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!

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.

library(dplyr)
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## The following objects are masked from 'package:stats':
## 
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## The following objects are masked from 'package:base':
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library(gapminder)
gapminder %>%
  group_by(continent) %>%
  summarise(
    average_gdpPercap = mean(gdpPercap, na.rm = TRUE),  # Average GDP per capita by continent
    max_gdpPercap = max(gdpPercap, na.rm = TRUE)        # Maximum GDP per capita by continent
  )
## # A tibble: 5 × 3
##   continent average_gdpPercap max_gdpPercap
##   <fct>                 <dbl>         <dbl>
## 1 Africa                2194.        21951.
## 2 Americas              7136.        42952.
## 3 Asia                  7902.       113523.
## 4 Europe               14469.        49357.
## 5 Oceania              18622.        34435.

Some of the variables include country, continent, year, life expectancy, population, and gdp per capita. Oceania has the highest average gdp per capita and Africa has the lowest average gdp per capita. Asia has the highest maximum gdp per capita and Africa has the lowest maximum gdp per capita.

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(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 variable 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, color = ~continent,
        size = ~pop, text = ~country, frame = ~year,
        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?
                 redraw = TRUE, # What did this do?
                 easing = "linear") #What did this do?
animated_chart
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The frame = 100 references the speed of the animation. More frames moves slower and vice versa. Redraw = TRUE means that each frame of the animation is re-drawn for each frame so is helpful if elements change between frames. Easing = “linear” means that the speed is constant throughout the animation, so there is no acceleration or deccerlation effects. Autoplay = TRUE means that the animation plays as soon as the code is run instead of clicking “Play.” - note: I did remove autoplay=TRUE because my code was not working with it

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 = 0,  
       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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We moved the Pause button to the right of the screen by changing x to 1. Theoretically, we think with a slower animation that this would make more sense as the animation is moving over time and your eyes tend to move to the right as this happens. However, with this animation, we think it is actually best to leave it on the left near the play button as your mouse is already there.

Q6

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

animated_plot1 <- plot_ly(gapminder, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, text =~paste("Country:", country, "<br>Population:", pop),
        hoverinfo = "text", frame = ~year, type = 'scatter', mode = "markers") %>%
  layout(title = "Life Expectancy vs. GDP per Capita in 2007",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy"))

animated_chart1 <- animated_plot1 %>% 
  animation_opts(frame = 100,  
                 redraw = TRUE, 
                 easing = "linear") 

interactive_chart1 <- animated_chart1 %>% layout(
  updatemenus = list(list(type = "buttons", showactive = FALSE, x = 1, y = 0, buttons = list(list(label = "Pause", method = "animate", args = list(NULL, list(frame = list(duration = 0,redraw = TRUE), mode = "immediate")))))))
interactive_chart1
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We added the population to the hover text with the country. We thought it would be helpful to see as the GDP per capita is presented so the population provides more context for that value.

Q7

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