Nations Dataset

library(tidyverse)

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nations <- read.csv("nations.csv")
head(nations)

  iso2c iso3c country year gdp_percap population birth_rate neonat_mortal_rate
1    AD   AND Andorra 1996         NA      64291       10.9                2.8
2    AD   AND Andorra 1994         NA      62707       10.9                3.2
3    AD   AND Andorra 2003         NA      74783       10.3                2.0
4    AD   AND Andorra 1990         NA      54511       11.9                4.3
5    AD   AND Andorra 2009         NA      85474        9.9                1.7
6    AD   AND Andorra 2011         NA      82326         NA                1.6
                 region      income
1 Europe & Central Asia High income
2 Europe & Central Asia High income
3 Europe & Central Asia High income
4 Europe & Central Asia High income
5 Europe & Central Asia High income
6 Europe & Central Asia High income

nations1 <- nations |>
  mutate(gdp = (gdp_percap * population)/10^12) |>
  filter(country == "China" | country == "India" | country == "United States" | country == "Indonesia")

chart1 <- nations1 |>
  ggplot(aes(year, gdp, color = country)) +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Set1") +
  labs(title = "Economies of the Four Most Populated Countries",
       x = "Year",
       y = "GDP ($ Trillion)",
       color = "Country")
chart1

# I did palette "Pastel2" instead of palette "Set2"
chart2 <- nations |>
  mutate(gdp = (gdp_percap * population)/10^12) |>
  group_by(region, year) |>
  summarise(GDP = sum(gdp, na.rm = T)) |>
  ggplot(aes(year, GDP)) +
  geom_area(aes(fill = region), color = "white") +
  scale_fill_brewer(palette = "Pastel2") +
  labs(title = "GDP by World Bank Region",
       x = "Year",
       y = "GDP ($ Trillion)",
       fill = "Region")

`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.

chart2