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':
## 
##     intersect, setdiff, setequal, union
gapminder2 <- gapminder %>%
  group_by(continent) %>%
  summarize(average_GDP = mean(gdpPercap), max_GDP = max(gdpPercap))
gapminder2
## # A tibble: 5 × 3
##   continent average_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"))

#Uncomment and modify the line below this one to create your plot!
#static_plot <- plot_ly(...)
library(ggplot2)
library(plotly)
static_plot <- plot_ly(gapminder, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, text = ~country,
        hoverinfo = "text", type = 'scatter', mode = "markers") %>%
  layout(title = "Life Expectancy vs. 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 varable that should determine the frame.

animated_plot <- plot_ly(gapminder, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, frame = ~year, text = ~country,
        hoverinfo = "text", type = 'scatter', mode = "markers") %>%
  layout(title = "Life Expectancy vs. 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.

#Uncomment the lines below to see the updated animation. 
animated_chart <- animated_plot %>% 
  animation_opts(frame = 100,  #What did this do? This makes the transitions faster between the years when you move the slider.
                 redraw = TRUE, # What did this do? This means that the plot is redrawn each time that the frame changes, whereas if the argument is false, only the points that change are updated. For this data, we did not see a significant difference between redraw = TRUE and redraw = FALSE.
                 easing = "linear") #This makes the animation go at a constant speed.
                 #autoplay = TRUE #What did this do)
animated_chart
## Warning: `line.width` does not currently support multiple values.

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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(
    list(
      type = "buttons",
      showactive = FALSE,
      x = 0,
      y = -0.6,
      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.

Q7

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