# NOT RUN {
# Import data.
fname <- system.file("extdata", "finndiane.txt", package = "Numero")
dataset <- read.delim(file = fname)
# Detect binary columns.
dataset <- nroPreprocess(dataset, method = "")
# Prepare training data.
trvars <- c("CHOL", "HDL2C", "TG", "CREAT", "uALB")
trdata <- scale.default(dataset[,trvars])
# K-means clustering.
km <- nroKmeans(data = trdata)
# Self-organizing map.
sm <- nroKohonen(seeds = km)
sm <- nroTrain(som = sm, data = trdata)
# Assign data points into districts.
matches <- nroMatch(centroids = sm, data = trdata)
# Select a subset of variables and detect binary data.
vars <- c("AGE", "MALE", "uALB", "CHOL", "DIAB_KIDNEY", "DECEASED")
selected <- nroPreprocess(dataset[,vars], method = "")
# Calculate district averages for seleted variables.
vars <- c("AGE", "MALE", "uALB", "CHOL", "DIAB_KIDNEY", "DECEASED")
planes <- nroAggregate(topology = sm, districts = matches, data = selected)
# Estimate statistics.
stats <- nroPermute(som = sm, districts = matches, data = selected)
# Set visuals.
colrs <- nroColorize(values = planes, amplitudes = stats)
labls <- nroLabel(topology = sm, values = planes)
# Plot colorings on screen.
nroPlot(elements = sm, colors = colrs, labels = labls)
# }
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