nations hw

Author

Emma Poch

setwd("C:/Users/emmap/Downloads/DATA110")
library(readr)
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.

library(ggplot2)
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

nations2 <- nations |>
  filter(!is.na(gdp_percap)) |>
  mutate(gdp = ((gdp_percap*population)/(10^12)))

Graph 1

graph1 <- nations2 |>
  filter(country == c("Singapore", "Korea, Rep.", "Oman", "Romania")) |>
  ggplot(aes(x = year, y = gdp, col = country)) +
  geom_point(size = 2)+
  geom_line()+
  scale_color_brewer(palette = "Set1")+
  theme_minimal()+
  labs(x = "Year", y = "GDP ($ Trillion)", title = "South Korea Stands Out in GDP Growth")

Warning: There was 1 warning in `filter()`.
ℹ In argument: `country == c("Singapore", "Korea, Rep.", "Oman", "Romania")`.
Caused by warning in `country == c("Singapore", "Korea, Rep.", "Oman", "Romania")`:
! longer object length is not a multiple of shorter object length

graph1

# I chose these specific countries because they've all experienced radical increases in their economic growth within the past 50-60 years. Despite them all experiencing significant changes compared to their own previous economies, I wanted to see how they compared to each other on the global scale.
# Source: https://ourworldindata.org/economic-growth-since-1950

Graph 2

graph2 <- nations2 |>
  group_by(region, year) |>
  summarise(sum_gdp = sum(gdp)) |>
  ggplot(aes(x = year, y = sum_gdp, fill = region))+
  geom_area(color = "white")+
  scale_fill_brewer(palette = "Set2")+
  theme_minimal()+
  labs(x = "Year", y = "GDP ($ Trillion)", title = "GDP by World Bank Region")

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

graph2