Gapminder data

Description

Excerpt of the Gapminder data on life expectancy, GDP per capita, and population by country.

Usage

gapminder

Format

The main data frame gapminder has 1704 rows and 6 variables:

country

factor with 142 levels

continent

factor with 5 levels

year

ranges from 1952 to 2007 in increments of 5 years

lifeExp

life expectancy at birth, in years

pop

population

gdpPercap

GDP per capita (US$, inflation-adjusted)

The supplemental data frame gapminder_unfiltered was not filtered on year or for complete data and has 3313 rows.

Source

https://www.gapminder.org/data/

See Also

country_colors for a nice color scheme for the countries

Examples

str(gapminder)
head(gapminder)
summary(gapminder)
table(gapminder$continent)
aggregate(lifeExp ~ continent, gapminder, median)
plot(lifeExp ~ year, gapminder, subset = country == "Cambodia", type = "b")
plot(lifeExp ~ gdpPercap, gapminder, subset = year == 2007, log = "x")

if (require("dplyr")) {
  gapminder %>%
    filter(year == 2007) %>%
    group_by(continent) %>%
    summarise(lifeExp = median(lifeExp))

  # how many unique countries does the data contain, by continent?
  gapminder %>%
    group_by(continent) %>%
    summarize(n_obs = n(), n_countries = n_distinct(country))

  # by continent, which country experienced the sharpest 5-year drop in
  # life expectancy and what was the drop?
  gapminder %>%
    group_by(continent, country) %>%
    select(country, year, continent, lifeExp) %>%
    mutate(le_delta = lifeExp - lag(lifeExp)) %>%
    summarize(worst_le_delta = min(le_delta, na.rm = TRUE)) %>%
    filter(min_rank(worst_le_delta) < 2) %>%
    arrange(worst_le_delta)
}