Gapminder data.

Excerpt of the Gapminder data on life expectancy, GDP per capita, and population by country.

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

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

Examples

str(gapminder)
#> Classes ‘tbl_df’, ‘tbl’ and '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 ...
head(gapminder)
#> # A tibble: 6 x 6
#>   country     continent  year lifeExp      pop gdpPercap
#>   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
#> 1 Afghanistan Asia       1952    28.8  8425333      779.
#> 2 Afghanistan Asia       1957    30.3  9240934      821.
#> 3 Afghanistan Asia       1962    32.0 10267083      853.
#> 4 Afghanistan Asia       1967    34.0 11537966      836.
#> 5 Afghanistan Asia       1972    36.1 13079460      740.
#> 6 Afghanistan Asia       1977    38.4 14880372      786.
summary(gapminder)
#>         country        continent        year         lifeExp     
#>  Afghanistan:  12   Africa  :624   Min.   :1952   Min.   :23.60  
#>  Albania    :  12   Americas:300   1st Qu.:1966   1st Qu.:48.20  
#>  Algeria    :  12   Asia    :396   Median :1980   Median :60.71  
#>  Angola     :  12   Europe  :360   Mean   :1980   Mean   :59.47  
#>  Argentina  :  12   Oceania : 24   3rd Qu.:1993   3rd Qu.:70.85  
#>  Australia  :  12                  Max.   :2007   Max.   :82.60  
#>  (Other)    :1632                                                
#>       pop              gdpPercap       
#>  Min.   :6.001e+04   Min.   :   241.2  
#>  1st Qu.:2.794e+06   1st Qu.:  1202.1  
#>  Median :7.024e+06   Median :  3531.8  
#>  Mean   :2.960e+07   Mean   :  7215.3  
#>  3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
#>  Max.   :1.319e+09   Max.   :113523.1  
#>                                        
table(gapminder$continent)
#> 
#>   Africa Americas     Asia   Europe  Oceania 
#>      624      300      396      360       24 
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
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)
}
#> # A tibble: 5 x 3
#> # Groups:   continent [5]
#>   continent country     worst_le_delta
#>   <fct>     <fct>                <dbl>
#> 1 Africa    Rwanda             -20.4  
#> 2 Asia      Cambodia            -9.10 
#> 3 Americas  El Salvador         -1.51 
#> 4 Europe    Montenegro          -1.46 
#> 5 Oceania   Australia            0.170

Format

Source

See also

Examples

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