| mtcars {datasets} | R Documentation |
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).
mtcars
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 |
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.�
Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391�411.
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)