Nations Charts Assignments

Nations Charts Assignments

Load the library

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(dplyr)
library(ggplot2)

Read the data

setwd("~/Downloads/First data 110 assignment_files")
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.

View the data

head(nations)

# A tibble: 6 × 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
# ℹ 2 more variables: region <chr>, income <chr>

GDP variable

nations <- nations |>
  mutate(gdp = gdp_percap * population / 10^12)

Plot 1: Dot-line of some countries

p1 <- nations |>
  filter(country %in% c("Cote d'Ivoire", "Canada", "Spain", "Thailand")) |>
  ggplot(aes(x = year , y = gdp, color = country)) +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Dark2", name = "country", labels = c("Cote d'Ivoire", "Canada", "Spain", "Thailand")) +
  labs(title = "GDP of Some Countries", 
       caption = "Nation Dataset",
       x = "Year",
       y = "GDP($ trillion)") +
  theme_minimal()
p1

Plot 2: Area chart of GDP by world bank region

p2 <- nations |>
  group_by(region, year) |>
  summarise(gdp = sum(gdp, na.rm = TRUE)) |>
  ggplot(aes(x = year, y = gdp, fill = region)) +
  geom_area() +
  scale_fill_brewer(palette = "Set1", name = "region") +
  labs(title = "GDP by world Bank Region", 
       caption = "Nation Dataset",
       x = "Year",
       y = "GDP($ trillion)") +
  theme_minimal()

`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.

p2

Brief Essay

Regarding the countries I selected, I decided to choose one country from each continent. I chose Côte d’Ivoire to represent Africa because it is my country. I selected Canada to represent American because I have a lot of friends there. For Europe, I chose Spain because it is a country that I really like. Finally, I chose Thailand to represent Asia because it is one of the countries I would like to visit.