scaled_mean() calculates scaled row means for a dataframe.
scaled_mean(.x, method = c("r", "c"))a data.frame
The method source: both the pure R and C++ versions are efficient
a numeric vector
library(dplyr)
iris %>%
select_if(is.numeric) %>%
scaled_mean()
#> [1] -1.17102394 -1.39511272 -1.42712540 -1.42712540 -1.17102394 -0.78687174
#> [7] -1.33108735 -1.20303662 -1.58718882 -1.36310004 -0.97894784 -1.23504931
#> [13] -1.45913809 -1.71523955 -0.85089711 -0.59479565 -0.91492248 -1.13901126
#> [19] -0.75485906 -1.01096053 -1.01096053 -1.01096053 -1.42712540 -1.04297321
#> [25] -1.13901126 -1.29907467 -1.10699857 -1.10699857 -1.17102394 -1.33108735
#> [31] -1.33108735 -1.01096053 -0.94693516 -0.81888443 -1.33108735 -1.36310004
#> [37] -1.07498589 -1.23504931 -1.58718882 -1.17102394 -1.20303662 -1.74725223
#> [43] -1.52316345 -1.01096053 -0.85089711 -1.39511272 -1.01096053 -1.42712540
#> [49] -1.01096053 -1.26706199 0.78174972 0.55766094 0.81376240 -0.24265614
#> [55] 0.49363557 0.14149606 0.65369898 -0.72284638 0.49363557 -0.21064345
#> [61] -0.75485906 0.23753411 -0.21064345 0.39759752 -0.14661809 0.55766094
#> [67] 0.23753411 -0.08259272 0.17350874 -0.24265614 0.58967362 0.10948338
#> [73] 0.42961020 0.30155947 0.33357216 0.49363557 0.62168630 0.81376240
#> [79] 0.33357216 -0.33869418 -0.33869418 -0.40271955 -0.08259272 0.49363557
#> [85] 0.17350874 0.52564825 0.68571167 0.14149606 0.04545801 -0.17863077
#> [91] -0.05058004 0.39759752 -0.08259272 -0.72284638 -0.01856736 0.07747069
#> [97] 0.07747069 0.26954679 -0.69083370 0.01344533 1.35797801 0.52564825
#> [103] 1.35797801 0.87778776 1.16590191 1.74213020 -0.08259272 1.42200337
#> [109] 0.94181313 1.77414288 0.94181313 0.78174972 1.13388923 0.42961020
#> [115] 0.71772435 1.06986386 0.94181313 2.09426971 1.80615557 0.26954679
#> [121] 1.35797801 0.46162289 1.71011752 0.58967362 1.26193996 1.38999069
#> [127] 0.55766094 0.62168630 0.97382581 1.19791459 1.38999069 1.99823166
#> [133] 1.00583850 0.58967362 0.58967362 1.67810484 1.22992728 0.94181313
#> [139] 0.55766094 1.16590191 1.26193996 1.13388923 0.52564825 1.38999069
#> [145] 1.38999069 1.06986386 0.58967362 0.90980045 1.10187654 0.62168630