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 0.2064431 -0.2648846 -0.195638565 -0.1345382 -0.2961773 0.26512010
#> sd 1.0873796 1.0673600 1.289761631 0.9107464 0.9635989 0.96499959
#> min -3.2445918 -3.9307422 -3.698190812 -2.1867473 -3.3986842 -2.71655257
#> max 2.2964653 2.5668284 2.749312095 2.6537489 1.9231585 2.25717744
#> skewness -0.2151013 -0.5020920 0.005369934 0.4050474 -0.3556456 -0.28807484
#> kurtosi -0.1239653 0.8122760 -0.273521306 0.3440463 0.1402111 -0.02211097
#> q25 -0.5077184 -0.8969083 -1.069782053 -0.6592801 -0.9289476 -0.42111608
#> q50 0.2535726 -0.2103734 -0.167497948 -0.2222320 -0.1813497 0.26647469
#> q75 0.9224631 0.4811286 0.568324956 0.5508523 0.3654583 0.96505774
#> x7 x8 x9 x10 group
#> mean -0.1763534 -0.35850508 0.3109732 0.23773994 1.5000000
#> sd 0.9754852 1.01972269 1.0727607 0.99254707 0.5025189
#> min -2.8047904 -3.27910511 -1.9169994 -2.41555453 1.0000000
#> max 1.8736472 2.22911563 3.2989544 2.76796033 2.0000000
#> skewness -0.2179225 -0.10579246 0.3463374 0.04282651 0.0000000
#> kurtosi -0.3564780 -0.02269649 -0.1528765 0.44887898 -2.0199000
#> q25 -0.8780804 -0.97209425 -0.5007841 -0.27164295 1.0000000
#> q50 -0.1174649 -0.37675884 0.3041808 0.20579093 1.5000000
#> q75 0.5634481 0.38023638 0.9843386 0.79030246 2.0000000