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.

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

The data set has data for every country, recorded every 5 years starting in 1952. There is information on the continent, year, life expectancy, populatio, and GDP per capita.

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(gapminder_data, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, text = ~country,
        hoverinfo = "text", type = 'scatter', mode = "markers") %>%
  layout(title = "Life Expectancy by GDP per Capita",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy"))
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 variable that should determine the frame.

animated_plot <-  plot_ly(gapminder_data, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, text = ~country, frame=~year, 
        hoverinfo = "text", type = 'scatter', mode = "markers") %>%
  layout(title = "Life Expectancy by GDP per Capita",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy"))
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.

animated_chart <- animated_plot %>% 
  animation_opts(frame = 100,  #this speeds up the animation, the number is the amoutn of time between frames in milliseconds
                 redraw = TRUE,  #makes the plot reset between frame
                easing = "linear") #details the type of transition between frames
animated_chart
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#autoplay was removed because of an error but it would have automatically started the animation withouot having to hit 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 = 1.13,  
       y = -0.215,  
       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.

Our plot has had this from the beginning.

Q7

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