nations hw 110

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dplyr)
library(ggplot2)

setwd("C:/Users/asman/Documents/data110") #setting working directory
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 <- nations %>%
  mutate(gdp = gdp_percap * population / 1e12) #mutating GDP, I used chatGPT to help

#creating first chart
countries <- nations %>%
  filter(country %in% c("China", "India", "United States", "Indonesia"))

ggplot(countries, aes(x = year, y = gdp, color = country)) +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Set1") +
  labs(x = "Year", y = "GDP in $ trillion", title = "GDP in the most populated countries")

#creating second chart

nations <- nations %>%
  mutate(gdp = gdp_percap * population / 1e12)

regions <- nations %>%
  group_by(region, year) %>%
  summarize(gdp = sum(gdp, na.rm = TRUE))

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

ggplot(regions, aes(x = year, y = gdp, fill = region)) +
  geom_area(color = "white", linewidth = 1) +
  scale_fill_brewer(palette = "Set2") +
  labs(x = "Year", y = "GDP in $trillions", title = "GDP by World Bank Region")+
  theme_minimal()