Data 110 plot homework

library(tidyverse)

setwd("~/Documents/Data\ 110/week7")
nations <- read.csv("nations.csv")

Create a new variable GDP by multiplying gdp_percap by population/1 trillion

nations <- nations %>% mutate("GDP ($ trillion)" = gdp_percap*population/1000000000000)

nations %>%
  filter(country %in% c("China", "Germany", "Japan", "United States")) %>%
  ggplot(mapping = aes(x = year, y = `GDP ($ trillion)`, color = country)) + 
  geom_point() +
  geom_line() + 
  labs(title = "China's Rise to Become the Largest Economy") +
  scale_color_brewer(palette = "Set1") +
  theme_bw() + 
  theme(panel.border = element_blank())

nations %>%
  group_by(region, year) %>%
  summarise(`GDP ($ trillion)`=sum(`GDP ($ trillion)`, na.rm = TRUE)) %>%
  ggplot(mapping = aes(x = year, y = `GDP ($ trillion)`, fill = region)) +
  geom_area(color = "white") +
  labs(title = "GDP by World Bank Region") +
  scale_fill_brewer(palette = "Set2") + 
  theme_bw() +
  theme(panel.border = element_blank())

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