Moments in Time Solutions

# load libraries
library(tidyverse)

In this complement mini-assignment, you are going to use the following data set.

The file processed-20220221-owid-life-expectancy-vs-gdp-per-capita.csv data set. (Max Roser & Ritchie, 2013)

D1 <- read_csv("processed-20220221-owid-life-expectancy-vs-gdp-per-capita.csv")

Basic Interactive Figure using plotly. Modify the R code below where you choose four countries other than what is written. The code below makes an interactive time-series plots for four countries showing the gdp_per_capita variable in time. Use the scale_color_colorblind() function to change the colors.

SOLUTIONS: Answers may vary.

# load packages
library(ggthemes)
library(plotly)

##### start: Write you chosen countries here
chosen_countries <- c("China","Mexico","India","Germany")
##### end: Write you chosen countries here

# subset the data
D1_sub <- D1 %>%
  # Use the %in% command to filter-in only the chosen countries
  filter(country %in% chosen_countries) %>%
  drop_na()

# create a time-series plot
p1 <- ggplot(data = D1_sub, aes(x = year, 
                                y = gdp_per_capita,
                                color = country)) + 
  geom_point() + 
  geom_line() +
#### start: Write plot modifications here
  scale_color_colorblind() +
#### end: Write plot modifications here
  labs(x = "year",
       y = "gdp per capita",
       title = "Yearly GDP per Capita")

# render the ggplot using plotly - interactive
ggplotly(p1)

Basic Animated Figure using gganimate.

# load gganimate package
# you need to install gganimate, gifski, transformr and png packages & restart R
library(gganimate)

# animate the time-series
p1_anim <- p1 + 
  geom_point(aes(group = seq_along(year))) + 
  transition_reveal(year)
animate(p1_anim,
        #fps = 20, duration = 15, # duration and frame rate
        #height = 4, width = 8, units = "in", res = 150 # size and resolution
)

# save the animation
anim_save("solutions-time_series_gganimate.gif")

Multidimensional Interactive Figure using plotly. Filter the data set to only include year 2018, do not include the “Antarctica” continent, and use the gdp_per_capita and life_expectancy variables to create an interactive scatter plot, use the facet_wrap() to separate the figure by continent.

SOLUTIONS: Answers may vary.

D1_2018 <- D1 %>%
  # filter only 2018
  filter(year == 2018) %>%
  # exclude Antarctica in the continent variable
  filter(continent != "Antarctica") %>%
  # remove any rows with missing values
  drop_na()

p2 <- ggplot(data = D1_2018, aes(x = gdp_per_capita, y = life_expectancy)) +
  geom_point(aes(label=country)) +
  facet_wrap(~continent) + # separate the plot by continent
  labs(x = "gdp per capita",
       y = "life expectancy",
       title = "2018 - GDP per Capita vs Life Expectancy")

## Warning: Ignoring unknown aesthetics: label

ggplotly(p2)

Multidimensional Animated Figure using gganimate. Create an animated scatter plot using the gdp_per_capita and life_expectancy variables while using the facet_wrap() to separate the figure by continent. Do not include the “Antarctica” continent. Note that you need to use the year variable to do the transitions. Save the resulting gif file.

D1_sub_sub <- D1 %>% 
  mutate(year = as.integer(year)) %>%
  filter(continent != "Antarctica") %>%
  drop_na()

p3 <- ggplot(data = D1_sub_sub,
             aes(x = gdp_per_capita, y = life_expectancy)) +
  geom_point() +
  facet_wrap(~continent) + 
  labs(x = "gdp per capita",
       y = "life expectancy",
       subtitle = "Data Source: Our World in Data")

# animate the time-series
p3_anim <- p3 + 
  transition_reveal(year) +
  labs(title = "GDP per Capita vs Life Expectancy")
animate(p3_anim)

# save the animation
anim_save("solutions-separated_plots_gganimate.gif")

Submit the resulting html and the gif files using the Google form.

References

Max Roser, E. O.-O., & Ritchie, H. (2013). Life expectancy. Our World in Data. https://ourworldindata.org/life-expectancy