Visualization of the Iris Dataset

This reproducible slideshow is part of a peer graded assignment in the Developing Data Products JHU coursera class.

This dataset was introduced by Ronald Fisher in 1936.The data set consists of 50 samples from each of three species of the Iris flower:

• Setosa
• Versicolor
• Virginica.

Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres.

Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other. Our aim is to recreate that model.

The shiny app I produced allows the user to :

• First perform a basic exploratory analysis with graps.
• Then predict their own values using two fitted models, an LDA and a simple decision tree.

The app can be found here

The github repo is here

An LDA was fit Ronald Fisher did and also a simple decision tree, with plots that enable users to visualize the datasets.

The plots were generated as follows:

# LDA model
ldamodel <- lda(formula = Species ~ ., data=iris, prior = c(1,1,1)/3, cv=TRUE)
# Extracting LDA linear components
predlda <- predict(ldamodel, iris)
proplda <- ldamodel$svd^2/sum(ldamodel$svd^2)
data <- data.frame(Species = iris$Species, lda = predlda$x)
# Plotting the data onto the components.
g <- ggplot(data) + geom_point(aes(lda.LD1, lda.LD2, colour=Species, shape = Species), size=2.5, alpha=0.7) + labs(x='Linear Discriminant 1 (99.12%)', y = 'Linear Discriminant 2 (0.08%)') + theme_tufte(base_size = 14)
g

# The code for the decision tree was far simpler.
model <- train(x=iris[,-5], y=iris[,5], method='rpart')
rpart.plot(x = model$finalModel, extra=4, tweak = 1.2, box.palette = list('Reds', 'Blues', 'Greens'))