Last data update: 2014.03.03
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R: Gapminder data.
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
The supplemental data frame gapminder_unfiltered was not
filtered on year or for complete data and has 3313 rows.
Source
http://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)
}
Results
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