wines: Chemical composition of wines

In Flury: Data Sets from Flury, 1997

Description

These data have been collected on the chemical composition of Weisser Riesling wines from three countries; South Africa,Germany and Italy

Usage

data(wines)

Format

'wines' is a data frame with 26 observations, one factor denoting the country of origin and 15 quantitative variables denoting 15 free monoterpenes and C[13]-norisoprenoids. It is thought these influence the wine's aroma.

Country

a factor with levels South Africa Germany Italy

Y1

a numeric vector

Y2

a numeric vector

Y3

a numeric vector

Y4

a numeric vector

Y5

a numeric vector

Y6

a numeric vector

Y7

a numeric vector

Y8

a numeric vector

Y9

a numeric vector

Y10

a numeric vector

Y11

a numeric vector

Y12

a numeric vector

Y13

a numeric vector

Y14

a numeric vector

Y15

a numeric vector

Details

There are a total of nine South African wines, seven German wines (all from Pfalz) and ten from Northern Italy (from both Trentino Alto Adige as Friuli)

Source

Marais, J., G. Versini, C.J. van Wyj and A. Rapp (1992) “Effect of region on free and bound monoterpene and C[13]-norisoprenoid concentration in Weisser Riesling wines” South African Journal of Enology and Viniculture 13:71-77

References

Flury, B.D. (1997) A First Course in Multivariate Statistics, Springer NY

Examples

data(wines)
## Not run: pairs(wines[,-1],
  lower.panel = function(x, y){ points(x, y,
  pch = unclass(wines[,1]),
  col = as.numeric(wines[,1]))},
  main = "Pairwise scatter plots for Marais wine data")
## rather congested scatter plots!
## End(Not run)

Flury documentation built on May 1, 2019, 6:50 p.m.