var_cars: Multi-variable transformation of the mtcars dataset.

In ale: Interpretable Machine Learning and Statistical Inference with Accumulated Local Effects (ALE)

Multi-variable transformation of the mtcars dataset.

Description

This is a transformation of the mtcars dataset from R to produce a small dataset with each of the fundamental datatypes: logical, factor, ordered, integer, and double. Most of the transformations are obvious, but two are noteworthy:

• For the unordered factor, the country of the car manufacturer is obtained based on the row names of mtcars. This var_cars version does not have row names.

• For the ordered factor, gears 3, 4, and 5 are encoded as 'three', 'four', and 'five', respectively. The text labels make it explicit that the variable is ordinal, yet the number names make the order crystal clear.

Here is the original description of the mtcars dataset:

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

Usage

var_cars

Format

A tibble with 32 observations on 12 variables.

mpg

double: Miles/(US) gallon

cyl

integer: Number of cylinders

disp

double: Displacement (cu.in.)

hp

double: Gross horsepower

drat

double: Rear axle ratio

wt

double: Weight (1000 lbs)

qsec

double: 1/4 mile time

vs

logical: Engine (0 = V-shaped, 1 = straight)

am

logical: Transmission (0 = automatic, 1 = manual)

gear

ordered: Number of forward gears

carb

integer: Number of carburetors

country

factor: Country of car manufacturer

Note

Henderson and Velleman (1981) comment in a footnote to Table 1: 'Hocking (original transcriber)'s noncrucial coding of the Mazda's rotary engine as a straight six-cylinder engine and the Porsche's flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.'

References

Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.

ale documentation built on May 29, 2024, 10:33 a.m.