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(gapminder)
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
summary_gap<-gapminder %>%
  mutate(gdp = gdpPercap * pop) %>% 
  group_by(continent) %>%
  summarize(
    avg_gdp = mean(gdp, na.rm = TRUE),
    max_gdp = max(gdp, na.rm = TRUE))
summary_gap
## # A tibble: 5 × 3
##   continent       avg_gdp max_gdp
##   <fct>             <dbl>   <dbl>
## 1 Africa     20904782844. 4.48e11
## 2 Americas  379262350210. 1.29e13
## 3 Asia      227233738153. 6.54e12
## 4 Europe    269442085301. 2.65e12
## 5 Oceania   188187105354. 7.04e11

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"))

library(plotly)
static_plot <- plot_ly(gapminder, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, type = 'scatter', mode = "markers") %>%
  layout(title = "Bubble Chart of GDP & Life Expectancy",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy"))
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? Yes, because the plot includes all values, which made clusters that causes confusion and chaos on the visualization.

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.

animated_plot <- plot_ly(gapminder,x = ~gdpPercap, y = ~lifeExp,color = ~continent, 
    size = ~pop, frame = ~year, type = 'scatter', mode = 'markers'
  )  %>% 
  layout(xaxis = list( type = "log") )
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.

animated_chart <- animated_plot %>% 
  animation_opts(frame = 100,  # change the amount of time between frames to 100 milliseconds
                 redraw = TRUE, # redraw the plot to make sure everything is updated in the plot before moving to the next transition
                 easing = "linear") #make sure the transition speed is constant
                 # autoplay = TRUE) #play the plot automatically
animated_chart

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 = -0.02,  
       y = -0.3,
       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.

updated_interactive_chart <- animated_chart %>% layout(
    title = "Bubble Chart of Country GDP vs. Life Expectancy Over Time",
    xaxis = list(
      title = "GDP per Capita",
      type = "log"    
    ),
    yaxis = list(
      title = "Life Expectancy (years)"
    ),
   updatemenus = list(
     list(
       type = "buttons",
       showactive = FALSE,
       x = -0.02,  
       y = -0.3,
       buttons = list(
         
         list(
           label = "Pause",
           method = "animate",
           args = list(NULL, list(frame = list(duration = 0, 
                                               redraw = TRUE), 
                                  mode = "immediate"))
         )
       )
     )
   )
 )
updated_interactive_chart

Q7

Practice publishing your final plot to Rpubs (or another location of your choice) and include the link in your submission. http://rpubs.com/Dobby171225/1243019