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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")