Principal Component analysis (PCA) in R

library("ggplot2")
library("gridExtra")
library(ggbiplot)
library("corrplot")
library(factoextra)
#conversion of row number
data=rownames(GGE)=c("Me","E6","E2","E4","KB","HUl","kat","Dan","EA2","Ts",
"E0","E5","DI","E8","E39","ET1","BO","Ab","Si")
pca =prcomp(GGE[,c(2:11)], center = TRUE,scale. = TRUE)
print(pca)
to get eigen value
eig.val = get_eigenvalue(pca)
eig.val
#to get scree plot
fviz_eig(pca, addlabels = TRUE, ylim = c(0, 50))
PCA results for variables
var=get_pca_var(pca)
to see the most contributing variables for each dimension
corrplot(var$cos2, is.corr=FALSE)
#to see the most contributing variables for both dimension
fviz_cos2(pca, choice = "var", axes = 1:2)
to draw a bar plot of variable contributions
Contributions of variables to PC1
a=fviz_contrib(pca, choice = "var", axes = 1)# top= 5 to limit to five var.
Contributions of variables to PC2
b=fviz_contrib(pca, choice = "var", axes = 2)
grid.arrange(a,b, ncol=2, top='Contribution of the variables to the first two PCs')
Total contribution on PC1 and PC2
fviz_contrib(pca, choice = "ind", axes = 1:2)
#Graph of variables
fviz_pca_var(pca,
col.var = "cos2",
gradient.cols = c("red", "blue", "green"),
repel = TRUE)
#Biplot of individuals and variables
fviz_pca_biplot(pca, repel = TRUE,
col.var = "blue",
col.ind = "red")
#########################################################################
#Plotting PCA
ggbiplot(pca)
#This will name each point with the name of the genotypes
ggbiplot(pca,labels=rownames(GGE))
plot using PC1 and PC2
ggbiplot(pca,ellipse=TRUE,choices=c(1,2),labels=rownames(GGE), groups=GGE$ENV)
scale the samples
ggbiplot(pca,ellipse=TRUE,obs.scale = 2, var.scale = 4.5,
labels=rownames(GGE), groups=GGE$ENV)
#remove the arrows altogether
ggbiplot(pca,ellipse=TRUE,obs.scale = 1, var.scale = 1,var.axes=FALSE,
labels=rownames(GGE), groups=GGE$ENV)
final biplot by Customize ggbiplot
aa=ggbiplot(pca,ellipse=TRUE,obs.scale = 1, var.scale = 1, labels=rownames(GGE), groups=GGE$ENV) +
scale_colour_manual(name="Location", values= c("blue", "red", "green","pink"))+
ggtitle("PCA of wakjira")+
theme_minimal()+
theme(legend.position = "bottom")
aa
ggsave(filename = "Waq.png", plot = aa,width = 22, height = 15, dpi = 2500, units = "cm")