Nations Charts Assignments

library(tidyverse)

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library(ggplot2)
library(RColorBrewer)

setwd("C:/Users/karen/OneDrive/Desktop/DATA 110")
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.

5 Countries to have hosted FIFA World Cup Twice

Nat_wgdpercap <- nations %>%
  select(country, year, gdp_percap, population, region) %>%
  mutate(gdp_trillions = (gdp_percap * population) / 1000000000000)

filt <- Nat_wgdpercap %>%
  filter(country %in% c("Brazil", "Italy", "France", "Mexico", "Germany" ))

WCFIFA <- ggplot(filt, aes(x = year, y = gdp_trillions, col = country)) +
  scale_color_brewer(palette = "Set1") +
  xlab("Year") +
  ylab("GDP in Trillions of Dollars")  +
  theme_bw( base_size = 12)

WCFIFA + 
  geom_point() +
  geom_line() 

Nat_2 <- Nat_wgdpercap %>%
  filter(year >= 2000) %>%
  group_by(region, year) %>%
  summarise(GDP = sum(gdp_trillions, na.rm = TRUE))

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

Thebear <- ggplot(Nat_2, aes(x = year, y = GDP, fill = region)) +
  scale_fill_brewer(palette = "Set2") +
  xlab("Year") +
  ylab("GDP in Trillions of Dollars") +
  theme_bw(base_size = 12)

Thebear + 
  geom_area(outline.type = "full", col = "BLACK") + 
  ggtitle("GDP by World Bank Region")