wine: The wine dataset from the UCI Machine Learning Repository.

In nspyrison/spinifex: Manual Tours, Manual Control of Dynamic Projections of Numeric Multivariate Data

The wine dataset from the UCI Machine Learning Repository.

Description

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.

Usage

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

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")

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

Examples

library(spinifex)
str(wine)
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))

animate_plotly(ggt)

nspyrison/spinifex documentation built on Feb. 7, 2024, 1:10 p.m.