# List of packages
packages <- c("tidyverse", "gapminder", "fst", "viridis", "ggridges", "modelsummary")

# Install packages if they aren't installed already
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)

# Load the packages
lapply(packages, library, character.only = TRUE)
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## Loading required package: viridisLite
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## `modelsummary` 2.0.0 now uses `tinytable` as its default table-drawing
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# Create a scatter plot of GDP per capita vs. life expectancy
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent), alpha = 0.5) + # Add points, color them by continent, and set transparency
  labs(title = "Life Expectancy vs. GDP per Capita",
       x = "GDP per Capita", y = "Life Expectancy", color = "Continent") + # Add labels and title
  theme_minimal() # Use a minimal theme for a clean look

My interpretation of this visual is that is manifests the life expectancy of individuals from different continents while also showcasing their GDP per capita. In a sense, it is showing which countries from which continents have a higher/lower life expentancy based on their GDP per capita and how it varies all across. A scatter plot is a good way to do this because it makes it easy to interpret and distingush.

("I did it!")
## [1] "I did it!"