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)
## 
## 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
gap_summary = gapminder %>% group_by(continent) %>% summarize(mean(gdpPercap),max(gdpPercap))
gap_summary
## # A tibble: 5 × 3
##   continent `mean(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.
#includes variables country,continent,year,life expectancy, population, and 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, type = 'scatter', mode = 'markers',marker = list(sizemode = "diameter")) %>%
  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.

# Code goes here

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

The frame function changes the number of frames used to display the chart. For example, frames=10 will be faster than frames=100 because it takes less frames to reach the end.

The redraw function draws a new plot for every year given instead of making a smooth transition.

Easing changes the speed of the transition so that it is constant at a linear pace.

#Uncomment the lines below to see the updated animation. 
animated_chart <- animated_plot %>% 
  animation_opts(frame = 10,
                 redraw = TRUE, # What did this do?
                 easing = "linear") #What did this do?
                 #autoplay = TRUE) #What did this do
animated_chart
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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 = 1.15,  
       y = -0.2,  
       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.

final_plotly = plot_ly(gapminder, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, mode = 'markers',marker = list(sizemode = "diameter"),frame=~year,
        text = ~country, hoverinfo = "text", type = 'scatter') %>%
  layout(title = "Life Expectancy vs. GDP per Capita",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy")) %>%
  animation_opts(frame = 10,
                 redraw = TRUE, 
                 easing = "linear") %>% layout(
   updatemenus = list(
     list(
       type = "buttons",
       showactive = FALSE,
       x = 1.15,  
       y = -0.2,  
       buttons = list(
         
         list(
           label = "Pause",
           method = "animate",
           args = list(NULL, list(frame = list(duration = 0, 
                                               redraw = TRUE), 
                                  mode = "immediate"))
         )
       )
     )
   )
 )
final_plotly
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Q7

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