##(as 'lib'is unspecified) library(pastecs) stat.desc(myvars) t ##nbr.val 32.0000000 32.000000032.000000032.00000000 ##nbr.null 0.0000000 0.0000000 0.000000019.00000000 ##nbr.na 0.0000000 0.0000000 0.00000000.00000000 #min 10.4000000 52.0000000 1.51300000.00000000 料nax 33.9000000 335.0000000 5.4240000 1.00000000 ##range 23.5000000283.0000000 3.91100001.00000000 ##sum 642.90000004694.0000000102.952000013.00000000 #median 19.2000000 123.0000000 3.32500000.00000000 #mean 20.0906250 146.6875000 3.21725000.40625000 ##SE.mean 1.0654240 12.1203173 0.17296850.08820997 #CI.mean.0.95 2.1729465 24.7195501 0.35277150.17990541 ##var 36.32410284700.866935 0.9573790 0.24899194 ##std.dev 6.0269481 68.5628685 0.97845740.49899092 ##coef.var 0.2999881 0.4674077 0.3041285 1.22828533 stat.desc(myvars,basic-T) mpg hp t am ##nbr.val 32.0000000 32.000000032.000000032.00000000 #nbr.null 0.0000000 0.0000000 0.000000019.00000000 #nbr.na 0.0000000 0.0000000 0.00000000.00000000 ##min 10.4000000 52.0000000 1.51300000.00000000 ##max 33.9000000335.0000000 5.42400001.00000000 range 23.5000000283.0000000 3.91100001.00000000 ##sum 642.90000004694.0000000102.952000013.00000000 #料median 19.2000000123.0000000 3.32500000.00000000 ##mean 20.0906250 146.6875000 3.21725000.40625000 ##SE.mean 1.0654240 12.1203173 0.17296850.08820997 ##CI.mean.0.95 2.1729465 24.7195501 0.35277150.17990541 ##var 36,32410284700.8669355 0.95737900.24899194 ##std.dev 6.0269481 68.5628685 0.97845740.49899092 ##coef.var 0.29998810.4674077 0.30412851.22828533 basic=T计算一些基础值，例如缺失值的数量等等 stat.desc(myvars,desc=T)## (as 'lib' is unspecified) library(pastecs) stat.desc(myvars) ## mpg hp wt am ## nbr.val 32.0000000 32.0000000 32.0000000 32.00000000 ## nbr.null 0.0000000 0.0000000 0.0000000 19.00000000 ## nbr.na 0.0000000 0.0000000 0.0000000 0.00000000 ## min 10.4000000 52.0000000 1.5130000 0.00000000 ## max 33.9000000 335.0000000 5.4240000 1.00000000 ## range 23.5000000 283.0000000 3.9110000 1.00000000 ## sum 642.9000000 4694.0000000 102.9520000 13.00000000 ## median 19.2000000 123.0000000 3.3250000 0.00000000 ## mean 20.0906250 146.6875000 3.2172500 0.40625000 ## SE.mean 1.0654240 12.1203173 0.1729685 0.08820997 ## CI.mean.0.95 2.1729465 24.7195501 0.3527715 0.17990541 ## var 36.3241028 4700.8669355 0.9573790 0.24899194 ## std.dev 6.0269481 68.5628685 0.9784574 0.49899092 ## coef.var 0.2999881 0.4674077 0.3041285 1.22828533 stat.desc(myvars,basic=T) ## mpg hp wt am ## nbr.val 32.0000000 32.0000000 32.0000000 32.00000000 ## nbr.null 0.0000000 0.0000000 0.0000000 19.00000000 ## nbr.na 0.0000000 0.0000000 0.0000000 0.00000000 ## min 10.4000000 52.0000000 1.5130000 0.00000000 ## max 33.9000000 335.0000000 5.4240000 1.00000000 ## range 23.5000000 283.0000000 3.9110000 1.00000000 ## sum 642.9000000 4694.0000000 102.9520000 13.00000000 ## median 19.2000000 123.0000000 3.3250000 0.00000000 ## mean 20.0906250 146.6875000 3.2172500 0.40625000 ## SE.mean 1.0654240 12.1203173 0.1729685 0.08820997 ## CI.mean.0.95 2.1729465 24.7195501 0.3527715 0.17990541 ## var 36.3241028 4700.8669355 0.9573790 0.24899194 ## std.dev 6.0269481 68.5628685 0.9784574 0.49899092 ## coef.var 0.2999881 0.4674077 0.3041285 1.22828533 #basic=T 计算一些基础值，例如缺失值的数量等等 stat.desc(myvars,desc=T) 3