The wine dataset from the UCI Machine Learning Repository.

Source: R/9_data.r

The wine dataset contains the results of a chemical analysis of wines grown in a specific area of Italy. Three types of wine are represented in the 178 samples, with the results of 13 chemical analyses recorded for each sample. The Type variable has been transformed into a categorical variable.

wine

Format

A data frame of 178 observations of target class Type and 12 numeric variables:

  • Type, The type of wine, the target factor, 1 (59 obs), 2(71 obs), and 3 (48 obs).

  • Alcohol, Alcohol

  • Malic, Malic acid

  • Ash, Ash

  • Alcalinity, Alcalinity of ash

  • Magnesium, Magnesium

  • Phenols, Total phenols

  • Flavanoids, Flavanoids

  • Nonflavanoids, Nonflavanoid phenols

  • Proanthocyanins, Proanthocyanins

  • Color, Color intensity

  • Hue, Hue

  • Dilution, D280/OD315 of diluted wines

  • Proline, Proline

Source

rattle, R package. G. Williams, 2020. rattle: Graphical User Interface for Data Science in R https://CRAN.R-project.org/package=rattle

PARVUS. M. Forina. et al. 1988. Elsevier, Amsterdam, PARVUS: An extendable package of programs for data exploration, classification and correlation. ISBN 0-44-430121z

Details

The data contains no missing values and consist of only numeric data, with a three class target variable (Type) for classification.

Replicating this dataset:

require("rattle")
str(rattle::wine)
## save(wine, file = "./data/wine.rda")

Examples

library(spinifex)
str(wine)
#> 'data.frame':	178 obs. of  14 variables:
#>  $ Type           : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Alcohol        : num  14.2 13.2 13.2 14.4 13.2 ...
#>  $ Malic          : num  1.71 1.78 2.36 1.95 2.59 1.76 1.87 2.15 1.64 1.35 ...
#>  $ Ash            : num  2.43 2.14 2.67 2.5 2.87 2.45 2.45 2.61 2.17 2.27 ...
#>  $ Alcalinity     : num  15.6 11.2 18.6 16.8 21 15.2 14.6 17.6 14 16 ...
#>  $ Magnesium      : int  127 100 101 113 118 112 96 121 97 98 ...
#>  $ Phenols        : num  2.8 2.65 2.8 3.85 2.8 3.27 2.5 2.6 2.8 2.98 ...
#>  $ Flavanoids     : num  3.06 2.76 3.24 3.49 2.69 3.39 2.52 2.51 2.98 3.15 ...
#>  $ Nonflavanoids  : num  0.28 0.26 0.3 0.24 0.39 0.34 0.3 0.31 0.29 0.22 ...
#>  $ Proanthocyanins: num  2.29 1.28 2.81 2.18 1.82 1.97 1.98 1.25 1.98 1.85 ...
#>  $ Color          : num  5.64 4.38 5.68 7.8 4.32 6.75 5.25 5.05 5.2 7.22 ...
#>  $ Hue            : num  1.04 1.05 1.03 0.86 1.04 1.05 1.02 1.06 1.08 1.01 ...
#>  $ Dilution       : num  3.92 3.4 3.17 3.45 2.93 2.85 3.58 3.58 2.85 3.55 ...
#>  $ Proline        : int  1065 1050 1185 1480 735 1450 1290 1295 1045 1045 ...
dat  <- scale_sd(wine[, 2:6])
clas <- wine$Type

bas <- basis_pca(dat)
mv  <- manip_var_of(bas)
mt  <- manual_tour(bas, mv)

ggt <- ggtour(mt, dat, angle = .2) +
  proto_default(aes_args = list(color = clas, shape = clas))
# \donttest{
animate_plotly(ggt)


# }