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.

gap.data=gapminder
gap.data%>%group_by(continent)%>%summarize(avg.GDP=mean(gdpPercap),max.GDP=max(gdpPercap))
## # 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"))

#Uncomment and modify the line below this one to create your plot!
static_plot <- plot_ly(data=gap.data,x=~gdpPercap,y=~lifeExp,type="scatter",mode="markers",marker=list(sizemode="diameter"),size=~pop,color=~continent)
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 varable that should determine the frame.

# Code goes here

#Uncomment the line below and modify as needed!
animated_plot <- plot_ly(data=gap.data,x=~gdpPercap,y=~lifeExp,type="scatter",mode="markers",marker=list(sizemode="diameter"),size=~pop,color=~continent,frame = ~year,text = ~paste('Country: ', country))
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.

#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
animated_chart

It looks like frame=100 sped up the animation, redraw=TRUE had minimal impact but with relevant plots should redraw the plot between frames as opposed to not redrawing and just shifting, easing = “linear” changes the transitions between frames to a linear rate, autoplay=TRUE makes the plot autoplay its animation stages.

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(title="Country Life Expetancy vs GDP per Capita 1952 to 2007",xaxis = list(title = "GDP Per Capita"),yaxis = list(title = "Life Expectancy"),
  updatemenus = list(
    list(
      type = "buttons",
      showactive = FALSE,
      x = 0,
      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.

Q7

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