% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-sowc_demographics.R
\docType{data}
\name{sowc_demographics}
\alias{sowc_demographics}
\title{SOWC Demographics Data.}
\format{
A data frame with 202 rows and 18 variables.
\describe{
\item{countries_and_areas}{Country or area name.}
\item{total_pop_2018}{Population in 2018 in thousands.}
\item{under18_pop_2018}{Population under age 18 in 2018 in thousands.}
\item{under5_pop_2018}{Population under age 5 in 2018 in thousands.}
\item{pop_growth_rate_2018}{Rate at which population is growing in 2018.}
\item{pop_growth_rate_2030}{Rate at which population is estimated to grow
in 2030.}
\item{births_2018}{Number of births in 2018 in thousands.}
\item{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. }
\item{life_expectancy_1970}{Life expectancy at birth in 1970.}
\item{life_expectancy_2000}{Life expectancy at birth in 2000.}
\item{life_expectancy_2018}{Life expectancy at birth in 2018.}
\item{dependency_ratio_total}{The ratio of the not-working-age
population to the working-age population of 15 - 64 years.}
\item{dependency_ratio_child}{The ratio of the under 15 population
to the working-age population of 15 - 64 years.}
\item{dependency_ratio_oldage}{The ratio of the  over 64 population
to the working-age population of 15 - 64 years.}
\item{percent_urban_2018}{Percent of population living in urban areas.}
\item{pop_urban_growth_rate_2018}{Annual urban population growth rate
from 2000 to 2018.}
\item{pop_urban_growth_rate_2030}{Estimated annual urban population growth
rate from 2018 to 2030.}
\item{migration_rate}{Net migration rate per 1000 population from
2015 to 2020.}
}
}
\source{
\href{https://data.unicef.org/resources/dataset/sowc-2019-statistical-tables/}{United Nations Children's Emergency Fund (UNICEF)}
}
\usage{
sowc_demographics
}
\description{
Demographic data from UNICEF's State of the World's Children 2019 Statistical Tables.
}
\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"
  )
}
\keyword{datasets}
