dplyr-1-quantile.R

library(dplyr, warn.conflicts = FALSE)
library(gapminder)
probs <- c(0.1, 0.5, 0.9)

gapminder %>% 
  group_by(continent) %>% 
  summarise(
    probs = probs,
    across(is.numeric & !year, ~ quantile(.x, probs))
  )
#> # A tibble: 15 x 5
#>    continent probs lifeExp        pop gdpPercap
#>    <fct>     <dbl>   <dbl>      <dbl>     <dbl>
#>  1 Africa      0.1    38.4    489931.      534.
#>  2 Africa      0.5    47.8   4579311      1192.
#>  3 Africa      0.9    60.8  26426121.     4857.
#>  4 Americas    0.1    50.6   1770851.     2321.
#>  5 Americas    0.5    67.0   6227510      5466.
#>  6 Americas    0.9    74.9  57318302.    12519.
#>  7 Asia        0.1    42.9   1310774       680.
#>  8 Asia        0.5    61.8  14530830.     2647.
#>  9 Asia        0.9    74.2 123822278.    20742.
#> 10 Europe      0.1    65.9   1997695      4206.
#> 11 Europe      0.5    72.2   8551125     12082.
#> 12 Europe      0.9    78.3  55026110.    28415.
#> 13 Oceania     0.1    70.3   2560430     11330.
#> 14 Oceania     0.5    73.7   6403492.    17983.
#> 15 Oceania     0.9    79.9  18240263.    26454.