my_gapminder: Gapminder data.
In Cherry-ty-Pan/STAT302package: Stat 302 Project 3

Description Usage Format Source See Also Examples

Excerpt of the Gapminder data on life expectancy, GDP per capita, and population by country. This data and documentation come from the gapminder package, available as gapminder.

1	my_gapminder

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.

http://www.gapminder.org/data/

country_colors for a nice color scheme for the countries

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

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

# how many unique countries does the data contain, by continent?
my_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?
my_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)) %>%
  dplyr::filter(min_rank(worst_le_delta) < 2) %>%
  arrange(worst_le_delta)
}