To pass specifications, you need to complete all questions, especially the final couple questions. You should have a functional animation with a working “play” button and some customized options. The animated line document on Canvas may be useful as another reference for animations using Plotly in R.

Note the use of the p_load() function from the pacman package!

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
#install.packages("pacman")
knitr::opts_chunk$set(echo = TRUE)
pacman::p_load(plotly, gapminder)

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.

data <- gapminder

summary <- data %>% group_by(continent) %>% summarize( Avg = mean(gdpPercap), Max = max(gdpPercap))

summary
## # A tibble: 5 × 3
##   continent    Avg     Max
##   <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(...)
static_plot <- plot_ly(data, 
               x = ~gdpPercap, 
               y = ~lifeExp, 
               size = ~pop, 
               color = ~continent,
               type = 'scatter', 
               mode = 'markers',
               marker = list(sizemode = 'diameter', opacity = 0.7)) %>%
  layout(
         xaxis = list(title = 'GDP per Capita', type = 'log'),
         yaxis = list(title = 'Life Expectancy')) %>%
  style(hoverinfo = "none")
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# Show the plot
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 <- static_plot <- plot_ly(data, 
               x = ~gdpPercap, 
               y = ~lifeExp, 
               size = ~pop, 
               color = ~continent, 
               text = ~country, 
               type = 'scatter', 
               mode = 'markers',
               frame = ~year,
               marker = list(sizemode = 'diameter', opacity = 0.7)) %>%
  layout(title = 'Gapminder Bubble Chart',
         xaxis = list(title = 'GDP per Capita', type = 'log'),
         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?
                 redraw = TRUE, # What did this do?
                 easing = "linear", #What did this do?
                 #autoplay = TRUE #What did this do
                 )
animated_chart
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In the code chunk frame = 100 made the animation go faster, redraw = TRUE made it replot the bubbles for each year/frame, easing = “linear” made the animation progress at a constant rate, and autoplay = TRUE makes the plot play by itself.

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 = -.05,  
       y = -.3,  
       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.

fig2 <- interactive_chart %>% layout(title = "Global GDP and Life Expectancy Over Time") %>% 
  style(hoverinfo = ~country)
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fig2

Q7

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