Nations Dataset

library(tidyverse)

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library(plotly)

Attaching package: 'plotly'

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library(ggfortify)

library(dplyr)
library(ggplot2)
library(RColorBrewer)

setwd("~/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.

gdp of each country in trillions of dollars

nations <- nations %>%
  mutate(gdp_trillions = gdp_percap * population / 10^12)

mutate and group by code reference:

https://youtu.be/GM3u9POqhjU?si=nweIt1H1YfVO0qTs

four desired countries

filtered_data <- nations %>%
  filter(country %in% c("China", "Germany", "Japan", "United States"))

plot 1

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

sum of gdp

region_data <- nations %>%
  group_by(region, year) %>%
  summarise(sum_GDP = sum(gdp_trillions, na.rm = TRUE))

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

plot 2

ggplot(region_data, aes(x = year, y = sum_GDP, fill = region)) +
  geom_area(color = "white", size = 0.1) +  # Thin white line around each area
  scale_fill_brewer(palette = "Set2") +
  labs(title = "GDP by World Bank Region", 
       x = "Year", 
       y = "GDP ($ trillion)") +
  theme_minimal()

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.