Assignment 6 Pt.2

Loading Nations Dataset

setwd("~/Documents/EC/Spring 2026/DATA 110")

nations <- read.csv("nations.csv")
head(nations)

  iso2c iso3c country year gdp_percap population birth_rate neonat_mortal_rate
1    AD   AND Andorra 1996         NA      64291       10.9                2.8
2    AD   AND Andorra 1994         NA      62707       10.9                3.2
3    AD   AND Andorra 2003         NA      74783       10.3                2.0
4    AD   AND Andorra 1990         NA      54511       11.9                4.3
5    AD   AND Andorra 2009         NA      85474        9.9                1.7
6    AD   AND Andorra 2011         NA      82326         NA                1.6
                 region      income
1 Europe & Central Asia High income
2 Europe & Central Asia High income
3 Europe & Central Asia High income
4 Europe & Central Asia High income
5 Europe & Central Asia High income
6 Europe & Central Asia High income

Loading Libraries

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(ggplot2)

Graph 1

nations_trillion <- nations |>
  mutate(gdp_trillion = gdp_percap*population/1000000000000)
head(nations_trillion)

  iso2c iso3c country year gdp_percap population birth_rate neonat_mortal_rate
1    AD   AND Andorra 1996         NA      64291       10.9                2.8
2    AD   AND Andorra 1994         NA      62707       10.9                3.2
3    AD   AND Andorra 2003         NA      74783       10.3                2.0
4    AD   AND Andorra 1990         NA      54511       11.9                4.3
5    AD   AND Andorra 2009         NA      85474        9.9                1.7
6    AD   AND Andorra 2011         NA      82326         NA                1.6
                 region      income gdp_trillion
1 Europe & Central Asia High income           NA
2 Europe & Central Asia High income           NA
3 Europe & Central Asia High income           NA
4 Europe & Central Asia High income           NA
5 Europe & Central Asia High income           NA
6 Europe & Central Asia High income           NA

graph_1 <- nations_trillion |>
  filter(country %in% c("United States", "Japan", "Germany", "China")) |>
  ggplot(aes(x = year, y = gdp_trillion, fill = country, color = country)) +
  geom_line() +
  geom_point() +
  scale_color_brewer(palette = "Set1")
graph_1

Graph 2

graph_2 <- nations_trillion |>
  group_by(region, year) |>
  summarise(GDP = sum(gdp_trillion, na.rm = TRUE))

`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by region and year.
ℹ Output is grouped by region.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(region, year))` for per-operation grouping
  (`?dplyr::dplyr_by`) instead.

graph_2 |>
  ggplot(aes(x = year, y = GDP, fill = region, color = region)) +
  geom_area(colour="white") +
  scale_fill_brewer(palette = "Set2")

graph_2

# A tibble: 175 × 3
# Groups:   region [7]
   region               year   GDP
   <chr>               <int> <dbl>
 1 East Asia & Pacific  1990  5.52
 2 East Asia & Pacific  1991  6.03
 3 East Asia & Pacific  1992  6.50
 4 East Asia & Pacific  1993  7.04
 5 East Asia & Pacific  1994  7.64
 6 East Asia & Pacific  1995  8.29
 7 East Asia & Pacific  1996  8.96
 8 East Asia & Pacific  1997  9.55
 9 East Asia & Pacific  1998  9.60
10 East Asia & Pacific  1999 10.1 
# ℹ 165 more rows