• gapminder
  • Install and test drive
  • Color schemes for countries and continents
  • How to use color scheme in ggplot2
  • How to use color scheme in base graphics
  • ISO 3166-1 country codes
  • What is gapminder good for?
  • How this sausage was made
  • Plain text delimited files
  • License
  • Citation

gapminder

Excerpt from the Gapminder data. The main object in this package is the gapminder data frame or "tibble". There are other goodies, such as the data in tab delimited form, a larger unfiltered dataset, premade color schemes for the countries and continents, and ISO 3166-1 country codes.

The gapminder data frames include six variables, (Gapminder.org documentation page):

Per-capita GDP (Gross domestic product) is given in units of international dollars, "a hypothetical unit of currency that has the same purchasing power parity that the U.S. dollar had in the United States at a given point in time" -- 2005, in this case.

Package contains two main data frames or tibbles:

• gapminder: 12 rows for each country (1952, 1955, ..., 2007). It's a subset of ...
• gapminder_unfiltered: more lightly filtered and therefore about twice as many rows.

Note: this package exists for the purpose of teaching and making code examples. It is an excerpt of data found in specific spreadsheets on Gapminder.org circa 2010. It is not a definitive source of socioeconomic data and I don't update it. Use other data sources if it's important to have the current best estimate of these statistics.

Install and test drive

Install gapminder from CRAN:

install.packages("gapminder")

Or you can install gapminder from GitHub:

devtools::install_github("jennybc/gapminder")

Load it and test drive with some data aggregation and plotting:

library("gapminder")

aggregate(lifeExp ~ continent, gapminder, median)
#>   continent lifeExp
#> 1    Africa 47.7920
#> 2  Americas 67.0480
#> 3      Asia 61.7915
#> 4    Europe 72.2410
#> 5   Oceania 73.6650

library("dplyr")
gapminder %>%
    filter(year == 2007) %>%
    group_by(continent) %>%
    summarise(lifeExp = median(lifeExp))
#> # A tibble: 5 x 2
#>   continent lifeExp
#>      <fctr>   <dbl>
#> 1    Africa 52.9265
#> 2  Americas 72.8990
#> 3      Asia 72.3960
#> 4    Europe 78.6085
#> 5   Oceania 80.7195
    
library("ggplot2")
ggplot(gapminder, aes(x = continent, y = lifeExp)) +
  geom_boxplot(outlier.colour = "hotpink") +
  geom_jitter(position = position_jitter(width = 0.1, height = 0), alpha = 1/4)

Color schemes for countries and continents

country_colors and continent_colors are provided as character vectors where elements are hex colors and the names are countries or continents.

head(country_colors, 4)
#>          Nigeria            Egypt         Ethiopia Congo, Dem. Rep. 
#>        "#7F3B08"        "#833D07"        "#873F07"        "#8B4107"
head(continent_colors)
#>    Africa  Americas      Asia    Europe   Oceania 
#> "#7F3B08" "#A50026" "#40004B" "#276419" "#313695"

The country scheme is available in this repo as

• PNG or PDF
• continent-colors.tsv and country-colors.tsv

How to use color scheme in ggplot2

Provide country_colors to scale_color_manual() like so:

... + scale_color_manual(values = country_colors) + ...

library("ggplot2")

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)))

How to use color scheme in 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])

ISO 3166-1 country codes

The country_codes data frame provides ISO 3166-1 country codes for all the countries in the gapminder and gapminder_unfiltered data frames. This can be used to practice joining or merging.

library(dplyr)

gapminder %>%
 filter(year == 2007, country %in% c("Kenya", "Peru", "Syria")) %>%
 select(country, continent) %>% 
 left_join(country_codes)
#> Warning: Column `country` joining factor and character vector, coercing
#> into character vector
#> # A tibble: 3 x 4
#>   country continent iso_alpha iso_num
#>     <chr>    <fctr>     <chr>   <int>
#> 1   Kenya    Africa       KEN     404
#> 2    Peru  Americas       PER     604
#> 3   Syria      Asia       SYR     760

What is gapminder good for?

I have used this excerpt in STAT 545 since 2008 and, more recently, in R-flavored Software Carpentry Workshops and a ggplot2 tutorial. gapminder is very useful for teaching novices data wrangling and visualization in R.

Description:

• 1704 observations; fills a size niche between iris (150 rows) and the likes of diamonds (54K rows)
• 6 variables
  • country a factor with 142 levels
  • continent, a factor with 5 levels
  • year: going from 1952 to 2007 in increments of 5 years
  • pop: population
  • gdpPercap: GDP per capita
  • lifeExp: life expectancy

There are 12 rows for each country in gapminder, i.e. complete data for 1952, 1955, ..., 2007.

The two factors provide opportunities to demonstrate factor handling, in aggregation and visualization, for factors with very few and very many levels.

The four quantitative variables are generally quite correlated with each other and these trends have interesting relationships to country and continent, so you will find that simple plots and aggregations tell a reasonable story and are not completely boring.

Visualization of the temporal trends in life expectancy, by country, is particularly rewarding, since there are several countries with sharp drops due to political upheaval. This then motivates more systematic investigations via data aggregation to proactively identify all countries whose data exhibits certain properties.

How this sausage was made

Plain text delimited files

If you want to practice importing from file, various tab delimited files are included:

• gapminder.tsv: the same dataset available via library("gapminder"); gapminder
• gapminder-unfiltered.tsv: the larger dataset available via library("gapminder"); gapminder_unfiltered.
• continent-colors.tsv and country-colors.tsv: color schemes

Here in the source, these delimited files can be found:

• in the inst/extdata/ sub-directory

Once you've installed the gapminder package they can be found locally and used like so:

gap_tsv <- system.file("extdata", "gapminder.tsv", package = "gapminder")
gap_tsv <- read.delim(gap_tsv)
str(gap_tsv)
#> 'data.frame':    1704 obs. of  6 variables:
#>  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
#>  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
#>  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
#>  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
#>  $ gdpPercap: num  779 821 853 836 740 ...
gap_tsv %>% # Bhutan did not make the cut because data for only 8 years :(
  filter(country == "Bhutan")
#> [1] country   continent year      lifeExp   pop       gdpPercap
#> <0 rows> (or 0-length row.names)

gap_bigger_tsv <-
  system.file("extdata", "gapminder-unfiltered.tsv", package = "gapminder")
gap_bigger_tsv <- read.delim(gap_bigger_tsv)
str(gap_bigger_tsv)
#> 'data.frame':    3313 obs. of  6 variables:
#>  $ country  : Factor w/ 187 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ continent: Factor w/ 6 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
#>  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
#>  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
#>  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
#>  $ gdpPercap: num  779 821 853 836 740 ...
gap_bigger_tsv %>% # Bhutan IS here though! :)
  filter(country == "Bhutan")
#>   country continent year lifeExp     pop gdpPercap
#> 1  Bhutan      Asia 1972  41.837 1087991  807.6226
#> 2  Bhutan      Asia 1977  44.708 1205659  816.3102
#> 3  Bhutan      Asia 1982  47.872 1333704  946.8130
#> 4  Bhutan      Asia 1987  50.717 1490857 1494.2901
#> 5  Bhutan      Asia 1992  54.471 1673428 1904.1795
#> 6  Bhutan      Asia 1997  58.929 1876236 2561.5077
#> 7  Bhutan      Asia 2002  63.458 2094176 3256.0193
#> 8  Bhutan      Asia 2007  65.625 2327849 4744.6400

License

Gapminder's data is released under the Creative Commons Attribution 3.0 Unported license. See their terms of use.

Citation

Run this command to get info on how to cite this package. If you've installed gapminder from CRAN, the year will be populated and populated correctly (unlike below).

citation("gapminder")
#> 
#> To cite package 'gapminder' in publications use:
#> 
#>   Jennifer Bryan (NA). gapminder: Data from Gapminder.
#>   https://github.com/jennybc/gapminder,
#>   http://www.gapminder.org/data/,
#>   https://doi.org/10.5281/zenodo.594018.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {gapminder: Data from Gapminder},
#>     author = {Jennifer Bryan},
#>     note = {https://github.com/jennybc/gapminder,
#> http://www.gapminder.org/data/,
#> https://doi.org/10.5281/zenodo.594018},
#>   }