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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.
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