gapminder | R Documentation |
Excerpt of the Gapminder data on life expectancy, GDP per capita, and population by country.
gapminder
The main data frame gapminder
has 1704 rows and 6 variables:
factor with 142 levels
factor with 5 levels
ranges from 1952 to 2007 in increments of 5 years
life expectancy at birth, in years
population
GDP per capita (US$, inflation-adjusted)
The supplemental data frame gapminder_unfiltered
was not
filtered on year
or for complete data and has 3313 rows.
https://www.gapminder.org/data/
country_colors
for a nice color scheme for the countries
str(gapminder)
head(gapminder)
summary(gapminder)
table(gapminder$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)
}
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