Nations_Final_Assignment

Author

N Bellot Norman

Published

June 21, 2024

How the “Most Unique Countries” Were Selected

U.S. News & World Report conducted a global study to rank the most unique countries worldwide. The rankings project measures global perceptions and defines countries in terms of several qualitative characteristics, such as impressions that have the potential to increase trade, travel, and investment (https://www.usnews.com/news/best-countries/articles/methodology). The results identified the top four countries: Egypt, India, the United Arab Emirates, and Qatar. For this reason, I selestudycted these countries. The dataset had some interesting elements. All except Egypt were ranked as high-income. Egypt ranked as lower middle income.

Load libraries

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ 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(RColorBrewer)

Load the dataset

setwd("C:/Users/naomi/OneDrive/Desktop/Desktop of 11-08-2022/Community College Classes/DATA 110/Nations")
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.

tibble(nations)

# A tibble: 5,275 × 10
   iso2c iso3c country  year gdp_percap population birth_rate neonat_mortal_rate
   <chr> <chr> <chr>   <dbl>      <dbl>      <dbl>      <dbl>              <dbl>
 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  
 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  
 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
# ℹ 5,265 more rows
# ℹ 2 more variables: region <chr>, income <chr>

Filter Data

nations <- nations %>%
  mutate(desired_countries = case_when(
    country %in% c("Egypt, Arab Rep.", "Qatar", "United Arab Emirates", "India") ~ country,
    !(region %in% c("Latin America & Caribbean", "East Asia & Pacific", "Europe & Central Asia", "Sub-Saharan Africa", "Middle East & North Africa")) ~ "Others",
    TRUE ~ NA_character_
  )) %>%
  filter(!is.na(desired_countries)) %>%
  mutate(group = factor(desired_countries, levels = rev(c("Egypt", "Arab Rep.", "Qatar", "United Arab Emirates", "India"))))

Add GDP in Trillions formula

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

nations <- nations %>% filter(desired_countries %in% c("Egypt, Arab Rep.", "Qatar", "United Arab Emirates", "India"))

Visual Display for GDP in Trillions by Desired Countries

ggplot(nations, aes(x = year, y = GDP_trillions, color = desired_countries)) +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Set1") +
  labs(title ="Desired Countries: A Longitudinal Analysis",
  caption = "Source: World Bank") +
  xlab("Years") +
  ylab("GDP in Trillions")

Warning: Removed 10 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 10 rows containing missing values or values outside the scale range
(`geom_line()`).

grouped_nations <- nations %>%
  mutate(GDP_trillions = (gdp_percap * population) / 1e12) %>%
  group_by(region, year) %>%
  summarise(GDP = sum(GDP_trillions, na.rm = TRUE))

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

Visual display of GPD in Trillions by Region Over the Years

ggplot(grouped_nations, aes(x = year, y = GDP, fill = region)) +
  geom_area(color = "white") +
  scale_fill_brewer(palette = "Set2") +
  labs(title = "GDP in Trillions by Region Over Years \n A Longitudinal Review",
  caption = "Source: World Bank") +
  xlab("Years") +
  ylab("GDP in Trillions") +
  theme_classic() + 
  theme(text = element_text(face = "bold", color = "darkgreen", family = "serif"),
  geom_line(alpha =0.9, color = "white"),
        axis.line = element_line(color = "darkgreen", linewidth = 1.0, linetype =     "solid"),
legend.position = c(0.2, 0.7)) +
  guides(fill = guide_legend(title = "Region", title.position = "top", title.hjust = 0.5))

Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.