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)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
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head(gapminder)
## # 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.
summ <- gapminder %>% group_by(continent) %>%
  summarize(avg_GDP = mean(gdpPercap), max_GDP = max(gdpPercap))
summ
## # A tibble: 5 × 3
##   continent avg_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"))

p1 <- plot_ly(gapminder, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, text = ~country,
        hoverinfo = "text", type = 'scatter', mode = "markers") %>% # Adds 'Country' label when you hover over an object
        # type: what plot you want to create; mode: type of points for the data (markers=points)
  layout(title = "Life Expectancy vs. GDP per Capita",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy"))
p1
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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.

p2 <- gapminder %>%
  plot_ly(
    x = ~gdpPercap, y = ~lifeExp, color = ~continent, 
    size = ~pop, frame = ~year, text = ~country, 
    hoverinfo = "text", type = 'scatter', mode = 'markers'
  )
animated_plot <- p2 %>% layout(xaxis = list(type = "log")) # Transforms the x-axis using the log functions
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,  # Controls speed of transition; higher number = slower
                 redraw = TRUE, # Redraws the plot in each frame
                 easing = "linear") # How frame changes from one to the next; linear = smooth transition
              # autoplay = TRUE # Automatically starts the plot; however, causes an error with the update 
animated_chart 
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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( # Adds features to the plotly object
     list(
       type = "buttons", # Specified to add buttons
       showactive = FALSE, # Whether active state of buttons is displayed
       x = 0, # Sets x-coordinate position of the button
       y = 1, # Sets x-coordinate position of the button; (0,0) is the bottom left corner 
       buttons = list(
         
        list(
           label = "Pause", # Name of the label
           method = "animate", # What do to when the button is clicked
           args = list(NULL, list(frame = list(duration = 0,  # Stops immediately
                                               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.

interactive_chart <- interactive_chart %>%
  layout(title = "GDP per Capita vs. Life Expectancy", xaxis=list("GDP per Capita (Log Scale)"), yaxis=list("Life Expectancy"))
interactive_chart
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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.