )
           )
         )

interactive_chart_final

Q7

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