library(tidymodels)

## ── Attaching packages ────────────────────────────────────── tidymodels 1.4.1 ──

## ✔ broom        1.0.9     ✔ recipes      1.3.1
## ✔ dials        1.4.2     ✔ rsample      1.3.1
## ✔ dplyr        1.1.4     ✔ tailor       0.1.0
## ✔ ggplot2      4.0.0     ✔ tidyr        1.3.1
## ✔ infer        1.0.9     ✔ tune         2.0.0
## ✔ modeldata    1.5.1     ✔ workflows    1.3.0
## ✔ parsnip      1.3.3     ✔ workflowsets 1.1.1
## ✔ purrr        1.1.0     ✔ yardstick    1.3.2

## Warning: package 'ggplot2' was built under R version 4.5.2

## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ purrr::discard() masks scales::discard()
## ✖ dplyr::filter()  masks stats::filter()
## ✖ dplyr::lag()     masks stats::lag()
## ✖ recipes::step()  masks stats::step()

setwd("C:/Users/sarah/Downloads")
nations <- read.csv("nations.csv")

nations <- nations |>
  mutate(gdp = (gdp_percap*population)/1000000000000)
head(nations, 10)

##    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
## 7     AD   AND Andorra 2004         NA      78337       10.9                2.0
## 8     AD   AND Andorra 2010         NA      84419        9.8                1.7
## 9     AD   AND Andorra 2001         NA      67770       11.8                2.1
## 10    AD   AND Andorra 2002         NA      71046       11.2                2.1
##                   region      income gdp
## 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
## 7  Europe & Central Asia High income  NA
## 8  Europe & Central Asia High income  NA
## 9  Europe & Central Asia High income  NA
## 10 Europe & Central Asia High income  NA

Graph 1

firstplot <- nations |>
  filter(country %in% c("China", "Japan", "Germany", "United States")) 
ggplot(firstplot, aes(x = year, y = gdp, group = country, color = country)) +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Set1") +
  labs(title = "China's Rise to Become the Largest Economy", y = "GDP ($trillion)")

Graph 2

secondplot <- nations |>
  group_by(region, year) |>
  summarise(GDP = sum(gdp, na.rm = TRUE))

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

secondplot

## # 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

ggplot(secondplot, aes(x = year, y = GDP, group = region, fill = region)) +
  geom_area(color = "white") +
  scale_fill_brewer(palette = "Set2") +
  labs(title = "GDP by World Bank Region", y = "GDP ($trillion)")

The difference between fill and color when making a graph is that fill colores in the entirety of a shape in the graph, whereas color adds a colored border to a shape.

Nations Charts Assignment | Assignment 6

Muhammad Ahtisham

2026-03-18

Graph 1

Graph 2