SOWC Child Mortality Data.

Description

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

Usage

sowc_child_mortality

Format

A data frame with 195 rows and 19 variables.

countries_and_areas

Country or area name.

under5_mortality_1990

Under-5 mortality rate (deaths per 1,000 live births) in 1990.

under5_mortality_2000

Under-5 mortality rate (deaths per 1,000 live births) in 2000.

under5_mortality_2018

Under-5 mortality rate (deaths per 1,000 live births) in 2018.

under5_reduction

Annual rate of reduction in under-5 mortality rate (%)2000–2018.

under5_mortality_2018_male

Under-5 mortality rate male (deaths per 1,000 live births) 2018.

under5_mortality_2018_female

Under-5 mortality rate female (deaths per 1,000 live births) 2018.

infant_mortality_1990

Infant mortality rate (deaths per 1,000 live births) 1990

infant_mortality_2018

Infant mortality rate (deaths per 1,000 live births) 2018

neonatal_mortality_1990

Neonatal mortality rate (deaths per 1,000 live births) 1990.

neonatal_mortality_2000

Neonatal mortality rate (deaths per 1,000 live births) 2000.

neonatal_mortality_2018

Neonatal mortality rate (deaths per 1,000 live births) 2018.

prob_dying_age5to14_1990

Probability of dying among children aged 5–14 (deaths per 1,000 children aged 5) 1990.

prob_dying_age5to14_2018

Probability of dying among children aged 5–14 (deaths per 1,000 children aged 5) 2018.

under5_deaths_2018

Annual number of under-5 deaths (thousands) 2018.

neonatal_deaths_2018

Annual number of neonatal deaths (thousands) 2018.

neonatal_deaths_percent_under5

Neonatal deaths as proportion of all under-5 deaths (%) 2018.

age5to14_deaths_2018

Number of deaths among children aged 5–14 (thousands) 2018.

Source

United Nations Children's Emergency Fund (UNICEF)

Examples

library(dplyr)
library(ggplot2)

# List countries and areas whose children aged 5 and under have a higher probability of dying in
# 2018 than they did in 1990
sowc_child_mortality |>
  mutate(decrease_prob_dying = prob_dying_age5to14_1990 - prob_dying_age5to14_2018) |>
  select(countries_and_areas, decrease_prob_dying) |>
  filter(decrease_prob_dying < 0) |>
  arrange(decrease_prob_dying)

# List countries and areas and their relative rank for neonatal mortality in 2018
sowc_child_mortality |>
  mutate(rank = round(rank(-neonatal_mortality_2018))) |>
  select(countries_and_areas, rank, neonatal_mortality_2018) |>
  arrange(rank)