SOWC Demographics Data.

Description

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

Usage

sowc_demographics

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.

Source

United Nations Children's Emergency Fund (UNICEF)

Examples

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"
  )