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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