load("Breast_cancer.RData")

head(Breast_cancer_data[,c(1:6)])

data <- Breast_cancer_data

dim(data)

require(FactoMineR)

library(ggplot2)

library(dplyr)

library(latex2exp)

pca <- PCA(data[,-c(1,2)],ncp=2,graph=FALSE)

dat <- data.frame(data,pc1=pca$ind$coord[,1],pc2=pca$ind$coord[,2],diagnosis=as.factor(data[,2]))

p <- ggplot(data = dat, aes(x = pc1, y = pc2, color = diagnosis))+

geom_hline(yintercept = 0, lty = 2) +

geom_vline(xintercept = 0, lty = 2) +

geom_point(alpha = 0.8) + theme_bw()

dat <- dat %>% filter(pc1<7 & pc2<10)

p1 <- ggplot(data = dat, aes(x = pc1, y = pc2))+

#geom_hline(yintercept = 0, lty = 2) +

#geom_vline(xintercept = 0, lty = 2) +

xlab(TeX(r'($pc_1$)')) + ylab(TeX(r'($pc_2$)'))+

geom_point(alpha = 0.8,size=2.5) + theme_bw()

p1 + theme(axis.text = element_text(size = 20))+ theme(axis.title = element_text(size = 20))

ggsave("breast-pca1-a-new.pdf",device="pdf", width = 8, height = 8)

p2 <- ggplot(data = dat, aes(x = pc1, y = pc2, color = diagnosis))+

geom_hline(yintercept = 0, lty = 2) +

geom_vline(xintercept = 0, lty = 2) +

xlab(TeX(r'($pc_1$)')) + ylab(TeX(r'($pc_2$)'))+

geom_point(alpha = 0.8,size=2.5) + theme_bw()+

theme(legend.position=c(0.15,0.92),legend.title=element_blank())

p3 <- p2 + scale_color_discrete( labels = c("benign", "malignant"))

p3 + theme(legend.text=element_text(size=20),axis.text = element_text(size = 20))+ theme(axis.title = element_text(size = 20))

ggsave("breast-pca1-b.pdf",device="pdf", width = 8, height = 8)

library(ggpubr)

ggarrange(p1,p2)