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(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
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.1
## ✔ readr     2.1.5
## ── 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
library(gganimate)
library(gifski)
data <- gapminder
data %>%
  group_by(continent) %>%
  summarize(average = mean(gdpPercap),
            maximum = max(gdpPercap))
## # A tibble: 5 × 3
##   continent average maximum
##   <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"))

library(plotly)
p2 <- plot_ly(data, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, text = ~country, marker = list(sizemode = "diameter"),
        hoverinfo = "text", 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"))
p2
## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

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.

animated_plot <- data %>%
  plot_ly(
    x = ~gdpPercap, y = ~lifeExp,color = ~continent, 
    size = ~pop, frame = ~year, text = ~country, marker = list(sizemode = "diameter"),
    hoverinfo = "text", type = 'scatter', mode = 'markers'
  )
animated_plot <- animated_plot %>% 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.

#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

The frame argument adjusts the speed of the animation. A higher number makes the points move slightly faster. The redraw argument resets between each frame. The easing function sset to linear makes the transitions slightly smoother. The autoplay argument was commented out because of an error due to the update but it would have made the animations run automatically.

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,
      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.

interactive_chart <- animated_chart %>% layout(
  title = "GDP per capita vs. Life expectancy from years 1952 to 2007",
  xaxis = list(title = "GDP per capita"),
  yaxis = list(title = "Life expectancy"),
  updatemenus = list(
    list(
      type = "buttons",
      showactive = FALSE,
      x = 0,
      y = 0,
      buttons = list(
        list(
          label = "Pause",
          method = "animate",
          args = list(NULL, list(frame = list(duration = 0,
                                              redraw = TRUE),
                                 mode = "immediate"))
        )
      )
    )
  )
)
interactive_chart

Q7

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