Note the use of the p_load() function from the pacman package!
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.
gapminder## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ℹ 1,694 more rowsGDP <- gapminder %>%
group_by(continent) %>%
summarise(average_gdp = mean(gdpPercap, na.rm=TRUE),
maximum_gdp = max(gdpPercap, na.rm = TRUE))
GDP## # A tibble: 5 × 3
## continent average_gdp maximum_gdp
## <fct> <dbl> <dbl>
## 1 Africa 2194. 21951.
## 2 Americas 7136. 42952.
## 3 Asia 7902. 113523.
## 4 Europe 14469. 49357.
## 5 Oceania 18622. 34435.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!
gapminder1 <- gapminder
static_plot <- plot_ly(gapminder1, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
size = ~pop, text = ~country,
hoverinfo = "text", type = 'scatter', mode = "markers", marker = list(sizemode = "diameter"))
static_plot## 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?
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
gapminder2 <- gapminder
animated_plot <- plot_ly(gapminder2, x = ~gdpPercap, y = ~lifeExp, color = ~continent,
size = ~pop, frame = ~year, text = ~country,
hoverinfo = "text", type = 'scatter', mode = "markers", marker = list(sizemode = "diameter"))
animated_plot## Warning: `line.width` does not currently support multiple values.
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## Warning: `line.width` does not currently support multiple values.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## Warning: `line.width` does not currently support multiple values.
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## Warning: `line.width` does not currently support multiple values.The frame function increased the speed of the animated plot, going through the GDP per Capita over the years at a much faster rate than beforehand. This function tells us the amount of frames that it will move through. The redraw function triggers a redraw of the plot at the completion of the graphical transition. It updates the graphical elements at the completion of the transition. The easing function specifies the type of what type of transition you want for your animated plot, allowing you to make the transitions smoother. Although our autoplay does not work due to a new update in R, the function is meant to set the playwith to run automatically with lattice graphics or base graphics.
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 = -0.5,
buttons = list(
list(
label = "Pause",
method = "animate",
args = list(NULL, list(frame = list(duration = 0,
redraw = TRUE),
mode = "immediate"))
)
)
)
)
)
interactive_chart## Warning: `line.width` does not currently support multiple values.
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## Warning: `line.width` does not currently support multiple values.Add a title, axis labels, any other important annotations to the plot. This could include adding hover text or other elements.
interactive_chart1 <- plot_ly(gapminder1, x = ~gdpPercap, y = ~lifeExp, color = ~continent,size = ~pop, frame = ~year, text = ~country, hoverinfo = "text", type = 'scatter', mode = "markers", marker = list(sizemode = "diameter")) %>%
layout(title = "Life Expectancy vs. GDP per Capita",
xaxis = list(title = "GDP per Capita"),
yaxis = list(title = "Life Expectancy")) %>% layout(
updatemenus = list(
list(
type = "buttons",
showactive = FALSE,
x = -0.02,
y = -0.5,
buttons = list(
list(
label = "Pause",
method = "animate",
args = list(NULL, list(frame = list(duration = 0,
redraw = TRUE),
mode = "immediate"))
)
)
)
)
)
interactive_chart1## Warning: `line.width` does not currently support multiple values.
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## Warning: `line.width` does not currently support multiple values.Practice publishing your final plot to Rpubs (or another location of your choice) and include the link in your submission.