wine: The wine dataset from the UCI Machine Learning Repository.
In grayskripko/rattle: Graphical User Interface for Data Mining in R

Description Usage Format Source References

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 categoric variable.

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

wine

A data frame containing 178 observations of 13 variables.

Type: The type of wine, into one of three classes, 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

The data was downloaded from the UCI Machine Learning Repository.

It was read as a CSV file with no header using read.csv. The columns were then given the appropriate names using colnames and the Type was transformed into a factor using as.factor. The compressed R data file was saved using save:

  UCI <- "http://archive.ics.uci.edu/ml"
  REPOS <- "machine-learning-databases"
  wine.url <- sprintf("
  wine <- read.csv(wine.url, header=FALSE) 
  colnames(wine) <- c('Type', 'Alcohol', 'Malic', 'Ash', 
                      'Alcalinity', 'Magnesium', 'Phenols', 
                      'Flavanoids', 'Nonflavanoids',
                      'Proanthocyanins', 'Color', 'Hue', 
                      'Dilution', 'Proline')
  wine$Type <- as.factor(wine$Type)
  save(wine, file="wine.Rdata", compress=TRUE)

Asuncion, A. & Newman, D.J. (2007). UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, School of Information and Computer Science.

grayskripko/rattle documentation built on May 17, 2019, 8:34 a.m.