Maps

Data from https://worldpopulationreview.com/country-rankings/median-income-by-country Import the data and libraries. Clean country names as necessary.

library(dplyr)

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library(sf)

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library(plotly)

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library(ggplot2)
library(viridis)

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library(rworldmap)

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## Loading required package: sp

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## ### Welcome to rworldmap ###

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income <- read.csv("C:/Users/ericp/Downloads/csvData.csv")
colnames(income) <- c("country", "medianIncome", "meanIncome", "gdpPerCapita", "pop2022")
income$country[which(income$country == 'United States')] <- 'United States of America'
income$country[which(income$country == 'DR Congo')] <- 'Democratic Republic of the Congo'
income$country[which(income$country == 'Serbia')] <- 'Republic of Serbia'
head(income)

##                    country medianIncome meanIncome gdpPerCapita    pop2022
## 1               Luxembourg        26321      31376       124590    647.599
## 2     United Arab Emirates        24292      27017        70089   9441.129
## 3                   Norway        22684      25272        70005   5434.319
## 4              Switzerland        21490      25787        72376   8740.472
## 5 United States of America        19306      25332        65297 338289.857
## 6                   Canada        18652      22042        51668  38454.327

Let’s look at the GDP per capita around the world.

world_sf <- st_as_sf(getMap(resolution = "low"))%>%
  left_join(income,by = c("GEOUNIT" = "country"))


map <- ggplot() +
  geom_sf(data = world_sf, mapping = aes(fill = gdpPerCapita, text=sprintf("Country: %s<br>GDP Per Capita: %s", GEOUNIT, gdpPerCapita)), color = "white", size = 0.1) +
  scale_fill_viridis(option='magma', name = "GDP Per Capita") + 
  labs(title = "GDP Per Capita Around the World")

## Warning: Ignoring unknown aesthetics: text

ggplotly(map, tooltip = "text")

There looks to be trends by continent. Most of Africa had GDP less than $10,000, most of South America and Asia $10,000-$20,000, and most of North America, Australia and Europe greater than $20,000.