pca: Plot Principal Component Analysis Results

In hclusteasy: Determining Hierarchical Clustering Easily

Plot Principal Component Analysis Results

Description

Apply PCA (Principal Component Analysis) to the data and construct a scatter plot of the first two principal components.

Usage

pca(data, groups = "none")

Arguments

data	Dataset in data.frame format.
groups	Groups to color observations and draw ellipses around each group of samples with a confidence level of 0.98. Default is "none".

Value

A ggplot.

Examples

# Load the required package
library(hclusteasy)


# Read the 'iris' dataset from the package
data("iris_uci")

# Select column "Species" (groups) in the iris dataset
species <- iris_uci[, 5]

# Remove column "Species" in the iris dataset
iris <- iris_uci[, -5]


# Apply pca and ploting the two firsts components without groups
pca(iris)

# Apply pca and ploting the first two components with groups
pca(iris, groups = species)

hclusteasy documentation built on June 22, 2024, 9:48 a.m.