ScotchWhiskey: Scotch Whiskey Data Set

In adespatial: Multivariate Multiscale Spatial Analysis

Scotch Whiskey Data Set

Description

Single Malt Scotch whiskeys from 109 distilleries

Usage

data(ScotchWhiskey)

Format

A list with 12 members:

geo

A SpatialPointsDataFrame-class object containing the geographic coordinates and other information about the distilleries.

colour

The whiskey colour coded as a 14-level factor.

nose

A set of 12 nasal notes (boolean).

body

A set of 8 body notes (boolean).

palate

A set of 15 palatine notes (boolean).

finish

A set of 19 finish (or after-taste) notes (boolean).

nbChar

Number of characteristics attributed to each distillery for each of the four sets of boolean features: nose, body, palate, finish.

listW

A listw object (see nb2listw) containing information about the spatial edges (neighbour links) between the distilleries.

links.mat

A binary square matrix of the spatial connexions between the distilleries (contiguity matrix).

neighbors

A SpatialLinesDataFrame-class object containing geographic information about the spatial links between the distilleries.

dist

A list of distance matrices obtained for each of the four sets of boolean features.

Details

There are 5 data sets: color, nose, body, palate, and finish. The binary (0,1) descriptors are in the same order as on p. 239 of the whisky paper.

There are two whiskies in the classification from the Springbank distillery. One pertains to the Islay group, the other to the Western group.

Please let us know of the analyses you have performed with the whiskey data, especially if you intend to publish them.

The distance matrices were calculated separately as follows for each tasting data set:

D = (1 - S4)^0.5,

where S4 is the Simple matching coefficient of Sokal & Michener (1958). This coefficient was called S4 in the Gower & Legendre (1986) paper and S1 in the Legendre & Legendre (2012) book. In package ade4, coefficient D = sqrt(1 - S4) is computed by function dist.binary using argument "method=2".

Source

Pierre Legendre <[e-mail]> and François-Joseph Lapointe <[e-mail]>, Département de sciences biologiques, Université de Montréal, Montréal, Québec, Canada.

References

Lapointe, F.-J. and P. Legendre. 1994. A classification of pure malt Scotch whiskies. Applied Statistics 43: 237-257 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/2986124")}

Gower, J.C. and Legendre, P. 1986. Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification, 3, 5-48. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF01896809")}

Legendre, P. and Legendre, L. 2012. Numerical Ecology. 3rd English edition. Elsevier Science BV, Amsterdam. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0304-3800(00)00291-X")}

Examples

data(ScotchWhiskey)
lapply(ScotchWhiskey,ncol)
ScotchWhiskey$nbChar
ScotchWhiskey$listW  ## attr(ScotchWhiskey$listW,"class")
names(ScotchWhiskey)
names(ScotchWhiskey$dist)

plotWhiskey <- function(main) {
    plot(x=ScotchWhiskey$geo@coords[,1L]/1000,
         xlab="Eastings (km)",
         y=ScotchWhiskey$geo@coords[,2L]/1000,
         ylab="Northings (km)",
         main=main,
         type="n",asp=1)
    apply(
        ScotchWhiskey$neighbor@data,1L,
        function(X,coords) {
            segments(
                coords[X[1L],1L]/1000,
                coords[X[1L],2L]/1000,
                coords[X[2L],1L]/1000,
                coords[X[2L],2L]/1000
            )
        },
        coords=ScotchWhiskey$geo@coords
    )
    invisible(NULL)
}

plotWhiskey("Scotch whiskey: peat nose")
cols <- c("blue","orange")
points(ScotchWhiskey$geo@coords/1000,pch=21L,
       bg=cols[ScotchWhiskey$nose[,"peat"]+1L])
legend(x=50,y=1000,legend=c("Has a peat nose","Has no peat nose"),
       pch=21L,pt.bg=rev(cols))

plotWhiskey("Scotch whiskey: soft body")
cols <- c("red","green")
points(ScotchWhiskey$geo@coords/1000,pch=21L,
       bg=cols[ScotchWhiskey$body[,"soft"]+1L])
legend(x=50,y=1000,legend=c("Has a soft body","Has no soft body"),
       pch=21L,pt.bg=rev(cols))

plotWhiskey("Scotch whiskey: spicy palate")
cols <- c("red","green")
points(ScotchWhiskey$geo@coords/1000,pch=21L,
       bg=cols[ScotchWhiskey$palate[,"spice"]+1L])
legend(x=50,y=1000,legend=c("Has a spicy palate","Has no spicy palate"),
       pch=21L,pt.bg=rev(cols))

plotWhiskey("Scotch whiskey: sweet finish")
cols <- c("red","green")
points(ScotchWhiskey$geo@coords/1000,pch=21L,
       bg=cols[ScotchWhiskey$finish[,"sweet"]+1L])
legend(x=50,y=1000,legend=c("Has a sweet finish","Has no sweet finish"),
       pch=21L,pt.bg=rev(cols))

## To visualize (part of) the distance matrices:
as.matrix(ScotchWhiskey$dist$nose)[1:5,1:5]
as.matrix(ScotchWhiskey$dist$body)[1:5,1:5]
as.matrix(ScotchWhiskey$dist$palate)[1:5,1:5]
as.matrix(ScotchWhiskey$dist$finish)[1:5,1:5]

## The data tables:
ScotchWhiskey$colour
head(ScotchWhiskey$nose)
head(ScotchWhiskey$body)
head(ScotchWhiskey$palate)
head(ScotchWhiskey$finish)

adespatial documentation built on Sept. 11, 2024, 7:04 p.m.