####Supplementary Material
library(plyr)
library(ggplot2)
CV<- function(x){sd(x)*100/mean(x)}
#####Supplementary table S2


ddply(td6_2cores, "VSE",summarise,N=length(Vol),mean=mean(Vol),median=median(Vol),sd=sd(Vol), cv= CV(Vol))

#####Supplementary table S3

ddply(td6_2vrmcores, "VSE",summarise,N=length(Est_vol),mean=mean(Est_vol),median=median(Est_vol),sd=sd(Est_vol), cv= CV(Est_vol))

#####Supplementary table S4


ddply(td6_2vrmcores, "VSE",summarise,N=length(Perc_extracted_volume),mean=mean(Perc_extracted_volume),median=median(Perc_extracted_volume),sd=sd(Perc_extracted_volume), cv= CV(Perc_extracted_volume))


#####Supplementary table S5


ddply(td6_2cores, "raw_material",summarise,N=length(Vol),mean=mean(Vol),median=median(Vol),sd=sd(Vol), cv= CV(Vol))

#####Supplementary table S6


ddply(td6_2vrmcores, "raw_material",summarise,N=length(Est_vol),mean=mean(Est_vol),median=median(Est_vol),sd=sd(Est_vol), cv= CV(Est_vol))



#####Supplementary table S7

ddply(td6_2vrmcores, "raw_material",summarise,N=length(Perc_extracted_volume),mean=mean(Perc_extracted_volume),median=median(Perc_extracted_volume),sd=sd(Perc_extracted_volume), cv= CV(Perc_extracted_volume))


#####Supplementary table S7
sand<-filter(td6_2vrmcores, raw_material %in% c("Sandstone"))
qua<-filter(td6_2vrmcores, raw_material %in% c("Quartzite"))
cc<-filter(td6_2vrmcores, raw_material %in% c("Cretaceous chert"))
quarz<-filter(td6_2vrmcores, raw_material %in% c("Quartz"))
lime<-filter(td6_2vrmcores, raw_material %in% c("Limestone"))

wilcox.test(cc$Est_vol,lime$Est_vol,B=10000000)
wilcox.test(cc$Est_vol,quarz$Est_vol,B=10000)
wilcox.test(cc$Est_vol,qua$Est_vol,B=10000)
wilcox.test(cc$Est_vol,sand$Est_vol,B=10000)


wilcox.test(lime$Est_vol,quarz$Est_vol,B=10000)
wilcox.test(lime$Est_vol,qua$Est_vol,B=10000)
wilcox.test(lime$Est_vol,sand$Est_vol,B=10000)

wilcox.test(quarz$Est_vol,qua$Est_vol,B=10000)
wilcox.test(quarz$Est_vol,sand$Est_vol,B=10000)

wilcox.test(qua$Est_vol,sand$Est_vol,B=10000)

#####Supplementary table S8
ks.test(cc$Est_vol,lime$Est_vol,B=10000)
ks.test(cc$Est_vol,quarz$Est_vol,B=10000)
ks.test(cc$Est_vol,qua$Est_vol,B=10000)
ks.test(cc$Est_vol,sand$Est_vol,B=10000)


ks.test(lime$Est_vol,quarz$Est_vol,B=10000)
ks.test(lime$Est_vol,qua$Est_vol,B=10000)
ks.test(lime$Est_vol,sand$Est_vol,B=10000)

ks.test(quarz$Est_vol,qua$Est_vol,B=10000)
ks.test(quarz$Est_vol,sand$Est_vol,B=10000)

ks.test(qua$Est_vol,sand$Est_vol,B=10000)

#####Supplementary table S9

wilcox.test(cc$Perc_extracted_volume,lime$Perc_extracted_volume,B=10000)
wilcox.test(cc$Perc_extracted_volume,quarz$Perc_extracted_volume,B=10000)
wilcox.test(cc$Perc_extracted_volume,qua$Perc_extracted_volume,B=10000)
wilcox.test(cc$Perc_extracted_volume,sand$Perc_extracted_volume,B=10000)


wilcox.test(lime$Perc_extracted_volume,quarz$Perc_extracted_volume,B=10000)
wilcox.test(lime$Perc_extracted_volume,qua$Perc_extracted_volume,B=10000)
wilcox.test(lime$Perc_extracted_volume,sand$Perc_extracted_volume,B=10000)

wilcox.test(quarz$Perc_extracted_volume,qua$Perc_extracted_volume,B=10000)
wilcox.test(quarz$Perc_extracted_volume,sand$Perc_extracted_volume,B=10000)

wilcox.test(qua$Perc_extracted_volume,sand$Perc_extracted_volume,B=10000)

#####Suplementary Table S10
ks.test(cc$Perc_extracted_volume,lime$Perc_extracted_volume,B=10000)
ks.test(cc$Perc_extracted_volume,quarz$Perc_extracted_volume,B=10000)
ks.test(cc$Perc_extracted_volume,qua$Perc_extracted_volume,B=10000)
ks.test(cc$Perc_extracted_volume,sand$Perc_extracted_volume,B=10000)


ks.test(lime$Perc_extracted_volume,quarz$Perc_extracted_volume,B=10000)
ks.test(lime$Perc_extracted_volume,qua$Perc_extracted_volume,B=10000)
ks.test(lime$Perc_extracted_volume,sand$Perc_extracted_volume,B=10000)

ks.test(quarz$Perc_extracted_volume,qua$Perc_extracted_volume,B=10000)
ks.test(quarz$Perc_extracted_volume,sand$Perc_extracted_volume,B=10000)

ks.test(qua$Perc_extracted_volume,sand$Perc_extracted_volume,B=10000)


#####Suplementary Table S11
td6_spi <- td6_2cores[!is.na(td6_2cores$SPI),]
t21spi<-filter(td6_spi, Layer %in% c("TD06.2.0-1"))
t23spi<-filter(td6_spi, Layer %in% c("TD06.2.2/3"))

t24spi<-filter(td6_spi, Layer %in% c("TD06.2.4"))
ks.test(t21spi$SPI,t23spi$SPI, B=10000)
ks.test(t21spi$SPI,t24spi$SPI,B=10000)
ks.test(t23spi$SPI,t24spi$SPI, B=10000)

#####Suplementary Table S12
t21<-filter(td6_2vrmcores, Layer %in% c("TD06.2.0-1"))
t23<-filter(td6_2vrmcores, Layer %in% c("TD06.2.2/3"))
t24<-filter(td6_2vrmcores, Layer %in% c("TD06.2.4"))
ks.test(t21$Perc_extracted_volume,t23$Perc_extracted_volume, B=10000)
ks.test(t21$Perc_extracted_volume,t24$Perc_extracted_volume,B=10000)
ks.test(t23$Perc_extracted_volume,t24$Perc_extracted_volume, B=10000)
#####Suplementary Table S13

ks.test(t21$Est_vol,t23$Est_vol, B=10000)
ks.test(t21$Est_vol,t24$Est_vol,B=10000)
ks.test(t23$Est_vol,t24$Est_vol, B=10000)


#######Supplementary Figure S1

SupFigure1<- ggplot(td6_2cores, aes(x= VSE, y= SPI, fill=VSE)) + 
  geom_boxplot()+ geom_jitter(shape=16, position=position_jitter(0.2))

SupFigure1 + scale_fill_grey() + theme_classic()+ ylim(0,1)+xlab("VSE")+ylab("SPI") + 
  theme(legend.position = "none", text = element_text(size=16))+scale_x_discrete(limits=c("A-B_Initial", "A", "C", 
                                                                                          "D", "F", "I", 
                                                                                          "J", "Tool"))

#####Shapiro Wilk

td6_2cores %>% 
  group_by(raw_material) %>%
  filter(n() >1) %>%
  do(tidy(shapiro.test(.$Vol)))

td6_2vrmcores %>% 
  group_by(raw_material) %>%
  filter(n() >1) %>%
  do(tidy(shapiro.test(.$Est_vol)))

td6_2vrmcores %>% 
  group_by(raw_material) %>%
  filter(n() >1) %>%
  do(tidy(shapiro.test(.$Perc_extracted_volume)))

td6_2cores %>% 
  group_by(VSE) %>%
  filter(n() >1) %>%
  do(tidy(shapiro.test(.$Vol)))

td6_2vrmcores %>% 
  group_by(VSE) %>%
  filter(n() >1) %>%
  do(tidy(shapiro.test(.$Est_vol)))


td6_2vrmcores %>% 
  group_by(VSE) %>%
  filter(n() >1) %>%
  do(tidy(shapiro.test(.$Perc_extracted_volume)))