gapminder | R Documentation |

## 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)
}
```