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(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.1
## ── 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
data(gapminder)

gdp_summary <- gapminder %>%
  group_by(continent) %>%
  summarize(
    avg_gdpPercap = mean(gdpPercap, na.rm = TRUE),
    max_gdpPercap = max(gdpPercap, na.rm = TRUE)
  )

gdp_summary
## # A tibble: 5 × 3
##   continent avg_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.

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(
  data = gapminder,
  x = ~gdpPercap,
  y = ~lifeExp,
  size = ~pop,
  color = ~continent,
  type = 'scatter',
  mode = 'markers',
  marker = list(sizemode = "diameter", line = list(width = NA))
) %>%
  layout(
    title = "Life Expectancy vs. GDP per Capita",
    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?

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(
  data = gapminder,
  x = ~gdpPercap,
  y = ~lifeExp,
  size = ~pop,
  color = ~continent,
  frame = ~year,
  type = 'scatter',
  mode = 'markers',
  marker = list(sizemode = "diameter", line = list(width = NA))
) %>%
  layout(
    title = "Life Expectancy vs. GDP per Capita Over Time",
    xaxis = list(title = "GDP per Capita"),
    yaxis = list(title = "Life Expectancy")
  )

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,       # Changes the duration of each frame, changing the animation speed.
    redraw = TRUE,     # Redraws each frame to help increase smoothness.
    easing = "linear", # Also helps making the animation more smooth by applying a linear easing function.
      )

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 = -0.125,  
      y = -0.175,    
      buttons = list(
        
        list(
          label = "Pause",
          method = "animate",
          args = list(NULL, list(
            frame = list(duration = 0, redraw = TRUE), 
            mode = "immediate"
          ))
        )
      )
    )
  )
)

interactive_chart

We moved the pause button to be near the play button because that makes most sense and is most convenient.

Q6

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

Q7

Practice publishing your final plot to Rpubs (or another location of your choice) and include the link in your submission. https://rpubs.com/eshanghandi/1242937