本文摘自：R 语言主成分分析(PCA)实战教程
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安装依赖:

install.packages("FactoMineR")
install.packages("factoextra")
library("FactoMineR")
library("factoextra")

数据准备:

# 来自factoextra包的decathlon2演示数据集，数据集如下：
data(decathlon2)
head(decathlon2)

# pca前，先进行标准化：标准偏差1，平均值为零
# FactoMineR 中，PCA之前会自动标准化数据
decathlon2.active <- decathlon2[1:23, 1:10]
decathlon2.active[, 1:6]

res.pca <- PCA(decathlon2.active, graph = FALSE)
PCA(decathlon2.active)  # 显示图

一、变量分析

var <- get_pca_var(res.pca)

1. 相关曲线作图

var$coord

fviz_pca_var(res.pca, col.var = "black")

2. 代表质量作图

var$cos2

corrplot展示各变量对各主成分的代表质量

library("corrplot")
# is.corr表示输入的矩阵不是相关系数矩阵
corrplot(var$cos2, is.corr=FALSE)

各变量对一二主成分的代表质量柱形图（通过值的叠加显示）

fviz_cos2(res.pca, choice = "var", axes = 1:2)

各变量相关图，颜色代表代表质量

fviz_pca_var(res.pca, col.var = "cos2",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE # Avoid text overlapping
             )

3. 变量对主成分的贡献作图

var$contrib

corrplot展示每个变量对每个主成分的贡献

library("corrplot")
corrplot(var$contrib, is.corr=FALSE)

各变量对第一主成分的贡献

fviz_contrib(res.pca, choice = "var", axes = 1, top = 10)

各变量对第二主成分的贡献

fviz_contrib(res.pca, choice = "var", axes = 2, top = 10)

各变量对第一二主成分的总贡献

fviz_contrib(res.pca, choice = "var", axes = 1:2, top = 10)

各变量相关图，颜色展示贡献度

fviz_pca_var(res.pca, col.var = "contrib",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07")
             )

二、观测值分析

factoextra包中的get_pca_ind()提取个体坐标，相关性，cos2 和贡献率

ind <- get_pca_ind(res.pca)
ind

1. 观测值坐标图

fviz_pca_ind(res.pca)

2. 观测值坐标图，cos2着色

ind$cos2

fviz_pca_ind(res.pca, col.ind = "cos2",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE # Avoid text overlapping
             )

3. 观测值坐标图，cos2着色，cos2大小

fviz_pca_ind(res.pca, col.ind = "cos2", pointsize = "cos2",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE # Avoid text overlapping
             )

4. 观测值柱形图，cos2代表质量

fviz_cos2(res.pca, choice = "ind")

5. 观测值柱形图，contrib贡献

fviz_contrib(res.pca, choice = "ind", axes = 1:2)

三、自定义观测值作图

1. 数据准备

head(iris)

iris.pca <- PCA(iris[,-5], graph = FALSE)
PCA(iris[,-5])

2. PCA展示，添加椭圆，自定义颜色

fviz_pca_ind(iris.pca,
             # 只显示点而不显示文本，默认都显示
             geom.ind = "point",
             # 设定分类种类
             col.ind = iris$Species,
             # 设定颜色
             palette = c("#00AFBB", "#E7B800", "#FC4E07"),
             # 添加椭圆 Concentration ellipses
             addEllipses = TRUE,
             legend.title = "Groups",
             )

3. PCA展示，添加椭圆，分组颜色

fviz_pca_ind(iris.pca,
             label = "none", # hide individual labels
             habillage = iris$Species, # color by groups
             addEllipses = TRUE, # Concentration ellipses
             palette = "jco" #  jco（临床肿瘤学杂志）调色板
             )

4. PCA展示，添加多边形，分组颜色

fviz_pca_ind(iris.pca, geom.ind = "point",
             col.ind = iris$Species, # color by groups
             palette = c("#00AFBB", "#E7B800", "#FC4E07"),
             # 用凸包多边形代替椭圆
             addEllipses = TRUE, ellipse.type = "convex",
             legend.title = "Groups"
             )

四、观测量和变量的biplot（双标图）

biplot 展示了两方面内容：根据前两个主成分，每个观测的得分；根据前两个主成分，每个变量的载荷。
1. PCA biplot

fviz_pca_biplot(res.pca, repel = TRUE,
                col.var = "#2E9FDF", # Variables color
                col.ind = "#696969"  # Individuals color
                )

2. PCA biplot，添加椭圆

fviz_pca_biplot(iris.pca, repel = TRUE,
                # 观测量颜色
                col.ind = iris$Species, palette = "jco",
                # 添加椭圆
                addEllipses = TRUE, label = "var",
                # 线条颜色
                col.var = "black",
                legend.title = "Species")

3. PCA biplot，添加椭圆，点大小

fviz_pca_biplot(iris.pca,
                # Fill individuals by groups
                geom.ind = "point",
                # 点的形状
                pointshape = 21,
                # 点的大小
                pointsize = 2.5,
                # 按照组类特定形状
                fill.ind = iris$Species,
                col.ind = "black",
                # Color variable by groups
                # 颜色
                col.var = factor(c("sepal", "sepal", "petal", "petal")),
                # 标题
                legend.title = list(fill = "Species", color = "Clusters"),
                repel = TRUE        # Avoid label overplotting
             )+
  ggpubr::fill_palette("jco")+      # Indiviual fill color
  ggpubr::color_palette("npg")      # Variable colors