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
head(data)
## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.
continentSummary <- data %>% group_by(continent) %>% 
  summarise(avgGDP = mean(gdpPercap), maxGDP = mean(gdpPercap))
continentSummary
## # A tibble: 5 × 3
##   continent avgGDP maxGDP
##   <fct>      <dbl>  <dbl>
## 1 Africa     2194.  2194.
## 2 Americas   7136.  7136.
## 3 Asia       7902.  7902.
## 4 Europe    14469. 14469.
## 5 Oceania   18622. 18622.

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(data, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, text = ~country,
        hoverinfo = "text", type = 'scatter', mode = "markers")
static_plot
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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(data, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, text = ~country,
        hoverinfo = "text", type = 'scatter', mode = "markers", frame=~year)
animated_plot
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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
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Adding “frame=100” causes the animation to move faster. Adding “redraw=TRUE” has the animation plot redraw the entire plot with each transition instead of merely moving points. Adding “easing=‘linear’” sets how time between the different frames changes throughout the animation. Adding “autoplay=TRUE” causes the plot to automatically play

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 = -0.1,  
       buttons = list(
         
         list(
           label = "Pause",
           method = "animate",
           args = list(NULL, list(frame = list(duration = 0, 
                                               redraw = TRUE), 
                                  mode = "immediate"))
         )
       )
     )
   )
 )
 interactive_chart
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Q6

Add a title, axis labels, any other important annotations to the plot. This could include adding hover text or other elements.

q6plot <- interactive_chart %>%
  layout(title = "Life Expectancy vs. GDP per Capita in 2007",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy"))
q6plot
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Q7

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