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(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.0     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ lubridate 1.9.2     ✔ tibble    3.2.1
## ✔ purrr     1.0.1     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks plotly::filter(), stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
gapminder %>% 
  group_by(continent) %>% 
  summarize(avg_gdp=mean(gdpPercap), 
            max_gdp=max(gdpPercap)) 
## # A tibble: 5 × 3
##   continent avg_gdp max_gdp
##   <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"))

static_plot <- plot_ly(gapminder, x=~gdpPercap, y=~lifeExp, size=~pop, 
                       color=~continent, type="scatter", mode="markers",
                       marker = list(sizemode = "diameter"))
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.

animated_plot <- plot_ly(gapminder, x=~gdpPercap, y=~lifeExp, size=~pop, 
                       color=~continent, type="scatter", mode="markers",
                       marker = list(sizemode = "diameter"), 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.

animated_chart <- animated_plot %>% 
  animation_opts(frame = 100, ## A higher number of frames makes the animation move slower, while a lower number of frames makes the animation move faster.
                 redraw = TRUE, ## When redraw is set to true, the the entire plot is redrawn after each animation frame.
                 easing = "linear") ## easing = "liner" means that there is a smooth, linear transition between each frame. The animation progresses at a constant rate.
## When autoplay is set to true, the animation automatically begins without the user having to press play
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 = 1,  
       y = 0,  
       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.

animated_plot <- plot_ly(gapminder, x=~gdpPercap, y=~lifeExp, size=~pop, 
                       color=~continent, type="scatter", mode="markers",
                       marker = list(sizemode = "diameter"), frame=~year,
                       text=~paste("Country: ", country)) %>% 
  layout(
    title = "Animated Bubble Chart of GDP per Capita and Life 
    Expectancy of Each Continent From 1952 to 2007",
    xaxis=list(title="GDP per Capita (dollars)"),
    yaxis=list(title="Life Expectancy (years)"),
  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"))
         )
       )
     )
   )
)
animated_plot

Q7

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