## ItMLiHSmar2022
## PCA.R, child script
## Principal components analysis for data visualisation
## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk


## ====================================================================
## Step 0: data wrangling
## ====================================================================
# source("data_format.R")

X1<-X %>% dplyr::mutate(across(where(is.factor), as.numeric))

pc.out<-prcomp(X1, center=TRUE, scale = TRUE)

pc.sum<-summary(pc.out)

## ====================================================================
## Step 1: plotting
## ====================================================================
Xy$group<-factor(Xy$group,labels = c("No decline", "Decline"))

library(ggfortify)
ppc12 <- autoplot(pc.out,
                  data = Xy,
                  x=1,
                  y=2,
                  colour = 'group')+
  labs(title = "PC1 and PC2",
       colour = "Outcome")
ppc13 <- autoplot(pc.out,
                  data = Xy,
                  x=1,
                  y=3,
                  colour = 'group')+
  labs(title = "PC1 and PC3",
       colour = "Outcome")
ppc23 <- autoplot(pc.out,
                  data = Xy,
                  x=2,
                  y=3,
                  colour = 'group')+
  labs(title = "PC2 and PC3",
       colour = "Outcome")

# Scree plot
pscr<-tibble(x=1:dim(pc.sum$importance)[2],
             Proportion=pc.sum$importance[2,],
             Cumulative=pc.sum$importance[3,])%>%
  pivot_longer(cols=-x)%>%
  ggplot(aes(x=x,y=value,color=name))+
  geom_line()+
  geom_point()+
  ylim(0,1)+
  labs(title = "Scree plot",
       color= "Variance")+
  ylab("Variance")+
  xlab("Principal components")

## ====================================================================
## Step 2: merge plots
## ====================================================================
library(patchwork)
pca22<-ppc12+
  theme(legend.position="none")+
  ppc13+
  ppc23+theme(legend.position="none")+
  pscr+ 
  plot_layout(ncol=2)+
  plot_annotation(title = 'Principal component visualisation',
                  tag_levels = "A")

# pca22