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)

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(...)

static_plot <- plot_ly(gapminder, x = ~gdpPercap, y = ~lifeExp, 
                       size = ~pop, color = ~continent, 
                       text = ~paste("Country: ", country, "<br>",
                                     "GDP: ", gdpPercap, "<br>",
                                     "Life Expectancy: ", lifeExp, "<br>",
                                     "Population: ", pop, "<br>"),
                       marker = list(sizemode = "diameter"),
                       type = "scatter", mode = "markers")

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(gapminder, x = ~gdpPercap, y = ~lifeExp, 
                         size = ~pop, color = ~continent, 
                         text = ~paste("Country: ", country, "<br>",
                                       "GDP: ", gdpPercap, "<br>",
                                       "Life Expectancy: ", lifeExp, "<br>",
                                       "Population: ", pop, "<br>"),
                         marker = list(sizemode = "diameter"),
                         type = "scatter", mode = "markers",
                         frame = ~year)

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?
                 #autoplay = TRUE #What did this do)
  )
animated_chart

It looks like the frame option changes how long each frame is shown for, when we made it a large number, each frame was shown for a long time. The redraw option redraws the plot for each frame of the plot. The easing option makes the sequence of frames animate in a linear fashion. The autoplay option makes the plot automatically play, without having to click 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,  
       y = 1,  
       buttons = list(
         
         list(
          label = "Pause",
           method = "animate",
           args = list(NULL, list(frame = list(duration = 0, 
                                               redraw = TRUE), 
                                  mode = "immediate"))
         )
       )
     )
   )
 )
 interactive_chart

Q6

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

final_chart <-interactive_chart %>% 
  layout(title = 'Gapminder GDP Per Capita over Time',
         xaxis = list(title = 'GDP per Capita'),
         yaxis = list(title = 'Life Expectancy'),
         showlegend = TRUE)

final_chart

Q7

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

https://rpubs.com/sreeKatragadda/1167589