added lab 4

Lab 4/given/Data_Analytics2025Fall_Lab4_code.R

@@ -0,0 +1,41 @@
+##########################################
+### Principal Component Analysis (PCA) ###
+##########################################
+
+## load libraries
+library(ggplot2)
+library(ggfortify)
+library(GGally)
+library(e1071)
+library(class)
+library(psych)
+library(readr)
+
+## set working directory so that files can be referenced without the full path
+setwd("/home/ion606/Desktop/Data Analytics/Lab 4")
+
+## read dataset
+wine <- read_csv("wine.data", col_names = FALSE)
+
+## set column names
+names(wine) <- c("Type","Alcohol","Malic acid","Ash","Alcalinity of ash","Magnesium","Total phenols","Flavanoids","Nonflavanoid Phenols","Proanthocyanins","Color Intensity","Hue","Od280/od315 of diluted wines","Proline")
+
+## inspect data frame
+head(wine)
+
+## change the data type of the "Type" column from character to factor
+####
+# Factors look like regular strings (characters) but with factors R knows 
+# that the column is a categorical variable with finite possible values
+# e.g. "Type" in the Wine dataset can only be 1, 2, or 3
+####
+
+wine$Type <- as.factor(wine$Type)
+
+
+## visualize variables
+pairs.panels(wine[,-1],gap = 0,bg = c("red", "yellow", "blue")[wine$Type],pch=21)
+
+ggpairs(wine, ggplot2::aes(colour = Type))
+
+###