ION606
42 lines
1.2 KiB
R
42 lines
1.2 KiB
R
##########################################
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### Principal Component Analysis (PCA) ###
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##########################################
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## load libraries
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library(ggplot2)
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library(ggfortify)
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library(GGally)
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library(e1071)
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library(class)
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library(psych)
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library(readr)
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## set working directory so that files can be referenced without the full path
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setwd("~/Courses/Data Analytics/Fall25/labs/lab 4/")
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## read dataset
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wine <- read_csv("wine.data", col_names = FALSE)
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## set column names
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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")
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## inspect data frame
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head(wine)
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## change the data type of the "Type" column from character to factor
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####
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# Factors look like regular strings (characters) but with factors R knows
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# that the column is a categorical variable with finite possible values
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# e.g. "Type" in the Wine dataset can only be 1, 2, or 3
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####
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wine$Type <- as.factor(wine$Type)
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## visualize variables
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pairs.panels(wine[,-1],gap = 0,bg = c("red", "yellow", "blue")[wine$Type],pch=21)
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ggpairs(wine, ggplot2::aes(colour = Type))
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###
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