Classification of Scotch Single Malts

86 malt whiskies are scored between 0-4 for 12 different taste categories including sweetness, smoky, nutty etc. Additionally, coordinates of distilleries allow us to obtain pairwise distance information. Using a combination of these variables it is possible to look for correlations between particular attributes of taste and physical location, for example does a shared local resource have a significant effect on nearby whiskies.
By using correlation analysis it may be possible to provide whisky recommendations based upon an individual's particular preferences. By computing the Pearson correlation coefficient and specifying a threshold value between 0 and 1, we can establish an adjacency matrix where each node is a malt whisky and an edge represents a level of similarity above the threshold.

data("d.whisky")

Format

A data frame with 86 observations on the following 16 variables.

distillery

a character Aberfeldy, Aberlour, AnCnoc, Ardbeg, ...

brand

a grouping factor to separate the better known distilleries (A) from the lesser known ones (B).

region

a factor with levels campbeltown, highland, islands, islay, lowland, speyside.

body

a numeric vector

sweetness

a numeric vector

smoky

a numeric vector

medicinal

a numeric vector

tobacco

a numeric vector

honey

a numeric vector

spicy

a numeric vector

winey

a numeric vector

nutty

a numeric vector

malty

a numeric vector

fruity

a numeric vector

floral

a numeric vector

postcode

a character AB30 1YE, AB35 5TB, ...

latitude

a numeric vector, coordinate pairs of distilleries.

longitude

a numeric vector, coordinate pairs of distilleries.

Source

http://www.mathstat.strath.ac.uk/outreach/nessie/nessie_whisky.html

References

http://www.mathstat.strath.ac.uk/outreach/nessie/index.html

Examples

head(d.whisky)
#>   distillery brand   region body sweetness smoky medicinal tobacco honey spicy
#> 1  Aberfeldy     A highland    2         2     2         0       0     2     1
#> 2   Aberlour     A speyside    3         3     1         0       0     4     3
#> 3     AnCnoc     A highland    1         3     2         0       0     2     0
#> 4     Ardbeg     A    islay    4         1     4         4       0     0     2
#> 5    Ardmore     B highland    2         2     2         0       0     1     1
#> 6      Arran     A  islands    2         3     1         1       0     1     1
#>   winey nutty malty fruity floral postcode latitude longitude
#> 1     2     2     2      2      2 PH15 2EB   286580    749680
#> 2     2     2     3      3      2 AB38 9PJ   326340    842570
#> 3     0     2     2      3      2  AB5 5LI   352960    839320
#> 4     0     1     2      1      0 PA42 7EB   141560    646220
#> 5     1     2     3      1      1 AB54 4NH   355350    829140
#> 6     1     0     1      1      2 KA27 8HJ   194050    649950

opar <- par(mfrow=c(3,3), cex.main=1.8)
for(i in 1:9)
  PlotPolar(d.whisky[i, 4:15], rlim=4, type="l", col=DescTools::hecru, 
            lwd=2, fill=SetAlpha(DescTools::hecru, 0.4),
            panel.first=PolarGrid(
              ntheta=ncol(d.whisky[i, 2:13]), nr = NA, col="grey",
              lty="dotted", las=1, cex=1.4, alabels=StrCap(colnames(d.whisky[i, 3:14])),
              lblradians=TRUE),
            main=d.whisky[i, "distillery"])



par(mfrow=c(3,3), cex.main=1.8, xpd=NA)
id <- d.whisky$distillery %in% c("Ardbeg","Caol Ila","Cragganmore","Lagavulin","Laphroig",
                                   "Macallan","Mortlach","Talisker","Tobermory")
PlotFaces(d.whisky[id, 4:15], nr=3, nc=3, col=hecru, scale=TRUE, fill=TRUE,
          labels=d.whisky$distillery[id])


par(opar)