D. R-script for correlation analysis

rm(list = ls()) ## Clear
## Install and load required R packages
install.packages("Hmisc")
library(Hmisc)
install.packages("corrplot")
library(corrplot)
install.packages("PerformanceAnalytics")
library("PerformanceAnalytics")

## Upload data
sut=read.csv("data/suture_side_corr.csv",row.names = 2)

## Check data
head(sut)

## Subsetting dataset with desirable traits to be analyzed
sut_select=sut[,c(4,5,6,10,12,13,15,16,19,20,22,24,25)]

## Run correlation analysis
res_sut<-cor(sut_select)

## Generating correlation plots
tiff(filename="graphs/suture_corr_with_1-cir.tif", units="in", width=5, height=5, res=1200) ## Define folder to save plot, its format and resolution
corrplot(res_sut, type = "upper", order = "original", tl.col = "black", tl.srt = 90,tl.cex = 1.2,method = "square") ##Generate the plot
graphics.off() ##Turn off the graphics. This will save plot in the output folder

##Save Pearson's correlation coefficients
corr_coeff_sut=as.data.frame(res_sut)
write.csv(corr_coeff_sut,"results/correlation_coefficient_suture.csv",row.names = TRUE)


# Get the p-values

p_matrix=cor.mtest(sut_select)
pvalues=as.data.frame(p_matrix$p,row.names = rownames(corr_coeff_sut))
colnames(pvalues)=c("PC1","PC2","PC3","MW","MH","FSIEI","DFB","PFB","cir","rect","DisAMa","ProAMa","ovo")
write.csv(pvalues,"results/p_values_suture.csv",row.names = TRUE)