profiling_num.RdGet a metric table with many indicators for all numerical variables, automatically skipping the non-numerical variables. Current metrics are: mean, std_dev: standard deviation, all the p_XX: percentile at XX number, skewness, kurtosis, iqr: inter quartile range, variation_coef: the ratio of sd/mean, range_98 is the limit for which the 98
profiling_num(data)
| data | data frame |
|---|
metrics table
profiling_num(mtcars)#> variable mean std_dev variation_coef p_01 p_05 p_25 #> 1 mpg 20.090625 6.0269481 0.2999881 10.40000 11.9950 15.42500 #> 2 cyl 6.187500 1.7859216 0.2886338 4.00000 4.0000 4.00000 #> 3 disp 230.721875 123.9386938 0.5371779 72.52600 77.3500 120.82500 #> 4 hp 146.687500 68.5628685 0.4674077 55.10000 63.6500 96.50000 #> 5 drat 3.596563 0.5346787 0.1486638 2.76000 2.8535 3.08000 #> 6 wt 3.217250 0.9784574 0.3041285 1.54462 1.7360 2.58125 #> 7 qsec 17.848750 1.7869432 0.1001159 14.53100 15.0455 16.89250 #> 8 vs 0.437500 0.5040161 1.1520369 0.00000 0.0000 0.00000 #> 9 am 0.406250 0.4989909 1.2282853 0.00000 0.0000 0.00000 #> 10 gear 3.687500 0.7378041 0.2000825 3.00000 3.0000 3.00000 #> 11 carb 2.812500 1.6152000 0.5742933 1.00000 1.0000 2.00000 #> p_50 p_75 p_95 p_99 skewness kurtosis iqr #> 1 19.200 22.80 31.30000 33.43500 0.6404399 2.799467 7.37500 #> 2 6.000 8.00 8.00000 8.00000 -0.1831287 1.319032 4.00000 #> 3 196.300 326.00 449.00000 468.28000 0.4002724 1.910317 205.17500 #> 4 123.000 180.00 253.55000 312.99000 0.7614356 3.052233 83.50000 #> 5 3.695 3.92 4.31450 4.77500 0.2788734 2.435116 0.84000 #> 6 3.325 3.61 5.29275 5.39951 0.4437855 3.172471 1.02875 #> 7 17.710 18.90 20.10450 22.06920 0.3870456 3.553753 2.00750 #> 8 0.000 1.00 1.00000 1.00000 0.2519763 1.063492 1.00000 #> 9 0.000 1.00 1.00000 1.00000 0.3817709 1.145749 1.00000 #> 10 4.000 4.00 5.00000 5.00000 0.5546495 2.056790 1.00000 #> 11 2.000 4.00 4.90000 7.38000 1.1021304 4.536121 2.00000 #> range_98 range_80 #> 1 [10.4, 33.435] [14.34, 30.09] #> 2 [4, 8] [4, 8] #> 3 [72.526, 468.28] [80.61, 396] #> 4 [55.1, 312.99] [66, 243.5] #> 5 [2.76, 4.775] [3.007, 4.209] #> 6 [1.54462, 5.39951] [1.9555, 4.0475] #> 7 [14.531, 22.0692] [15.534, 19.99] #> 8 [0, 1] [0, 1] #> 9 [0, 1] [0, 1] #> 10 [3, 5] [3, 5] #> 11 [1, 7.38] [1, 4]