nations

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── 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(knitr)

# Set working directory
setwd("/Users/rahwahagos/Downloads/Hate_Crimes")

# Load the nations dataset
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_trillion = (gdp_percap * population) / 1e12)

# Check the new variable
summary(nations$gdp_trillion)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
 0.00001  0.00774  0.03244  0.32594  0.18490 18.08291      766 

nations |>
  group_by(country) |>
  summarize(max_gdp = max(gdp_trillion, na.rm = TRUE)) |>
  arrange(desc(max_gdp)) |>
  head(10)

Warning: There were 21 warnings in `summarize()`.
The first warning was:
ℹ In argument: `max_gdp = max(gdp_trillion, na.rm = TRUE)`.
ℹ In group 4: `country = "American Samoa"`.
Caused by warning in `max()`:
! no non-missing arguments to max; returning -Inf
ℹ Run `dplyr::last_dplyr_warnings()` to see the 20 remaining warnings.

# A tibble: 10 × 2
   country            max_gdp
   <chr>                <dbl>
 1 China                18.1 
 2 United States        17.3 
 3 India                 7.35
 4 Japan                 4.66
 5 Germany               3.76
 6 Russian Federation    3.63
 7 Brazil                3.29
 8 Indonesia             2.69
 9 France                2.60
10 United Kingdom        2.60

chart1 <- nations |>
  
  filter(country %in% c("China", "United States", "India", "Japan")) |>
  
  
  ggplot(aes(x = year, y = gdp_trillion, color = country)) +
  geom_line(size = 1.2) +
  geom_point(size = 2, alpha = 0.7) +
  
  
  scale_color_brewer(palette = "Set1") +
  
  labs(
    title = "China's Rise to Become the Largest Economy",
    subtitle = "GDP in trillions of US dollars (constant)",
    x = "Year",
    y = "GDP (trillion $)",
    color = "Country",
    caption = "Data source: Nations dataset (gapminder)"
  ) +
  
  theme_minimal() +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5)
  )

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

print(chart1)

chart2 <- nations |>
  group_by(region, year) |>
  summarize(gdp_trillion = sum(gdp_trillion, na.rm = TRUE), .groups = "drop") |>
  
  ggplot(aes(x = year, y = gdp_trillion, fill = region)) +
  geom_area(alpha = 0.8, color = "white", size = 0.2) +
  
  scale_fill_brewer(palette = "Set2") +
  
  labs(
    title = "GDP by World Bank Region",
    subtitle = "GDP in trillions of US dollars (constant)",
    x = "Year",
    y = "GDP (trillion $)",
    fill = "Region",
    caption = "Data source: Nations dataset (gapminder)"
  ) +
  
  theme_minimal() +
  theme(
    legend.position = "right",
    plot.title = element_text(hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5)
  )

print(chart2)

Chart 1: Major Economies
This line chart with points shows the GDP growth of the four largest economies over time. China's remarkable rise is evident, surpassing the United States to become the world's largest economy. India also shows strong growth, particularly in recent decades.
Chart 2: Regional GDP
The area chart displays total GDP by World Bank region over time. North America and Europe & Central Asia have historically dominated global GDP, while East Asia & Pacific has shown dramatic growth, particularly since the 1990s, reflecting China's economic expansion. Sub-Saharan Africa and South Asia remain smaller economies, though they show consistent growth.

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