249  mtcars-easystats

Schlüsselwörter

Aufgaben, Statistik, Prognose, Modellierung, R, Datenanalyse, Regression

249.1 Exercise

Provide an overview of descriptive statistics on the mtcars dataset using the R package easystats.

249.2 Solution

Starting R packages:

library(easystats) 
library(tidyverse)  # comfort

Providing the dataset mtcars:

data(mtcars)

Here are some descriptive statistics for the metric variables of the dataset:

describe_distribution(mtcars) 

Variable	Mean	SD	IQR	Range	Skewness	Kurtosis	n	n_Missing
mpg	20.09	6.03	7.53	(10.40, 33.90)	0.67	-0.02	32	0
cyl	6.19	1.79	4.00	(4.00, 8.00)	-0.19	-1.76	32	0
disp	230.72	123.94	221.53	(71.10, 472.00)	0.42	-1.07	32	0
hp	146.69	68.56	84.50	(52.00, 335.00)	0.80	0.28	32	0
drat	3.60	0.53	0.84	(2.76, 4.93)	0.29	-0.45	32	0
wt	3.22	0.98	1.19	(1.51, 5.42)	0.47	0.42	32	0
qsec	17.85	1.79	2.02	(14.50, 22.90)	0.41	0.86	32	0
vs	0.44	0.50	1.00	(0.00, 1.00)	0.26	-2.06	32	0
am	0.41	0.50	1.00	(0.00, 1.00)	0.40	-1.97	32	0
gear	3.69	0.74	1.00	(3.00, 5.00)	0.58	-0.90	32	0
carb	2.81	1.62	2.00	(1.00, 8.00)	1.16	2.02	32	0

What about statistics for the non-metric (nominal) variables (columns)? Let’s restrict the analysis to the columns vs, am, and gear.

data_tabulate(mtcars, select = c("vs", "am", "gear"))

Frequency Table

Variable	Value	N	Raw %	Valid %	Cumulative %
vs	0	18	56.25	56.25	56.25
	1	14	43.75	43.75	100.00
	(NA)	0	0.00	(NA)	(NA)
am	0	19	59.38	59.38	59.38
	1	13	40.62	40.62	100.00
	(NA)	0	0.00	(NA)	(NA)
gear	3	15	46.88	46.88	46.88
	4	12	37.50	37.50	84.38
	5	5	15.62	15.62	100.00
	(NA)	0	0.00	(NA)	(NA)