R语言做主成分分析的一个简单小例子

这个是公众号 小明的数据分析笔记本 2019年11月30号 的推文

数据集总共是178个样本，14个变量，第一个变量是葡萄酒的种类，其余变量是葡萄酒的一些指标

读取数据集

# df<-read.delim("https://gist.githubusercontent.com/tijptjik/9408623/raw/b237fa5848349a14a14e5d4107dc7897c21951f5/wine.csv",
#                sep = ",",
#                header = TRUE)
# readr::write_csv(df,file = "Wine.csv")

df<-read.delim("Wine.csv",
               sep = ",",
               header = TRUE)
dim(df)

## [1] 178  14

library(knitr)

## Warning: package 'knitr' was built under R version 4.0.5

kable(df[1:6,])

Wine	Alcohol	Malic.acid	Ash	Acl	Mg	Phenols	Flavanoids	Nonflavanoid.phenols	Proanth	Color.int	Hue	OD	Proline
1	14.23	1.71	2.43	15.6	127	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065
1	13.20	1.78	2.14	11.2	100	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050
1	13.16	2.36	2.67	18.6	101	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185
1	14.37	1.95	2.50	16.8	113	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480
1	13.24	2.59	2.87	21.0	118	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735
1	14.20	1.76	2.45	15.2	112	3.27	3.39	0.34	1.97	6.75	1.05	2.85	1450

主成分分析

df$Wine<-as.factor(df$Wine)
winepca<-prcomp(df[,2:14],scale. = TRUE)
library(factoextra)

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 4.0.5

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

fviz_eig(winepca,addlabels = T)

fviz_pca_ind(winepca,
             col.ind = df$Wine,
             addEllipses = T,
             geom=("point"),
             legend.title="")

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小明的数据分析笔记本