Title:	Data from Gapminder
Version:	1.0.1
Description:	An excerpt of the data available at Gapminder.org. For each of 142 countries, the package provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007.
License:	CC0
URL:	https://github.com/jennybc/gapminder, https://www.gapminder.org/data/, https://doi.org/10.5281/zenodo.594018, https://jennybc.github.io/gapminder/
BugReports:	https://github.com/jennybc/gapminder/issues
Depends:	R (≥ 3.1.0)
Imports:	tibble
Suggests:	dplyr, ggplot2 (≥ 3.0.0), knitr, rmarkdown, testthat (≥ 3.0.0)
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-06-12 09:30:15 UTC; jenny
Author:	Jennifer Bryan [aut, cre]
Maintainer:	Jennifer Bryan <jenny@posit.co>
Repository:	CRAN
Date/Publication:	2025-06-12 14:30:06 UTC

Country codes

Description

Country codes

Usage

country_codes

Format

Data frame of Gapminder country names and ISO 3166-1 country codes:

country

Country name.

iso_alpha

The 3-letter ISO 3166-1 alpha-3 code.

iso_num

The 3-digit ISO 3166-1 numeric-3 code.

Also includes the countries covered by the supplemental data frame gapminder_unfiltered.

Examples

if (require("dplyr")) {
  gapminder %>%
    filter(year == 2007, country %in% c("Kenya", "Peru", "Syria")) %>%
    select(country, continent) %>%
    left_join(country_codes)
}

Gapminder color schemes.

Description

Color schemes for the countries and continents in the Gapminder data.

Usage

country_colors

Format

Named character vectors giving country and continent colors:

country_colors

colors for the 142 countries

continent_colors

colors for the 5 continents

See Also

gapminder for a description of the dataset

Examples

# ggplot2 examples are below these base graphics examples!

# using country_colors with base graphics

# for convenience, integrate the country colors into the data.frame
gap_with_colors <-
  data.frame(gapminder,
    cc = I(country_colors[match(
      gapminder$country,
      names(country_colors)
    )])
  )

# bubble plot, focus just on Africa and Europe in 2007
keepers <- with(
  gap_with_colors,
  continent %in% c("Africa", "Europe") & year == 2007
)
plot(lifeExp ~ gdpPercap, gap_with_colors,
  subset = keepers, log = "x", pch = 21,
  cex = sqrt(gap_with_colors$pop[keepers] / pi) / 1500,
  bg = gap_with_colors$cc[keepers]
)

if (require(ggplot2)) {
  # with ggplot2, just provide country_colors to scale_color_manual():
  # ... + scale_color_manual(values = country_colors) + ...

  # simple line plot for 5 countries
  h_countries <- c("Egypt", "Haiti", "Romania", "Thailand", "Venezuela")
  h_dat <- droplevels(subset(gapminder, country %in% h_countries))
  h_dat$country <- with(h_dat, reorder(country, lifeExp, max))
  ggplot(h_dat, aes(x = year, y = lifeExp)) +
    geom_line(aes(color = country)) +
    scale_colour_manual(values = country_colors) +
    guides(color = guide_legend(reverse = TRUE))

  # spaghetti plot for lots of countries
  ggplot(
    subset(gapminder, continent != "Oceania"),
    aes(x = year, y = lifeExp, group = country, color = country)
  ) +
    geom_line(lwd = 1, show.legend = FALSE) +
    facet_wrap(~continent) +
    scale_color_manual(values = country_colors) +
    theme_bw() +
    theme(strip.text = element_text(size = rel(1.1)))

  # bubble plot for lots of countries
  gap_bit <- subset(gapminder, year == 2007 & continent != "Oceania")
  gap_bit <- gap_bit[with(gap_bit, order(continent, -1 * pop)), ]
  ggplot(gap_bit, aes(x = gdpPercap, y = lifeExp, size = pop)) +
    scale_x_log10(limits = c(150, 115000)) +
    ylim(c(16, 96)) +
    geom_point(pch = 21, color = "grey20", show.legend = FALSE) +
    scale_size_area(max_size = 40) +
    facet_wrap(~continent) +
    coord_fixed(ratio = 1 / 43) +
    aes(fill = country) +
    scale_fill_manual(values = country_colors) +
    theme_bw() +
    theme(strip.text = element_text(size = rel(1.1)))
}

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)
}

Gapminder data, unfiltered.

Description

The supplemental data frame gapminder_unfiltered was not filtered on year or for complete data and has 3313 rows. Everything else is as documented in gapminder.

Usage

gapminder_unfiltered

Format

An object of class tbl_df (inherits from tbl, data.frame) with 3313 rows and 6 columns.