wine | R Documentation |
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 <- "https://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 [https://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, School of Information and Computer Science.
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