## Header ---------------------------
## Script name: Instron Sample Averaging
## Purpose of script: Create an averaged dataset for the displacement/force for each textile


## Author: Eric Beaudette
##
## Date Created: November 1st, 2022
## Last Modified: October 12th, 2024
##
## Email: beaud128@umn.edu

## Set Up -------------
setwd("/Users/ericbeaudette/Desktop/DRUM Records/Compression Modelling - Tensile Testing Data/Raw Data")
library(ggplot2)
library(scales)
library(data.table)

## Sample Upload ----
Sample1 <- read.csv(file= "T1 1L C 1.csv", header=TRUE)
Sample1 <- Sample1[-1,]
Sample1 <- Sample1[,c(-4,-5)]
Sample1$Displacement <- as.numeric(Sample1$Displacement)
Sample1$Force <- as.numeric(Sample1$Force)

Sample2 <- read.csv(file= "T1 1L C 2.csv", header=TRUE) 
Sample2 <- Sample2[-1,]
Sample2 <- Sample2[,c(-4,-5)]
Sample2$Displacement <- as.numeric(Sample2$Displacement)
Sample2$Force <- as.numeric(Sample2$Force)

Sample3 <- read.csv(file= "T1 1L C 3.csv", header=TRUE) 
Sample3 <- Sample3[-1,]
Sample3 <- Sample3[,c(-4,-5)]
Sample3$Displacement <- as.numeric(Sample3$Displacement)
Sample3$Force <- as.numeric(Sample3$Force)

Sample4 <- read.csv(file= "T1 1L C 4.csv", header=TRUE) 
Sample4 <- Sample4[-1,]
Sample4 <- Sample4[,c(-4,-5)]
Sample4$Displacement <- as.numeric(Sample4$Displacement)
Sample4$Force <- as.numeric(Sample4$Force)

Sample5 <- read.csv(file= "T1 1L C 5.csv", header=TRUE) 
Sample5 <- Sample5[-1,]
Sample5 <- Sample5[,c(-4,-5)]
Sample5$Displacement <- as.numeric(Sample5$Displacement)
Sample5$Force <- as.numeric(Sample5$Force)



## Quick Visualization ----

ggplot()+
  geom_point(data = Sample1, aes(x=Displacement, y= Force), col="grey")+
  geom_point(data = Sample2, aes(x=Displacement, y= Force), col="grey")+
  geom_point(data = Sample3, aes(x=Displacement, y= Force), col="grey")+
  geom_point(data = Sample4, aes(x=Displacement, y= Force), col="grey")+
  geom_point(data = Sample5, aes(x=Displacement, y= Force), col="grey")+
  xlim(0,500)+
  ylim(0,100)


## Start and Stop for Grouping ----

# Data is trimmed based on the highest minimum strain and the lowest maximum strain to ensure there is data from all five samples

start<- ceiling(as.numeric( max(c(Sample1[1,2],Sample2[1,2],Sample3[1,2],Sample4[1,2],Sample5[1,2]))))
stop <- floor(as.numeric(min(c(Sample1[nrow(Sample1),2],Sample2[nrow(Sample2),2],Sample3[nrow(Sample3),2],Sample4[nrow(Sample4),2],Sample5[nrow(Sample5),2]))))

avgfile <- data.frame(matrix(nrow=1, ncol=3) )

row <- 1
range<- stop-start
## Merging Loop ----

for (i in (0:(range*4))){
  lowlim <- start + (i/4)
  highlim<- start + (i/4) + .25
  
  x<- rbindlist(list(subset(Sample1, ((Displacement>=lowlim) & (Displacement < highlim))),
                     subset(Sample2, ((Displacement>=lowlim) & (Displacement < highlim))),
                     subset(Sample3, ((Displacement>=lowlim) & (Displacement < highlim))),
                     subset(Sample4, ((Displacement>=lowlim) & (Displacement < highlim))),
                     subset(Sample5, ((Displacement>=lowlim) & (Displacement < highlim)))
                     )
                )
  
  avgfile[row,1] <- (lowlim+highlim) / 2
  avgfile[row,2] <- mean(x$Force)
  avgfile[row,3] <- sd(x$Force)
  
  
  row <- row +1
}

colnames(avgfile)<- c("Displacement","Force","SDForce")
  
## Visualization - Post Average
ggplot()+
  geom_point(data = Sample1, aes(x=Displacement, y= Force), col="grey")+
  geom_point(data = Sample2, aes(x=Displacement, y= Force), col="grey")+
  geom_point(data = Sample3, aes(x=Displacement, y= Force), col="grey")+
  geom_point(data = Sample4, aes(x=Displacement, y= Force), col="grey")+
  geom_point(data = Sample5, aes(x=Displacement, y= Force), col="grey")+
  geom_point(data = avgfile, aes(x=Displacement, y= Force), col="red")+
  xlim(0,500)+
  ylim(0,100)


write.csv(avgfile, 
          file = "T1 1L C AVG.csv", 
          row.names = FALSE)