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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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