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.

Data <- gapminder
Data01 <- Data %>% group_by(continent) %>% 
  summarise(mean_gdp = mean(gdpPercap, na.rm=T), max_gdp=max(gdpPercap, na.rm=T))
Data01
## # 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.

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(Data, x = ~gdpPercap, y = ~lifeExp, type = 'scatter', mode = 'markers', size=~pop, color=~continent, text=~country,
                       marker=list(sizemode = "diameter", opacity=0.5, hoverinfo="text"))
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(Data, x = ~gdpPercap, y = ~lifeExp, type = 'scatter', mode = 'markers', size=~pop, color=~continent,
                       marker=list(sizemode = "diameter", opacity=0.5), frame=~year)
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(frame = 100, 
                redraw = TRUE,
                easing = "linear")
                # autoplay = TRUE)
animated_chart

The argument frame=100 changes the timing of the animation so each frame (year) is 100 milliseconds. The argument redraw=TRUE creates a ‘redraw’ of the plot at the end of each transition. The argument easing=‘linear’ changes the type of transition easing in the plot. Linear easing means that the progression is constant. The argument autoplay=True means that the animation automatically starts when the code is run.

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 = 1.15,  
       y = -0.27,  
       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 %>% layout(xaxis=list(title="GDP per capita"), yaxis=list(title="Life Expectancy"), title="GDP per Capita vs. Life Expectancy")

Q7

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