sowc_demographics: SOWC Demographics Data.

Description

Demographic data from UNICEF's State of the World's Children 2019 Statistical Tables.

Usage

sowc_demographics

Arguments

Format

A data frame with 202 rows and 18 variables.

countries_and_areas: Country or area name.
total_pop_2018: Population in 2018 in thousands.
under18_pop_2018: Population under age 18 in 2018 in thousands.
under5_pop_2018: Population under age 5 in 2018 in thousands.
pop_growth_rate_2018: Rate at which population is growing in 2018.
pop_growth_rate_2030: Rate at which population is estimated to grow in 2030.
births_2018: Number of births in 2018 in thousands.
fertility_2018: Number of live births per woman in 2018.A total fertility level of 2.1 is called replacement level and represents a level at which the population would remain the same size.
life_expectancy_1970: Life expectancy at birth in 1970.
life_expectancy_2000: Life expectancy at birth in 2000.
life_expectancy_2018: Life expectancy at birth in 2018.
dependency_ratio_total: The ratio of the not-working-age population to the working-age population of 15 - 64 years.
dependency_ratio_child: The ratio of the under 15 population to the working-age population of 15 - 64 years.
dependency_ratio_oldage: The ratio of the over 64 population to the working-age population of 15 - 64 years.
percent_urban_2018: Percent of population living in urban areas.
pop_urban_growth_rate_2018: Annual urban population growth rate from 2000 to 2018.
pop_urban_growth_rate_2030: Estimated annual urban population growth rate from 2018 to 2030.
migration_rate: Net migration rate per 1000 population from 2015 to 2020.

Examples

Run this code

library(dplyr)
library(ggplot2)

# List countries and areas' life expectancy, ordered by rank of life expectancy in 2018
sowc_demographics %>%
  mutate(life_expectancy_change = life_expectancy_2018 - life_expectancy_1970) %>%
  mutate(rank_life_expectancy = round(rank(-life_expectancy_2018), 0)) %>%
  select(
  countries_and_areas, rank_life_expectancy, life_expectancy_2018,
    life_expectancy_change
    ) %>%
  arrange(rank_life_expectancy)

# List countries and areas' migration rate and population, ordered by rank of migration rate
sowc_demographics %>%
  mutate(rank = round(rank(migration_rate))) %>%
  mutate(population_millions = total_pop_2018 / 1000) %>%
  select(countries_and_areas, rank, migration_rate, population_millions) %>%
  arrange(rank)

# Scatterplot of life expectancy v population in 2018
ggplot(sowc_demographics, aes(life_expectancy_1970, life_expectancy_2018, size = total_pop_2018)) +
  geom_point(alpha = 0.5) +
  labs(
    title = "Life Expectancy",
    subtitle = "1970 v. 2018",
    x = "Life Expectancy in 1970",
    y = "Life Expectancy in 2018",
    size = "2018 Total Population"
  )

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