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 = get_eigenvalue(pca) #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$cos2, =FALSE) #to see the most contributing variables for both dimension fviz_cos2(pca, choice = “var“, axes = 1:2) # to draw a bar plot of variable contributions # Contributions of variables to PC1 a=fviz_contrib(pca, choice = “var“, axes = 1)# top= 5 to limit to five var. # Contributions of variables to PC2 b=fviz_contrib(pca, choice = “var“, axes = 2) (a,b, ncol=2, top=’Contribution of the variables to the first two PCs’) # Total contribution on PC1 and PC2 fviz_contrib(pca, choice = “ind“, axes = 1:2) #Graph of variables fviz_pca_var(pca, = “cos2“, = c(“red“, “blue“, “green“), repel = TRUE) #Biplot of individuals and variables fviz_pca_biplot(pca, repel = TRUE, = “blue“, = “red“) ######################################################################### #Plotting PCA ggbiplot(pca) #This will name each point with the name of the genotypes ggbiplot(pca,labels=rownames(GGE)) # plot using PC1 and PC2 ggbiplot(pca,ellipse=TRUE,choices=c(1,2),labels=rownames(GGE), groups=GGE$ENV) # scale the samples ggbiplot(pca,ellipse=TRUE, = 2, = 4.5, labels=rownames(GGE), groups=GGE$ENV) #remove the arrows altogether ggbiplot(pca,ellipse=TRUE, = 1, = 1,=FALSE, labels=rownames(GGE), groups=GGE$ENV) # final biplot by Customize ggbiplot aa=ggbiplot(pca,ellipse=TRUE, = 1, = 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( = “bottom“) aa ggsave(filename = ““, plot = aa,width = 22, height = 15, dpi = 2500, units = “cm“)