mtcars {datasets} | R Documentation |
Motor Trend Car Road Tests
Description
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
mtcars
Format
A data frame with 32 observations on 11 (numeric) variables.
[, 1] | mpg | Miles/(US) gallon |
[, 2] | cyl | Number of cylinders |
[, 3] | disp | Displacement (cu.in.) |
[, 4] | hp | Gross horsepower |
[, 5] | drat | Rear axle ratio |
[, 6] | wt | Weight (1000 lbs) |
[, 7] | qsec | 1/4 mile time |
[, 8] | vs | Engine (0 = V-shaped, 1 = straight) |
[, 9] | am | Transmission (0 = automatic, 1 = manual) |
[,10] | gear | Number of forward gears |
[,11] | carb | Number of carburetors |
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.’
Source
Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.
Examples
require(graphics)
pairs(mtcars, main = "mtcars data", gap = 1/4)
coplot(mpg ~ disp | as.factor(cyl), data = mtcars,
panel = panel.smooth, rows = 1)
## possibly more meaningful, e.g., for summary() or bivariate plots:
mtcars2 <- within(mtcars, {
vs <- factor(vs, labels = c("V", "S"))
am <- factor(am, labels = c("automatic", "manual"))
cyl <- ordered(cyl)
gear <- ordered(gear)
carb <- ordered(carb)
})
summary(mtcars2)
[Package datasets version 4.4.1 Index]