Use ?tidyr_tidy_select
functions like where(is.numeric)
!
dropouts %>%
group_by(ETHNIC) %>%
summarize(across( where(is.numeric), ~ sum(.x, na.rm = TRUE)))
# A tibble: 9 × 18
ETHNIC E7 E8 E9 E10 E11 E12 EUS ETOT D7 D8
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 2922 2709 3140 3291 2776 2345 43 11595 66 52
2 1 2523 2589 2808 2886 2889 3020 33 11636 16 15
3 2 44199 43898 43343 43757 45840 43001 596 176537 69 37
4 3 2390 2284 2436 2352 2471 2571 42 9872 9 7
5 4 12210 12883 13429 14037 14273 14469 233 56441 12 8
6 5 254745 252583 264302 260201 252458 253193 2680 1032834 682 550
7 6 26383 26786 28497 28731 28696 30799 485 117208 217 164
8 7 113498 115139 115593 117592 119308 121771 1829 476093 323 267
9 9 14704 13469 13999 13238 12810 13000 138 53185 59 38
# ℹ 7 more variables: D9 <dbl>, D10 <dbl>, D11 <dbl>, D12 <dbl>, DUS <dbl>,
# DTOT <dbl>, YEAR <dbl>