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library(dslabs)head(gapminder)
country year infant_mortality life_expectancy fertility
1 Albania 1960 115.40 62.87 6.19
2 Algeria 1960 148.20 47.50 7.65
3 Angola 1960 208.00 35.98 7.32
4 Antigua and Barbuda 1960 NA 62.97 4.43
5 Argentina 1960 59.87 65.39 3.11
6 Armenia 1960 NA 66.86 4.55
population gdp continent region
1 1636054 NA Europe Southern Europe
2 11124892 13828152297 Africa Northern Africa
3 5270844 NA Africa Middle Africa
4 54681 NA Americas Caribbean
5 20619075 108322326649 Americas South America
6 1867396 NA Asia Western Asia
options(scipen=0)big_four <-c("United States", "Russia", "United Kingdom", "China")gapminder_filtered <- gapminder %>%filter(year %in%c(1989, 2011), country %in% big_four)ggplot(gapminder_filtered, aes(x = gdp /1e9, y = life_expectancy, color = country, size = population)) +geom_point() +labs(title ="Life Expectancy vs. GDP for Big 4 Allies (1989 & 2011)",x ="GDP (Billions USD)",y ="Life Expectancy (Years)") +theme_minimal() +scale_color_manual(values =c("blue", "green", "red", "purple", "orange")) +theme(legend.position ="bottom") +facet_grid(~year)
I used the “gapminder” dataset, which contains information about various countries over time, including variables such as life expectancy, GDP, fertility rate, population, continent, region, and year. Specifically, I filtered the dataset to include data for the “Big 4” (the largest allies of WWI) during the years 1989 and 2011, focusing on life expectancy and GDP. The most limiting aspect of creating this graph was the lack of information on Russia.
