Compute all relevant summary statistics given a dataframes of all numerical columns.
Examples
X <- sim_clust(2, 100, dmin = 0.5, rmin = 0.3, nind = 10)
get_summary_stats(X)
#> x1 x2 x3 x4 x5 x6
#> mean 0.2386105 -0.3742016 -0.2786282 -0.2829597 -0.3192138 0.3357628
#> sd 0.9148479 1.0437201 0.9769165 1.0490810 1.0249724 1.1390903
#> min -1.6467491 -2.5590055 -2.7915919 -2.9942053 -3.0254740 -1.9128498
#> max 2.5393693 2.8657338 1.6823990 2.1079998 1.9603184 2.8475308
#> skewness 0.2623499 0.4737498 -0.1749102 -0.1647284 -0.3626759 0.2231190
#> kurtosi -0.2795580 0.2327558 -0.5061163 -0.5217937 -0.1456928 -0.7889060
#> q25 -0.4287504 -1.1060165 -0.9479723 -1.0793802 -0.9199909 -0.3874004
#> q50 0.2518483 -0.4517733 -0.2393360 -0.2964951 -0.1800402 0.1990394
#> q75 0.7841179 0.1817248 0.3932007 0.4627224 0.3576622 1.0866015
#> x7 x8 x9 x10 group
#> mean -0.3897756 -0.21647604 0.34199206 0.2775550 1.5000000
#> sd 0.9347328 1.09017236 1.01573659 0.9953181 0.5025189
#> min -2.8479440 -2.39708648 -1.66211737 -2.4145533 1.0000000
#> max 1.1688020 2.58756698 2.71284862 2.5725838 2.0000000
#> skewness -0.3830376 0.09666975 0.03942061 -0.1362803 0.0000000
#> kurtosi -0.7094524 -0.71214711 -0.74580182 -0.2479250 -2.0199000
#> q25 -1.0116941 -1.07772602 -0.51274295 -0.4210566 1.0000000
#> q50 -0.3335516 -0.20852154 0.41437602 0.2944162 1.5000000
#> q75 0.3547800 0.55990651 1.08520266 1.0704200 2.0000000