library(gapminder) library(tidyverse) library(gganimate) library(gifski) library(ggplot2)

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$set(echo = TRUE)
knitr::opts_chunk$set(warning = F)
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 <- gapminder

avg_gap <- gap %>% group_by(continent) %>% 
  summarise(avgGDP = mean(gdpPercap, na.rm =T), maxGDP = max(gdpPercap, na.rm = T))

avg_gap
## # A tibble: 5 × 3
##   continent avgGDP  maxGDP
##   <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"))

#Uncomment and modify the line below this one to create your plot!
static_plot <- plot_ly(gap, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
        size = ~pop, text = ~country,
        hoverinfo = "text", type = 'scatter', mode = "markers")

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(gap, x = ~gdpPercap, y = ~lifeExp,color = ~continent, 
    size = ~pop, frame = ~year, text = ~country, 
    hoverinfo = "text", type = 'scatter', mode = 'markers')

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? time of the animation- each frame is going ot be 100 mililseconds
                 redraw = TRUE, # What did this do? redraws the image each time it appears
                 easing = "linear") #What did this do? animation will progress at a constant rate
                # autoplay = TRUE) #What did this do #automatically playing without having the user to press play button
animated_chart
#frame: each frame within the animation will last a 100 milliseconds, it gives each picture a time, therefore there are 100 different frames/pictures the animation will run through

#redraw: each time there is a new image, instead of animating based on the previous image, the animation completely redraws the new frame/image

#easing: the animation will progress at a constant rate

#autoplay: the animation will start automatically, going through all the frames without needing user approval or the user pressing play

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 = .01,  
       y = -.4,  
       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.

interactive2 <-interactive_chart %>%   layout(title = "Life Expectancy vs. GDP per Capita in 2007",
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Life Expectancy"))
interactive2

Q7

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

Rpubs link: https://rpubs.com/sasefi12/1242949