Nations HW

library(tidyverse)

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✖ dplyr::filter() masks stats::filter()
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setwd("C:/Users/hanle/Desktop/Han Le - Intro to Data/intro2r")
nations <- read_csv("nations.csv")

Rows: 5275 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): iso2c, iso3c, country, region, income
dbl (5): year, gdp_percap, population, birth_rate, neonat_mortal_rate

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

nations_gdp <- nations |>
  mutate(gdp = (gdp_percap * population) / 1000000000000)

#Graph 1

nations_countries <- nations_gdp |>
  filter(country == "China" | 
           country == "Germany" | 
           country == "Japan" | 
           country == "United States")


ggplot(nations_countries, aes(x = year, y = gdp, color = country)) +
  geom_line() +
  geom_point() +
  scale_color_brewer(palette = "Set1") +
  labs(title = "China's Rise to Become the Largest Economy", 
       x = "Year", 
       y = "GDP (Trillions of $)")+
  theme_minimal()

#Graph 2

nations_region <- nations_gdp |>
  group_by(region, year) |>
  summarise(GDP = sum(gdp, na.rm = TRUE))

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

ggplot(nations_region, aes(x = year, y = GDP, fill = region)) +
  geom_area(color = "white") +
  scale_fill_brewer(palette = "Set2") +
  labs(title = "GDP by World Bank Region", 
       x = "Year", 
       y = "GDP ($ trillion)") +
  theme_minimal()