Nneka’s Nation Plots

Nneka’s Nation Plots

Load Libraries

library(tidyverse)
library(dplyr)
library(ggplot2)
library(plotly)
nations <- read_csv("/Users/nnokigbo/Downloads/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.

head(nations)

Creating mutation for plot 1

nations1 <- nations |> 
  mutate(gdp = (gdp_percap * population) / 1000000000000) |>
  filter(country %in% c("China", "Germany", "Japan", "United States"))
head(nations1)

Plot 1

plot1 <- nations1 |>
  ggplot(aes(x = year, y = gdp, color = country)) +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Set1") +
  labs(
    x = "Year",
    y = "GDP ($Trillions)",
    title = "China's Rise to Become the Largest Economy"
  )
plot1 <- ggplotly(plot1)
plot1

Summarization of plot 2

nations2 <- nations |> 
  mutate(gdp = (gdp_percap * population) / 1000000000000) |>
  group_by(year, region) |>
  summarise(sum_GDP = sum(gdp, na.rm = TRUE))

head(nations2)
  

Plot 2

plot2 <- nations2 |>
  ggplot(aes(x = year, y = sum_GDP, fill = region)) +
  geom_area(color = "white") +
  scale_fill_brewer(palette = "Set2") +
  labs(
    title = "GDP by World Bank Region",
    x = "Year",
    y = "GDP ($Trillion)"
  )

plot2 <- ggplotly(plot2)
plot2